>> WELCOME BACK EVERYONE.
HERE SUPER EXCITED TO HAVE YOU
BACK HERE ON DAY 2 OF THE
INSILIC O DRUG DISCOVERY
WORKSHOP.
THIS DAY WILL BE REALLY EXCITING
AND WE HAVE A SUPER ECSIGHTING
PROGRAM FOR YOU TODAY.
WE WILL CONTINUE SESSION 3 ON
APPLICATIONS OF INSILIC O
METHODS ON DRUG DISCOVERY.
WE WILL HAVE A SESSION 4 ON
EMERGING TRENDS IN INSILIC O
EMERGING TRENDS, AND IN THE
2:00, WE WILL HAVE THE NEW NOBEL
LAUREATE SPEAKER SPEAK ON DESIGN
FUNCTIONS USING DEEP LEARNING
AND THEN LATER IN THE AFTERNOON,
WE WILL HAVE A SUPER EXCITING
PANEL DISCUSSION ON BRIDGING THE
GAPS IN TRANSLATION AND AS I
MENTIONED YESTERDAY, IF WE
DIDN'T COVER ANY OF THE
QUESTIONS YOU HAD, AND YOU WANT
TO BRING THEM BACK THIS IS A
PERFECT PLACE DURING THE Q&A
SESSION AT 420 TO BRING THOSE
QUESTIONS BACK AND WE WILL
TACKLE THEM .
ALL RIGHT, LET'S CONTINUE TO
SESSION 3 HERE.
Y I WILL BRING BACK MY
COLLEAGUE, DR. SHYAM RELE, WHO
IS A PROGRAM OFFICER AT BARDA TO
THE STAGE AND SHYAM, THE FLOOR
IS YOURS.
>> GOOD MORNING EVERYONE.
WELCOME TO THE SECOND DAY OF
THIS EXCITING WORKSHOP,
CONTINUING FROM SEAN AND HIS
EXCELLENT TALKS YESTERDAY, WE
WILL CONTINUE WITH APPLICATIONS
OF INSILIC O METHODS IN DRUG
DISCOVERY.
OUR FIRST SPEAKER TODAY IS
MARISSA SAUNDERS, SHE IS AT
REKURGZ PHARMACEUTICALS, SHE HAS
ALREADY WORKED ON USING AI AND
LEARNING TO ANSWER IMPACTFUL
QUESTIONS AND SHE'S LEADING
EFFORTS AT THE INTERFACE BETWEEN
DEEP LEARNING AND MODELS TO
ACCELERATE DISCOVERY AND
DECISION MAKING TO ENABLE DRUG
DISCOVERY AT SCALE USING
AUTOMATED OMICS EXPERIMENTS, AI
IS MACHINE LEARNING PREDICTIONS
AND ANALYSIS AND STANDARDIZED
WORK FLOWS AND DECISION ENGINES.
[INDISCERNIBLE] IN THE LEARNING
PLACE USING LANGUAGE MODELS TO
DEVELOP TAXONOMY OF SKILLS AND
AS A BIG DATA SCIENTIST
CONSULTANT WORKING IN AREAS FROM
BIOTECH TO VIDEO GAMES.
SHE HAS A Ph.D. IN CHEMISTRY
FROM THE UNIVERSITY OF CHICAGO
AND COMPLETED POST DOC AT THE
UNIVERSITY OF UTAH IN STRUCTURAL
BIOLOGICAL [INDISCERNIBLE]
ELECTRON MICROSCOPY IMAGES INTO
PROTEIN STRUCTURES.
MARISSA, BACK TO YOU, I THINK.
>> THANK YOU SO MUCH.
>> YOU HAVE 25 MINUTES FOR THE
TALK AND I WILL GIVE YOU A
NOTICE AT AROUND 22 MINUTES IF
THATIA OKAY.
>> PERFECT.
THAT SOUNDS GREAT.
IS EVERYONE SEEING MY SLIDE
OKAY.
>> YES.
>> WONDERFUL.
>> WELL I'M SO HAPPY TO BE HERE,
THANK YOU SO MUCH FOR THE
INVITATION TO TALK, I AM
MARISSA, SANDERS AND I AM
EXCITED TO SHARE TODAY'S
TREALING EARLY DRUG DORPHY AT
SCALE, TO ORIENT YOU IF YOU
AREN'T FAMILIAR WITH REKURGZ, WE
ARE A TECH BIOCOMPANY WITH OUR
HEADQUARTERS IN SALT LAKE CITY,
UTAH.
WE ALSO HAVE OFFICES IN
[INDISCERNIBLE], TORONTO,
MONTREAL AND LONDON.
YOU MAY BE ASKING WHAT IS THIS
BIOTECH THING, I THOUGHT THERE
WAS A TERM OF BIOTECH, SO WHAT
WE THINK AT RECURSION, THAT
THERE ARE IMPORTANT TOOLS IN
DRUG DISCOVERY.
THIS IS WHERE WE STARTED TOO, WE
STARTED APPROACHING DISCOVERY IN
SCREENING USING MORPHOLOGICAL
PROFILES AND SPECIFICALLY USING
CELL PAINTING BUT AS WE
CONTINUED ON THIS JOURNEY, WE
REALIZED DISCOVERING AND
DEVELOPING MEDICINE REQUIRES
HUNDREDS OF STEPS AND IF YOU
REALLY WANT TO ACCELERATE
GETTING MEDICINES INTO THE HANDS
OF PATIENTS, YOU HAVE TO
INDUSTRIALIZE, DRUG DISCOVERY
AND THAT MEANS YOU HAVE TO TAKE
THESE POINT SOLUTIONS, AND
INTEGRATE THEM AS MODULES ACROSS
MANY DIVERSE STEPS, AND SO, THIS
IS THE PIPELINE THAT WE USE TO
THINK ABOUT HOW WE MOVE A DRUG
FROM HYPOTHESIS, ALL THE WAY
THROUGH THE CLINIC, AND YOU CAN
SEE THAT THERE ARE MANY
DIFFERENT MODULES, ALONG THE
WAY.
SO THIS IDEA OF USING POINT
SOLUTIONS IN MANY PLACES ACROSS
STEPS IS THE FIRST OF THE KEY
IDEAS THAT I HOPE YOU WILL TAKE
AWAY TODAY.
THE OTHER 2 WHICH I WILL
INTRODUCE HERE AND WILL TALK
ABOUT THROUGH THE COURSE OF THIS
TALK ARE THAT THE MOST EFFICIENT
EXPERIMENT, 1 CAN RUN IS NO
EXPERIMENT AT ALL.
AND SO THAT IS THE REAL PROMISE
OF INSILIC O METHODS.
THEY HELP US TO PREDICT WHICH
EXPERIMENTS ARE THE RIGHT
EXPERIMENTS TO RUN AND THAT
MEANS WE CAN BE MORE EFFICIENT
IN BOTH TIME AND MONEY IN
GETTING THROUGH DRUG DISCOVERY.
TO BE ABLE TO GET BETTER AND
BETTER AT KNOWING WHICH
EXPERIMENTS WE SHOULD RUN, WE
NEED BETTER MODELS AND FOR THAT
WE NEED BETTER DATA SO A KEY
IDEA AT RUECURSION, SO THAT IN
ORDER TO HAVE THESE CYCLES OF
LEARNING AND IMPROVING MODELS WE
HAVE TO COUPLE THE PHYSICAL
EXPERIMENTS WE USE TO PROGRESS
PROGRAMS THROUGH WITH INSILIC O
METHODS SO THAT WE CAN
ITERATIVELY IMPROVE OUR MODELS.
WE START BY THINKING ABOUT WHAT
ARE THE DATA MODULES, WHERE ARE
THE PLACES THAT WE NEED TO FOCUS
AND YOU CAN SEE IN THIS
PIPELINE, WE HAVE A RANGE OF
DIFFERENT DATA POINTS WHERE
WE'RE COLLECTING DATA, RANGING
FROM PATIENT DATA THAT HELP US
BUILD CAUSAL ML MODELS TO TARGET
PREDICTIONS TO OUR SCALED OMICS
CAPABILITIES AND ALL OF THESE
DIFFERENT DATA MODALITIES THEN
HELP US TO NAVIGATE THROUGH DRUG
DISCOVERY.
WE USE ML MODELS AND AI MODELS
THROUGHOUT THIS PROCESS TO HELP
INFORM WHICH EXPERIMENTS WE
SHOULD RUN.
SO IN THE PATIENT CONNECTIVITY
AND NOVELTY STAGE AS AN EXAMPLE,
WE USE LLMs TO FILTER THROUGH
THE CORPUS OF DATA IN THE WORLD
AND HELP US KNOW WHERE WE SHOULD
BE INTERESTED IN LOOKING FOR NEW
HYPOTHESIS.
WHEN WE HAVE PATIENT DATA, WE
USE CAUSE AT GENETIC MODELS TO
HELP INFORM WHAT OUR UNKNOWN
TARGETS OR CAUSAL GENES FOR DRUG
DISCOVERY.
WHEN WE MOVE TO HIDDEN TARGET
VALIDATION, WE USE GENERATIVE
CHEMISTRY MODELS WHICH YOU HEARD
ABOUT EARLIER YESTERDAY IN ORDER
TO GENERATE THE STRUCTURES THAT
WE NEED TO BE FEEDING INTO THIS
SYSTEM AND WE USE FOUNDATION
MODELS FOR IMAGING WHICH I WILL
TALK ABOUT IN JUST A MINUTE IN
ORDER TO INTERPRET OUR SCALED
GENOMICS DATA.
WHEN WE GO INTO COMPOUND
OPTIMIZATION, WE ARE USING
PROTEIN LIGAND BINDING MODELS TO
FIND OUT WHAT IS THE ACTUAL
PROTEIN TARGET THAT COMPOWBDS
OUR BINDING TO AND WE USE
CHEMICAL PROPERTY PREDICTION IN
ORDER TO KNOW WHICH THINGS ARE
ACTUALLY GOING TO MOVE US TO A
GOOD SPACE IN TERMS OF ADMIT
PROPERTIES.
AND THEN FINALLY IN TRANSLATION,
WE USE THINGS LIKE VIDEO
ANALYSIS AND ACTIVE LEARNING
MODELS IN ORDER TO TELL US WHICH
COMPOUNDS ARE STARTING TO SHOW
EFFECTS OF TOXICITY IN OUR MICE,
AND TO BETTER TRUNCATE THOSE
EXPERIMENTS IF WE NEED TO.
AND SO, IN ORDER TO BUILD ALL OF
THESE MODELS THAT HELP US TO
FILTER, WE REALLY DO NEED LARGE
AMOUNTS OF DATA AND TO GET
THERE, WE LEAN INTO THIS FLY
WHEEL IDEA, WHERE WE HAVE REAL
PHYSICAL EXPERIMENTS, HAPPENING
IN HIGH DIMENSIONAL ASSAYS, AT
SCALE AND THIS DATA THEN FEEDS
INTO DIGITAL REPRESENTATIONS OF
REALITY, SO WE GENERATE THIS
CORPUS OF DATA AND THEN THAT
DATA IN TURN AND FED INTO OUR
ALGORITHMS AND METHODS INSILIC O
IN ORDERED TO HELP US CREATE
THESE IN SILIC O MODELS WHICH IN
TURN INFORM WHICH EXPERIMENTS WE
SHOULD RUN NEXT.
TO DO THIS, WE ARE REALLY USING
THIS DRUG DISCOVERY PROCESS TO
GENERATE A DATA SET THAT IS FIT
FOR PURPOSE FOR NEW ML MODELS.
-- IN THESE HIGH DIMENSIONAL
INTERACTIONS AND SO AS A RESULT
THE KINDS OF DATA WE COLLECT AT
SCALE ARE OMICS DATA, GENOMICS,
TRANSCRIPT OMICS, PROTEOMICS AND
IN ADDITION TO STANDARD ADME
ASSAYS AND THEN SOME MOUSE
MODELS AND REAL WORLD PATIENT
DATA.
AND OVER THE YEARS, WE'VE
MANAGED TO COLLECT A SIGNIFICANT
CORPUS OF DATA ACROSS ALL OF
THESE LAYERS, EITHER THROUGH
PARTNERSHIPS OR THROUGH
GENERATING THAT DATA IN-HOUSE ON
OUR PLATFORM AND YOU CAN SEE OUR
PH ENOMICS AND WHERE YOU CAN
WHERE THERE'S REAL WORLD DATA,
GENOMICKINGS DATA WHERE WE
PARTNER WITH PEOPLE TO HAVE
ACCESS TO THIS DAT AND THEN
THERE ARE ALSO LAYERS WHERE WE
ARE JUST NOW STARTING TO ENTER
THIS FLY WHEEL, PROTEOMICS WE'RE
JUST STARTING THIS, WE STARTED
EARLIER THIS YEAR AND OUR ADME
PLATFORM WE STARTED RUNNING AT
SCALE LATE LAST YEAR EMPLOY WHAT
ALL THIS DATA GIVES US IS THE
ABILITY TO BUILD A RANGE OF
DIFFERENT MODELS AND YOU CAN SEE
HERE, WE BUILD EVERYTHING FROM
CAUSAL GENETIC MODELS THROUGH TO
CHEMICAL PROPERTY PREDICTION
MODELS, AND EVEN VIDEO TIME
LAPSE BEHAVIORIAL MODELS FOR OUR
IN VIVO OMICS.
THIS IS OUR FOUNDATIONAL LAYER
AND IT IS THE PLACE WE OFTEN
START IN THINKING ABOUT
PROGRAMS.
THE IDEA BEHIND PH ENOMICS IS A
MORE WHOLESTIC WAY TO CAPTURE
THE RESPONSE.
SO IF YOU THINK ABOUT IT BY
DISEASE PRETURB ANT THAT
PROTURBATION IS GOING TO CHANGE
THE GENE EXPRESSION AND SO
CHANGE THE TRANSCRIPTION
ACTIVITIES IN THE CELL.
IT WILL ALSO SHIFT PROTEIN
LEVELSAs A RESULT OF THAT
CHANGE IN TRANSCRIPTION, THAT IN
TURN WILL SHIFT PROTEIN ACTIVITY
AND ALL OF THOSE THINGS ARE
THINGS WE CAN MEASURE THROUGH
DIFFERENT MEASURE OMICS AND
PROTEOMICS AND METRICS TAB O
LOAMICS, BUT THE FANS, ALL OF
THESE, THE MORPHOLOGY OF THIS
END POINT OF THIS CELL IS
INFORMED BY ALL OF THESE CHANGES
RESULTING IN A CELLULAR END
STATE AND WE INFACT SEE IF WE
APPLY DIFFERENT PROTURBATIONS TO
CELLS THAT WE CAN VISUALLY
DISTINGUISH BETWEEN SOME VERY
MORPHOLOGIES AND SO THIS IS THE
FOUNDATION FOR ATION FOR WHICH
WE'RE BUILDING.
WHAT WAS EXCITING FOR THIS IS WE
COULD TEST FOR PROTURBATION, WE
COULD PHYSICALLY STRESS THESE
CELLS OUT AND ALL OF THOSE
THINGS COULD BE CAPTURED ON OUR
PLATFORM.
BUT IN THIS MODALITY WE WERE
STILL IN THIS PLACE WHERE FOR
EVERY DIFFERENT DISEASE WE
WANTED TO TEST, WE HAD TO SCREEN
ALL OF OUR COMPOUNDS AND SO WHAT
THAT MEANT IS IF WE WANT TO
THINK BROADLY, IF WE WANT TO
SCREEN ACROSS A WIDE BREDTH OF
DISEASES WE HAVE THIS QUADRATIC
SCALING WHERE WE HAVE TO SCREEN
THE ENTIRE LIBRARY FOR EACH 1 OF
THOSE AND THAT WAS NOT SOMETHING
THAT WOULD SCALE THE WAY WE HAD
AMBITIONS TO SCALE.
AND SO THE WAY THAT WE SOLVED
THIS PROBLEM WAS TO THINK ABOUT
USING AN INFERENTIAL MAP.
SO THE IDEA WAS INSTEAD OF THE
PARADIGM WHERE YOU HAVE THE
DISEASE, AND ON TOP OF THAT
DISEASE, YOU PUT A COMPOUND, THE
QUESTION WAS, CAN WE TAKE A
DISEASE AND THEN SEPARATELY TAKE
A COMPOUND AND TREAT CELLS WITH
THOSE AND IN SOME WAY FROM THE
IMAGES CAPTURE BIOLOGICAL
RELATIONSHIPS IN SUCH A WAY THAT
THOSE PRESENTATIONS LET US INFER
SOMETHING ABOUT WHAT WILL HAPPEN
IF THOSE THINGS ARE PUT
TOGETHER.
AND SO WHAT WE'VE COME UP WITH
IS THESE PHENOMAPS, YOU WILL SEE
THIS PICTURE IN A FEW SLIDES
HERE, THE RED SQUARES ARE WHERE
THOSE 2 THINGS ARE VERY SIMILAR
TO 1 ANOTHER, SO THE IMAGES LOOK
SIMILAR BIOLOGICALLY AND THE
BLUE SQUARES IS WHERE THEY LOOK
OPPOSITE PRACTICES 1 ANOTHER IN
THIS BIOLOGICAL REPRESENTATIONAL
SPACE AND YOU MIGHT SAY, THE KEY
THING HERE, IS THEN TO FIGURE
OUT HOW TO TRANSFORM AN IMAGE
INTO A REPRESENTATION THAT
CARRIES THIS BIOLOGICAL MEANING
AND HOW DO WE DO THIS.
THE WAY WE'RE DOING THIS, IN
FACT, IS THROUGH DEEP LEARNING
MODELS AND MOST RECENTLY IT'S
THROUGH FOUNDATION MODELS AND
THESE MODELS ENABLE US TO TAKE
ADVANTAGE OF THIS FLY WHEEL OF
DATA GENERATION AS I'LL EXPLAIN
NOW.
SO WE HAVE GENERATED TO DATE
OVER 250 MILLION DIFFERENT
EXPERIMENTS RANGING ACROSS 50
HUMAN CELL TYPES, INCLUDING A
TRILLION DIFFERENT HUMAN NEURONS
THAT HAVE BEEN GENERATED AND
WHAT WE CAN DO WITH ALL OF THIS
DATA IS WE CAN FEED IT INTO A
FOUNDATION MODEL WHICH IS A
MODEL WHERE WE'RE NOT TELLING
THE MACHINE WHAT THE ANSWER IS
THAT WE WANT IN TERMS OF A CLASS
BUT WE'RE RATHER ASKING IT DO A
TASK AND THE TASK WE'RE ASKING
IT TO DO IS TO RECONSTRUCT A
MASK IMAGE SO WE CAN TAKE THIS
IMAGE WHICH IS OUR INPUT IMAGE
ON THE RIGHT, AND WE CAN MASK
OUT A LARGE CHUNK OF IT, SAY 75%
OF THE IMAGE GETS MASKED OUT AND
THEN WE ASK OUR ML ALGORITHM TO
RECONSTRUCT THAT IMAGE FROM THIS
MASKED INPUT AND AS YOU CAN SEE
IT DOES A VERY, VERY GOOD JOB OF
DOING THAT, AND WHAT'S MORE
IMPORTANT IS THIS TASK CAPTURES
INFORMATION ABOUT THE BIOLOGY
THAT'S GOING ON ABOUT THE
MORPHOLOGY OF THESE CELLS, THAT
THEN WE CAN USE THOSE EMBEDDINGS
TO REPRESENT OUR PROTURBATIONS
IN THIS INFERENTIAL MAP.
ONE OF THE THINGS THAT'S REALLY
EXCITING ABOUT THIS MODEL IS
THAT WE ARE SEEING THAT IT
SCALES CONTINUES TO SCALE WITH
THE MEMBER OF PARAMETERS SO WE
CAN BUILD LARGER AND LARGER
NETWORK MODELS AS WE GET MORE
DATA AND AS WE HAVE ACCESS TO
MORE COMPUTE AND THAT MEANS
THESE MODELS WILL GET BETTER AND
BETTER AND THAT'S REALLY
DESIRABLE.
WE'VE ALSO SEEN THAT THESE
MODELS ARE GENERALIZABLE TO NEW
PROBLEMS, AND SO IF WE COMPARE
THIS TO OTHER WAYS OF EMBEDDING
IMAGE DATA WE SEE THAT IT DOES
SIGNIFICANTLY BETTER AT RECALL
AND ACTUALLY IS ABLE TO CAPTURE
THINGS BEYOND JUST THE CONTEXT
WHERE IT WAS TRAINED, WHICH IS
THE GREAT PROMISE OF FOUNDATION
MODELS.
PHENOM DATA WHICH IS AVAILABLE
THROUGH VIDEOS BY
[INDISCERNIBLE] FOR ANYONE TO
USE IS 1 OF THOSE MODELS AND WE
CONTINUE TO ITERATE ON THIS
INTERNALLY AS WE GENERATE MORE
AND MORE DATA IN THIS FLY WHEEL
OF IMPROVEMENT AND WHEN WE DO
THIS, WHEN WE PUT TOGETHER THIS
MAP, WE CAN LOOK THEA THE MAP
AND WE CAN SAY, FOR ANY
COMBINATION THAT WE WANT TO
THINK OF, WHAT IS THE SIGNAL
THAT WE'RE SEEING IN THE MAP.
ARE THESE SIMILAR, DO THEY HAVE
NO RELATIONSHIPS OR ARE THEY
OPPOSITE TO EACH OTHER AND WE
CAN DO THIS NOT ONLY FOR
GENE-GENE RELATIONSHIPPED WHERE
WE KNOW THESE SIGNALING PATHWAYS
AND WE SEE THAT THE MAP
RECAPITULATES THESE SIGNALING
PATHWAYS AND WE CAN DO THIS IN
COMPARING GENES TO COMPOUNDS AND
WE CAN SEE THAT THE MAPS ARE
ALSO RECAPITULATING THE BIOLOGY
WE KNOW FROM LITERATURE AND FROM
PRIOR EXPERIMENTS WE SHOULD SEE.
AND THEN WE CAN AM CO BIEN THOSE
INTO 1 SO THAT WE SEE BOTH THE
COMPOUNDS AND THE RELATED GENES
AND GET THE FULL PICTURE OF THE
KNOWN AND UNKNOWN BIOLOGY AROUND
A PARTICULAR AREA.
ONCE WE'VE GOT THE NOMICs
DATA, WE ALSO WANT A METHOD TO
VALENTINED DIADIC TWHA OR HAVE
AN ORTHOGONAL ASSAY TO SHOW US
WHAT WE'RE SEEING IS NOT JUST AN
ARTIFACT OF HOW WE'RE DOING FOR
GENOMICS BUT IT IS REAL BIOLOGY
AND THE WAY WE'RE DOING THAT IS
THROUGH TRANSCRIPTOMICS, THIS IS
OUR SECOND DATA LAYER, WE HAVE
GENERATED AT SCALE, THE FIRST
IME NATIONAL LIBRARY --
GENOME SCALE THROUGH MAP TO
PERTURB SEQ, IT GIVES US A WAY
TO NOT ONLY TIE THIS BACK TO
PATIENTS BUT IT HAS GIVEN US
THIS MULTIMODAL WORK FLOW IN
TRANSCRIPTOMICS AND PREDICT
WHICH COMPOUNDS WILL FAIL DOWN
STREAM TESTS AND WHICH COMPOUNDS
WILL PASS DOWN STREAM TESTS AND
THAT LETS US RUN THE RIGHT
EXPERIMENTS AT THE RIGHT TIMES.
OTHER MODELS WE'RE USING RIGHT
NOW, WE'RE USING PATIENT DATA TO
CREATE CAUSAL MODELS, SO IN THIS
MODEL SETTING, THE DATA THAT
WE'RE USING AS PARTNERSHIP DATA
FROM [INDISCERNIBLE] WHICH HAVE
PATIENT RECORDS THAT ARE
DEIDENTIFIED AND STANDARDIZED
AND BY COMBINING THIS DATA,
WHICH REALLY DOES FORWARD
GENETICS WHERE WE KNOW THE
PHENOTYPE OF THE PATIENT AND WE
CAN MAP FORWARD WHAT ARE THE
GENETIC DRIVERS THAT ARE
ASSOCIATED WITH THAT PHENOTYPE,
WE CAN COUPLE THAT WITH REKURGZS
PLATFORM WHICH LETS US KNOCK OUT
PARTICULAR GENES AND OBSERVE A
PHENOTYPE IN CELLS AND SO WE GET
THIS VIRTUOUS CYCLE OF LEARNING
WHERE WE'RE USING BOTH FORWARD
IMENETTICS AND REVERSE GENETICS
TO GET TO NOVEL TARGETS.
ONCE WE HAVE THOSE TARGETS, AND
WE HAVE COMPOUNDS THAT LOOK LIKE
THEY'RE AFFECTING THEM THE
INFORMIC SPACE WE CAN LOOK AT
OUR CHEMISTRY MODELS LIKE OUR
MATCH MAKER MODEL THAT PREDICTS
WHICH PROTEINS A PARTICULAR
COMPOUND WILL BYPASSED WITH AND
FROM THIS MODEL WE ACTUALLY HAVE
OVER 2 QUADRILLION DIFFERENT
PROTEIN LIGAND INTERACTIONS
WHERE WE SCORED HOW LIKELY IT IS
THAT THOSE 2 ARE INTERACTING AND
THAT LETS US GO FROM THE
PHENOMMICS TARGET TO BEING ABLE
TO HYPOTHESIZE WHAT BINDING
TARGETS MIGHT BE.
AND SO DOES THIS WORK?
DOES THIS ACTUALLY MAKE A
DIFFERENCE IN HOW FAST WE'RE
ABLE TO MOVE OR HOW MANY
DIFFERENT HYPOTHESIS WE'RE ABLE
TO TEST, SO WE HAVE DATA ON THE
FIRST 2 STAGES HERE OF A VOLUME
WE CAN START TO AGGREGATE AND
WHAT WE SLEEP APNEA AND OBESITY
IS BEFORE WE DID THIS
TREALIZATION PROCESS IN 2022, IT
TOOK A TEAM OF 40 PEOPLE AND WE
WERE ABLE TO EXPLORE 30 PROGRAM
HYPOTHESIS, THE FOLLOWING YEAR,
WHEN WE HAD STARTED THIS
INDUSTRIALIZED PROCESS, THAT
TEAM WAS COUNSEL TO 7 PEOPLE,
AND THE NUMBER OF PROGRAM
HYPOTHESIS HAD EXPLODED TO ABOUT
115 OVER THE COURSE OF THE YEAR.
AND THEN IF WE LOOK AT THE FIRST
HALF OF 2024, THIS YEAR, WE NEED
LESS THAN 1 FULL-TIME PERSON TO
GET PROGRAMS FROM INITIATION ALL
THE WAY TO WHERE WE'RE
NOMINATING THEM FOR
[INDISCERNIBLE] AND WE'RE ABLE
TO DO THAT FOR 201 PROGRAM
HYPOTHESIS GOING BO THE SYSTEM.
SO WHAT THAT'S DO SUGGEST
ENABLING US TO SHIFT THE
TIMELINES UPON AND THE SHAPE OF
THE DRUG DORPHY FUNNEL FROM A
V-SHAPED FUNNEL THAT TAKES 1-2
YEARS FOR THIS EARLY HIT
DISCOVERY AND MILLIONS OF
DOLLARS TO SOMETHING THAT TAKES
WEEKS OR MONTHS AND CAN BE
MEASURED IN THE THOUSANDS OF
DOLLARS.
SO IN THE TIME I HAVE LEFT, I
WOULD LIKE TO TALK THROUGH A
CASE STUDY TO SHOW YOU HOW THIS
MIGHT BE APPLIED TO A PARTICULAR
DISCOVERY PROGRAM.
I'M GOING TO TALK ABOUT RBM39
THIS, IS A PROGRAM THAT WAS 1 OF
OUR EARLIER PROGRAMS IN IT SO IT
DOESN'T HAVE ALL THE MODELS THAT
I'VE TALKED THROUGH BUT IT IS 1
THAT WE'VE BROUGHT ALL THE WAY
THROUGH TO CLEARING AYE IERK ND
AND SO I THINK IT'S A GREAT
STORY TO TELL.
SO CDK12 WAS INITIALLY
IDENTIFIED AS A TARGET FOR HR
PROFICIENT CANCERS BUT THE
CHALLENGE IS BECAUSE THIS IS A
KINASE, WE HAD NONSPECIFICKITY
ISSUES FOR AN INDICATOR FOR
CDK12 AND CDK13 ARE VERY SIMILAR
AND SO MOST CDK INHIBITORS ALSO
ENDED UP INHIBITING CDK13 AND
THAT CAUSED A LOT OF TOXICITY.
WHEN WE LOOKED AT OUR PHENOMAP,
WE SAW A GENE RBM39, WHERE THE
KNOCK OUT OF RBM39 MIMICS THE
KNOCK OUT OF CDK12 BUT IT
SHOULDN'T SHOW THAT SAME
RELATIONSHIP WITH CDK13 SO THIS
SEEMED LIKE A GREAT TARGET FOR
GETTING THE EFFECTS OF CDK12
WITHOUT HAVING THE INHIBITION OF
CDK13.
WHEN WE SCREENED SMALL
MOLECULES, WHAT WE SAW, WAS THAT
THERE ARE SMALL MOLECULES THAT
TARGET RBM 39, AND SHOW
SIMILARITY TO CDK12 WITHOUT
HAVING ANY SIMILARITY TO CDK13
AND SO THIS FELT REALLY, REALLY
EXCITING.
WITH OUR MATCH MAKER PRODUCT,
WHICH PREDICTS THE BINDING OF
COMPOUNDS TO PROTEIN POCKETS, WE
WERE ABLE TO IDENTIFY -- WE WERE
ABLE TO PREDICT THAT THESE NEW
COMPOUNDS WERE NOT IN FACT CDK
INHIBITORS AND SO HERE, TOO, IS
A SIGNIFICANT SIMILARITY SCORE.
WE CAN SEE THAT POSITIVE
CONTROLS, CDK12 AND 13
INHIBITORS ARE SHOWING, ARE
SCORING AS BEING PREDICTED AS
BEING THE KINASES HERE BUT THESE
2 NEW MOLECULES, THESE RECURSION
MOLECULES ARE NOT PREDICTED THAT
WAY EMPLOY WE CAN PHYSICALLY
KREEN TO CONFIRM THAT DIGITAL
HYPOTHESIS AND RUN AN EXPERIMENT
THAT MATTERS AND WHAT WE CAN SEE
HERE IS WHEN WE SCREEN FOR CDK12
ACTIVITY, WE CAN SEE THAT CDK 12
INHIBITORS ARE ACTUALLY BINDING
TO CDK12, OUR HIT COMPOUNDS ARE
NOT BINDING TO CDK12 DESPITE
HAVING THE SAME PHENOMMIC CENTER
FOR EXCELLENCE ON AGING AND THAT
LED US TO FEEL LIKE A REALLY EBS
SIGHTING DREK TO TAKE THIS
PROGRAM.
IN ORDER TO DEVELOP THAT
COMPOUND AND GET IT TO A
COMPOUND THAT WE CAN DOSE INTO
AN ANIMAL WE HAVE TO ITERATE ON
THE COMPOUND DESEEN AND THAT
GIVES US A CHANCE TO TEST IT
AGAINST THAT RESPONSE AND NOT
JUST ABOUT RBM12 AND CDK39, BUT
MAKING SURE WE'RE ARE NOT
PICKING UP ACTIVITY WE DON'T
WANT.
SO AS WE INCREASE CONCENTRATION,
WE CAN EVALUATE SIMILARITY TO
THESE DIFFERENT TARGETS AND GET
A NICE CONCENTRATION RESPONSE
CURVE THAT ALLOWS US TO ITERATE
ON IMPROVING MOLECULES AND THAT
LETS US DRIVE STRUCTURE ACTIVITY
RELATIONSHIPS.
WE CAN SEE HERE THAT FROM THIS
COMPOUND WE'RE ABLE TO MAKE
VARIATIONS, CHANGE SOME OF THE
R-GROUPS AND THEN PROFILE WITHIN
THE PHENOMMIC MAP TO MAKE SURE
WE'RE MAINTAINING THE RESPONSE
WE WANT TO SEE AND WHEN WE TEST
FOR RBM39 DEGRADATION WE SEE AN
ECHOING OF THAT EFFECT.
SO WE KNOW THAT THE PHENOMMIC
INFER ENSEL MAP IS TELLING US
WHAT THE PHYSICAL EXPERIMENTS
WOULD TELL US AS WELL.
AND THEN FINALLY EXCITING 3 THIS
COMPOUND GOT TO THE PLACE WHERE
WE WERE ABLE TO DOSE IT IN A
TUMOR MODEL, AND SO, WHAT WE SEE
HERE IS WITH OUR HIT COMPOUND,
WE SEE A SIGNIFICANT REDUCTION
IN THE TUMOR VOLUME OVER TIME
AND WE ALSO SEE AN INCREASE IN
OUR SURVIVAL RATES OVER TIME
COMPARED TO THE VEHICLE.
AND SO WHAT THIS STORY SHOULD
ILLUSTRATE IS THAT THESE
PREDICTIONS AND MINIMAL STANDARD
EXPERIMENTS WILL LET US RAPIDLY
AND EFFICIENTLY IDENTIFY A
DEVELOPMENT CANDIDATE.
THESE ARE THE NUMBERS FOR THE
RBM 39 PROGRAM WHERE YOU CAN SEE
INSILICO, WE STARTED VERY, VERY
WIDE AND WE WERE ABLE TO SCREEN
A VERY LARGE NUMBER OF MOLECULES
INSILICO, WE WERE ABLE TO IN
PHENOMMICS EXPERIMENTS GO
THROUGH A SMALLER SET OF THOSE
COMPOUNDS AND DRIVE RELATIVELY
QUICKLY TO CANDIDATE QUALITY
COMPOUNDS.
AND WE WERE ABLE TO DO THIS IN A
RELATIVELY SHORT TIME, IT TOOK
US 9 MONTHS FROM STARTING THIS
PROGRAM TO HAVING SOMETHING THAT
FELT LIKE IT HAD AN APPROPRIATE
THERAPEUTIC INDEX TO MOVE
FORWARD WITH.
AND IN FACT, THIS COMPOUND JUST
CLEARED IND EARLIER THIS MONTH
AND SO WE ANTICIPATE STARTING
PHASE 1 PHASE 2 TRIALS FOR THAT
AT THE BEGINNING OF THE NEXT
YEAR.
AND SO IN SUMMARY, WE'RE REALLY
EXCITED ABOUT THIS IDEA OF DATA
LOOPS, THIS IDEA OF RUNNING
EXPERIMENTS WHERE WE NEED TO IN
ORDER TO IMPROVE OUR MODELS
WHICH WILL TELL US BETTER WHICH
SPRMS WE NEED TO RUN.
ANDLET END GAME IS THAT PATIENTS
ARE WAITING FOR THIS AND WE NEED
TO FIND NEW WAYS TO DELIVER
THIS, AND SO FAR WHAT WE'VE
TALKED ABOUT AT THIS WORKSHOP
ARE THE WAY TO MOVE THIS
FORWARD.
THAT'S ALL I HAVE.
>> THANK YOU.
THANK YOU SO MUCH, MARISSA FOR
AN EXCELLENT PRESENTATION.
I WILL SEE IF THERE ARE ANY
QUESTIONS.
THERE ARE SOME QUESTIONS IN THE
CHAT, WHICH I WILL LEAVE FOR YOU
MARISSA, IF YOU CAN ANSWER THOSE
THAT WOULD BE GREAT.
I AM EXCITED TO LEARN MORE ABOUT
THE MATCH MAKER MODEL, I AM
CURIOUS WHAT INPUTS WERE USED
FOR THE MODEL, WAS IT FRAMED ON
DIFFERENT PROTEIN LIVING
CONTEXTS AND THE WORD OF
PROTEINS AND LIGANDS, DELIVERED
SEPARATELY.
SO IT WAS ACTUALLY TRAINED ON A
LARGE SUITE OF DATA INCLUDING
KNOWN PROTEIN LIGAND
INTERACTIONS INCLUDING
BIOCHEMICAL ASSAY INFORMATION
FOR BINDING AND SO, THOSE --
THAT WAS THE INPUT AND THEN IT
HAS BEEN SHOWN TO BE ABLE TO
MAKE THESE PREDICTIONS FOR
THINGS THAT IT HAD NOT SEEN
BEFORE, SO IT DOES REST ON THAT
KIND OF DATA.
>> WE WILL TAKE 1 MORE QUESTION
IT'S GREAT TAKEN--THEY THE MODEL
HAS A LOT IN TIME AND COST,
EARLY LEAD DISCOVERY, WHAT'S
YOUR STRATEGY TO DO THE SAME IN
THE CLINICAL OUTCOME?
>> IT'S A REALLY, REALLY GOOD
QUESTION.
IN THE CLINICAL SPACE, WE KNOW
THAT EARLY CLINICAL SCREENS ARE
RELATIVELY SMALL IN TERMS OF
PATIENT NUMBERS, WE RUN THE
FIRST FEW CYCLES THROUGH IN A
SMALL HANDFUL OF PATIENTS AND SO
IT'S REALLY IMPORTANT TO BE ABLE
TO GET THE PATIENT PROFILE RIGHT
BECAUSE WE -- WE KNOW THAT IN
THIS AGE OF PERSONALIZED
MEDICINE, PARTICULARLY IN
ONCOLOGY, EACH PATIENT IS
SLIDELY DIFFERENT AND WHAT WE
WANT TO DO IS IDENTIFY THE
PATIENT COHORT THAT BEST FITS
OUR TREATMENT, AND SO 1 OF THE
WAYS THAT MODELS CAN HELP IS IN
FINDING THEERATE PATIENT
POPULATION TO BRING INTO OUR
CLINICAL TRIALS.
THERE'S ALSO MORE LOGISTICAL
THINGS WE CAN DO IN THE CLINICAL
TRIAL SPACE, IN TERMS OF KNOWING
HOW BEST TO RECRUIT PEOPLE INTO
THESE TRIALS IN ORDER TO
IDENTIFY WHAT IS THE POPULATION
WE NEED TO OPEN THIS UP TO IN
ORDER TO GET ENOUGH PEOPLE
ENROLLED AND SO I THINK THERE
ARE A WIDE RANGE OF WAYS THAT WE
CAN USE SOME OF THESE SAME TYPES
OF STRATEGIES IN ORDER TO
ACCELERATE CLINICAL PHASES.
>> ONE MORE QUESTION: CAN YOU
PLEASE SHED MORE LIGHT ON --
OOPS, SORRY ON VALIDATION OF
MODEL AND IN CASE OF ISOFORMS
OUR SELECTIVITY AND SPECIFICITY
WILL HANDLE?
>> YEAH, SO I'M ACTUALLY HAPPY
TO REFER YOU TO SOME OF THE
PUBLICATIONS AROUND MATCH MAKER.
I DON'T HAVE THOSE CITATIONS ON
HAND.
I WILL LOOK THEM UP OFFLINE HERE
AND HAPPY TO SEND THAT ON TO
YOU.
IN GENERAL MOST OF THE WORK THAT
WE'RE DOING HERE AT RECURSION,
MOST OF THE PAPERS WE PUBLISHED
ARE AVAILABLE ON THE WEBSITE, SO
RECURSION.COM.
THERE IS A LITERATURE AREA THERE
THAT YOU CAN LOOK AND SEE THE
BACKING SCIENCE FOR THESE
MODELS.
>> ONE LAST QUESTION, I WILL GO
WITH, HOW TO ELUC DATE DRUG
MECHANISM OF ACTION BASED ON
PHENOMMICS DATA?
>> YOU KNOW THAT'S AN AWESOME
QUESTION AND IT'S SOMETHING WE
THINK ABOUT A LOT.
I THINK THE TERM MECHANISM OF
ACTION IS ACTUALLY A LITTLE BIT
FUZZY AND IT MEANS DIFFERENT
THINGS TO DIFFERENT PEOPLE, SO 1
OF THE THINGS THAT WE THINK
ABOUT WHEN WE THINK ABOUT
MECHANISM OF ACTION IS WHAT ARE
THE GENERAL BIOLOGICAL PATHWAYS
THAT ARE BEING ACTIVATED AND TO
ANSWER THAT QUESTION.
WE USE THE PHENOMMICS DATA AND
THE TRANSKRPT ORDER OF
MICRONSICS DATA AND WE'RE --
TRANSCRIPTOMICS DATA AND WE'RE
ABLE TO FIND SIMILAR YEENS AND
KNOW HOW THEY'RE ACTING AND ALSO
ABLE TO LOOK AT THE
TRANSCRIPTOMIC REGULATION AND
SAY
WHAT ARE THE PATHWAYS THAT ARE
SHIFTING.
THERE'S A QUESTION AROUND MOA
HAD IS WHAT IS THE TARGET THE
DRUG IS BINDING TOO IN AND THAT
IS 1 THAT REALLY HELPS TO
ACCELERATE DRUG STOEVERY DOWN
STREAM BUT IT'S NOT NECESSARILY
SOMETHING THAT WE HAVE TO HAVE
EARLY ON AND IT'S ALSO NOT
SOMETHING THAT'S ABSOLUTELY
REQUIRED AND SO, THE EXACT
BINDING TARGET IS SOMETHING WE
WOULD TRY TO PREDICT THROUGH
MATCH MAKER AND WE ALSO TEST
THINGS THAT ARE SHOWING STRONG
SIMILARITY IN THE MAP TO SEE IF
WE DO SEE THAT BINDING AND IT IS
SOMETHING THAT IS AN ITERATIVE
EFFORT THAT WE PARTICULARLY PAY
ATTENTION TO IN LATER STAGES OF
DRUG DESIGN.
>> MARISSA, IF YOU CAN STAY
ONLINE AND ANSWER SOME OF THE
QUESTIONS IN THE CHAT, THAT
WOULD BE GREAT.
WE HAVE TO MOVE TO OUR NEXT
SPEAKER.
>> ABSOLUTELY.
>> THANK YOU AGAIN MARISSA.
THE LAST SPEAKER IN THIS SESSION
IS ALEX ZHAVORONKOV, ALEX IS THE
FOUNDER AND LEADER OF THE
INSILICO MEDICINE GENERATING
ARTIFICIAL IN. TELEIGENCE AND
PLATFORMS FOR DRUG DISCOVERY.
SINCE 2007 HE HAS DONE
REINFORCEMENT LEARNING AND THE
GENERATION OF MOLECULAR
STRUCTURES THE, THE DESIRED
PROPERTIES AND [INDISCERNIBLE]
AND PATIENT DATA.
UNDER HIS LEADERSHIP INSILICO
RAISED OVER 400 MILLION IN
MEDICAL GRAPTS FROM
BIOTECHNOLOGY, HEALTHCARE AND
FINANCIAL INVESTORS, R&D
RESEARCH CENTERS IN SEVERAL
REGIONS.
THE COMPANY HAS NOMINATED 18
PRECLINICAL CANDIDATES SO FAR,
STARTED CLINICAL TRIALS AND
ENTERED PHASE 2 WITH AN AI
DISCOVERED NOVEL TARGET AND AI
DESIGNED NOVEL MOLECULE.
ALEX, I CAN HAND IT OVER TO YOU
IF YOU ARE READY WITH YOUR
PRESENTATION.
>> READY WITH THE PRESENTATION,
THANK YOU SHYAM, AND GREAT TO BE
PRESENTING TO YOU TODAY.
THANK YOU FOR THIS GENEROUS
INTRODUCTION AND THABILITY TO
PRESENT WITH SUCH ESTEEMED
SPEAKERS.
LET ME SHARE MY SCREEN AND START
PRESENTING.
SO MY TALK TODAY WILL FOCUS ON
THE CAPABILITIES OF END-TO-END
GENERATIVE AI AND ROBOTICS FOR
DRUG DORPHY.
I HAVE CASE STUDIES SHOWING WHAT
AI DRUG DISCOVERY CAN DO TODAY
AND WHAT WE EXPECT IT TO BE ABLE
TO DO IN THE NEAR FUTURE.
THERE IS A LOT OF HYPE AROUND
THIS AREA, AND MANY COMPANIES
ARE FORMED EVERY DAY, OR EVERY
WEEK AND OF COURSE, LET'S HOPE
FOR PROGRESS IN ACADEMIA, MUCH
OF THAT IS REAL AND I WOULD LIKE
TO TALK A LITTLE BIT ABOUT THE
RECENT EXAMPLES.
SO, A LITTLE BIT ABOUT INSILICO,
JUST LIKE MARISSA PRESENTED FOR
RECURSION, WE ARE ALSO DIVERSE
AND FLOABALLY PRESENT.
OUR HEADQUARTERS, GLOBAL
HEADQUARTERS IS IN BOSTON, WE
JUST MOVED IT FROM NEW YORK.
SO 1000 MASS AVENUE, WELCOME TO
JOIN.
OUR AI IS DONE IN MONTREAL
CANADA, THAT'S WHERE WE DO
THEORY AI AND IT'S ALSO DONE IN
ABU DHABI, HIGHLY RECOMMEND
VISITING AND WE ALSO HAVE GREAT
PRESENCE IN CHINA, SMALL TEEM IN
HONG KONG, TAI PEI, SHANGHAI,
SUZHOW, AND WE UTILIZE THESE TO
RAPIDLY DESIGN AND DISCOVER
DRUGS, HOWEVER OUR MISSION AS A
COMPANY IS TO EXTENT HEALTHY
PRODUCTIVE LONGEVITY FOR
EVERYONE AND OUR GROUND GOAL IS
TO SOLVE AGING.
HAD IS HOW THE COMPANY IS
ORGANIZED SO PRETTY IMPORTANT
SLIDE.
WE HAVE OUR OWN LLMs THAT HELP
ORCHESTRATE THE MAIN SPECIFIC
MODELS.
OF COURSE WE ALSO UTILIZE LARGE
FOUNDATIONAL MODELS TO CREATE
SOME OF THE AGENTS THAT WE USE
EVERYWHERE.
WE HAVE OVER 800, THE MAIN
SPECIFIC MODELS, SOME OF THEM
ARE TRANSFORMER BASED, SOME ARE
[INDISCERNIBLE], SOME ARE
DIFFUSION, DIFFUSION
TRANSFORMERS, AND WHAT ARE
LLMs ARE REALLY GOOD AT IS
ORCHESTRATING SO IF YOU HAVE API
TO THOSE MODEL ANDS GREAT
DESCRIPTION, YOU CAN HAVE AGENTS
EVEN CREATING RESEARCH PLANS FOR
YOU USING THE MAIN SPECIFIC
MODELS.
ANDUREENTLY WE HAVE OVER 2000
AGENTS IN THE COMPANY THAT WE'VE
CREATED, SO WE PRETTY MUCH
REPLACED A LOT OF KIND OF LOW
LEVEL DATA LIMITATION AND EVEN
SOME OF THE CODING TASKS WITH
AGENTS, WE HAVE SOFTWARE IN THE
MARKET, CURRENTLY 10 OUT OF THE
24 COMPANIES ARE USING OUR
TOOLS, AND WE PROTECT OURSELVES
PRACTICES OUR DATA HOWEVER, WE
REALLY LIKE TO RECEIVE FEEDBACK
ON THE MODELS AND FEEDBACK IT
WAS INVALUABLE AND IT GOES BACK
TO OUR QC PROGRAMS AND WE DO
EXPERT LEARNING WITH EXPERT
FEEDBACK, SO WE REGULARLY
RETRAIN THE MODELS AND MADE THEM
AVAILABLE FOR THE COMMUNITY.
AND WE USE THOSE SAME MODELS TO
PUT THE DRUGS IN CLINICAL TRIALS
ITSELF, WE WILL WORK WITH SMALL
MOLL KIEWMS BIOLOGICS AND DO
TARGET DISCOVERY.
WE ALSO DO A LOT OF WORK TBH
AGING WORK SO PLEASE FOLLOW US
ON GOOGLE SCHOLAR, WE SIEWCIALLY
PUT OUT MAYBE 1 OR 2 INTERESTING
PAPER IS IN AGING RESEARCH EVERY
QUARTER AND WE START ALSO
UTILIZING GENERATIVE ABILITY
ANDY FOR AGRICULTURAL TASKS, SO
PESTICIDES, HERBICIDES AND ALSO
FOR US TO CAPTURE OUR MATERIAL
DESIGN TASKS.
SO LOOKING FOR MOLECULES AND
PROVIDING CO2 EFFICIENTLY, WE'VE
RECENTLY DRIVEN REVENUE FROM
THAT AS WELL, IT'S NONAPOPTOTIC
THE JUST HEALTHY
[INDISCERNIBLE], IT'S A REALLY
INTERESTING BUSINESS.
AND NOW I'M GOING TO SHOW YOU
THE MOST IMPORTANT SLIDE DURING
THIS HOPEFULLY THIS TALK BY VERY
OFTEN PEOPLE TRY TO PROJECT WHAT
DRUG DISCOVERY IS GOING TO BE
WITH AI, IN THE FUTURE, IN TERMS
OF SPEED, COST, AND PROBABILITY
OF SUCCESS, HOWEVER, THERE ARE
VERY FEW BENCHMARKS OUT THERE
THAT CAN BE USED TO SEE, OKAY,
ARE YOU OUT PERFORMING OR NOT.
SO HERE ARE SOME INTERNAL
BENCHMARKS FROM INSILICO, MANY
OF THOSE ARE DATE STAMPED WITH
PRESS RELEASES OR ACADEMIC
PAPERS, SO IN 2019, WE RAISED
OUR FIRST BIG ROUND TO DO OUR
OWN DRUG DISCOVERY AND ALSO PUT
THE SOFTWAREOT MARKET, SO THEN
WE NOMINATED 20 PRECLINICAL
CANDIDATES, 9 REACHED IND
CLEARANCE, 2 ARE IN PHASE 2, 1
JUST COMPLOATED PHASE 2 A, WITH
SAFETY AND DOSE DEPENDENT
EFFICACY AND ACROSS ALL THESE,
NOW OUR AVERAGE TIME TO
PRECLINICAL CANDIDATE IS 13
MONTHS.
SHORTEST TIME TO PRECLINICAL
CANDIDATE WAS 9 NONTHS.
THAT WAS ALSO PARTNERED SO IT
ALREADY PROGRESSED INTO PHASE 1
FROM 0 TO PRECLINICAL CANDIDATE
WITH A NOVEL TARGET, IT WAS A
NOVEL TARGET FOR CANCER, WE
MANAGED TO ACHIEVE A PRECLINICAL
CANDIDATE IN 9 MONTHS.
FOR US THE PC C PACKAGE INCLUDES
2AN MALEFFICACY MODELS AND 28
DAY NONGLP TALKS.
SO, IT'S PRETTY COMPREHENSIVE
AND AT A MINIMUM 2 MODELS.
IN OUR ACTINIC PAPER WE
DEMONSTRATED WE PUT OUT ATM
EXPERIMENTS IN 1 PAPER
PRECLINICAL AND THAT TOOK US 18
MONTHS.
THAT'S OUR LONGEST PROGRAM FOR
T-NICK, FOR 2022 NOMINATED 9 PC
Cs, IF WE GO AT CURRENT
CAPACITY AND HAD UNLIMITED
FUNDING WE COULD PROBABLY DO 12
PRECLINICAL CANDIDATES AND WE
CURRENTLY DO NOT HAVE ANY
[INDISCERNIBLE] PROMISING
TARGETS, I HAVE MORE THAN 400
TARGETS AFTER THIS TALK TO GO
AFTER IF ANYBODY UPONS AND USUAL
LEE WE TRY TO BALANCE TARGET AND
COMPOUND NOVELTY, CONFIDENCE AND
COMMERCIAL ATTRACTABILITY.
SO THOSE ARE THE 3 CRITERIA, 1
SELECTING THE PROGRAM.
BECAUSE AT THE END OF THE DAY,
YOU ALSO WANT TO INSURE THE YOB
CORPS IS MARKETABLE.
OUR YOB CORPS IS NONAPOPTOTIC
THE TO TAKE EVERY PROGRAM INTO
THE CLINIC OURSELVES, WE TRY TO
DEVELOP THE DRUG TO THE POINT
WHERE IT'S DERISKED AND THEN BIG
PHARMACEUTICAL COMPANIES TAKE IT
OVER AND DO LATER STAGE CLINICAL
TRIALS.
SO HOW DO WE LOOK AT NOVELTY, WE
USUALLY LOOK AT WHETHER THE
TARGET HAS ALREADY BEEN IN THE
CLINICAL TRIALS, IS THERE
CLINICAL POC, OTHER SELECTIVE
COMPOUNDS, HOW MANY
PUBLICATIONS, SO FOR EXAMPLE,
FOR TNICK WHEN WE STARTED
WORKING ON THE PROGRAM, I WILL
TALK ABOUT IT TODAY, THERE WERE
ONLY I THINK 70 PAPERS JUST
MENTIONING THE GENE IN DIFFERENT
CONTEXT.
NO SELECTIVE COMPOUNDS, NO
CLINICAL PSC, NO CLINICAL
TRIALS, SO THAT WAS PRETTY NOVEL
AND WE USUALLY CONSIDER THAT TO
BE MAYBE 75% NOVELTY, AND YOU DO
SCORING WITH EVERYTHING THAT HAS
BEEN DEVELOPED AGAINST THE
TARGET, DORPHED AGAINST THIS
TARGET AND THEN WE HAVE TO LOOK
AT, YOU KNOW IS THAT GOING TO BE
FIRST IN CLASS, BEST IN CLASS,
PATTERN DIFFERENTIATED, WHAT IS
THE PATENT LIFE ON, SO FOR OUR
T-NICK PROGRAM IT WAS A
SCAFFOLD, LESS THAN 60%
[INDISCERNIBLE] SIMILARITY WITH
EVERYTHING KNOWN.
THIS IS THIS SLIDE THAT SHOWS
OUR INSILICO PRICE SO WE HAVE
SOFTWARE FOR SCIENCE, YOU CAN
BUY MOST OF THOSE COMMERCIALLY,
WE HAVE MORE VERTICALS, 42
BIOLOGY, 42 MEDICINE, 42 SCIENCE
AND MEDICINE, SO IT ALLOWS TO
YOU DISCOVER TARGETS UTILIZING
MORE THAN 60 DIFFERENT TARGET
DISCOVERY PHILOSOPHIES SO 23
DIFFERENT MODELS.
IN 1 PLACE, SO DEPENDING ON YOUR
KIND OF THERAPEUTIC AREA,
WHETHER HE OR SHE LIKES,
[INDISCERNIBLE] WILL BE ABLE TO
SATISFY THE REQUIREMENTS MOST
LIKELY, IF NOT, THEY HAVE THEIR
OWN PHILOSOPHY, I CAN TELL US
THIS PHILOSOPHY WILL INTEGRATE
INTO THE METABOLOMICS IF IT HAS
RECENTLY SOLID THEORETICAL BASE.
WE HAVE A SYSTEM CALLED GENERAL
ANTIBIOTICS WHICH ALLOWS TO YOU
GENERATE ANTIBIOTICS ANTIBODIES
AND WE STARTED THIS IN PILOT
MODE, CUSTOMERS STARTED BUYING.
WE DON'T HAVE OUR OWN BIOLOGICS
PROGRAMS YET AND SOME OF THE
SOFTWARE IS OPEN SOURCE, SOME OF
THE VERY EXCITING PROGRAMS WE
HAVE A LOOK IN OUR LIFE MODELS
SO THINK OF LIKE OPEN AI'S
[INDISCERNIBLE] THAT CAN
GENERATE 1 MINUTE TO HIGH
QUALITY VIDEO.
WE DO SIMILAR THINGS WITH
BIOLOGICAL DATA AND
[INDISCERNIBLE] 3 GPT,
MULTIOMIMS TRANSFORMER
MULTIMODAL, MULTIOMICS,
TRANSFORM IS CURRENTLY OPEN
SOURCE AND YOU CAN PLAY WITH IT
ON DISCORD, SO WE'RE BUILDING A
COMMUNITY TO UNDERSTAND BIOLOGY
IN TIME.
[INDISCERNIBLE] ALLOWS TO YOU
GENERATE TOOLS WITH PROPERTIES,
IT HAS OVER 40 GENERATIVE MODELS
AND OVER 700 PREDICTIVE MODELS
FOR REINFORCEMENT LEARNING, VERY
STABLE SYSTEM, CHEMISTRY ALLOWS
YOU TO DO DFT, CHEAPLY,
ACCURATELY, IT COMES FREE WITH
CHEMISTRY 42, ON MEDICINE, 42,A
LOWS TO YOU PREDICT THE OUTCOMES
OF CLINICAL TRIALS, HAVE A LOOK
AT SOME OF OUR PUBLISHED PAPERS
WHERE WE DID RETROSPECTIVE,
PERSPECTIVE AND QUASI
PERSPECTIVE, AND IT ALLOWS YOU
TO WORK OUTSIDE PHARMA AND WE
HAVE A FUN TOOL CALLED DORA, IT
ALLOWS YOU TO SPAWN TEAMS OF AI
RESEARCHERS.
THEY GO AND PERFORM DIFFERENT
RESEARCH TASKS AND THEN BRING
THEIR RESEARCH DATA BACK AND IT
GETS COMPILED INTO A RESEARCH
PAPER, IT'S $17 A MONTH.
YOU CAN GET IT FROM OUR WEBSITE
AND WE USE THESE TOOLS
OURSELVES, WHATEVER WE PROVIDE
TO THE COMMUNITY TO VALIDATE
THEM USING OUR OWN PROGRAMS.
SO THE FIRST PROGRAM THAT WE
STARTED IN 2019 IS T-NICK, AND
WITH IMD SAFETY AND UNEXPECTED
DOSE EFFICACY WITH ACTUAL
IMPROVE AMS WE SEE, SO WE ARE
SITTING ON GOOD DATA, IT'S ALSO
ABOUT EFFICACY ON KIDNEY AND
FIBROSIS, AND ALSO OTHER
FIBROSIS, AND WE NOMINATE 2
CANDIDATES AND WE BALANCE THE
REST OF THE PIPELINE WITH
DIFFERENT ABILITIES OF OUR
PLATFORM.
BALANCING NOVELTY CONFIDENCE AND
COMMERCIAL TRACTABILITY, SOME OF
THOSE ARE PROGRAMS WE HAVE
ALREADY LICENSED, SO USP 1 WAS A
PRETTY SUCCESSFUL LICENSE SAYING
ON EXERCISE TO [INDISCERNIBLE],
$18 MILLION CASH UPFRONT TO
REACH I& D.
SO NOT A SUPER NOVEL TARGET BUT
A HIGH CONFIDENCE 1 AND WE HAD
BEST IN CLASS CHEMISTRY.
-- IT HAS PROGRESSED NICELY AND
MANY OF THESE ARE UNDER TERM
SHEETS AS WE'RE CONTINUING TO
SUPPLY FROM PHARMACEUTICAL
COMPANIES WHERE HIGH QUALITY
DRUGS.
USUALLY TO BE ABLE TO LICENSE
OUT AN AI DISCOVERED THERAPEUTIC
YOU NEED TO HAVE HIGH LEVEL OF
QUALITY OF THE DATA RULE.
SO THAT'S WHY WE WANT TO
MULTIPLE PHARMACEUTICAL
COMPANIES AND FROM BIOFARM O
SUITICAL COMPANIES TO CREATE
THOSE THAT EXCEED THE QUALITY
STANDARDS OF LARGE
PHARMACEUTICAL COMPANIES AND
INSURE THAT THE AVERAGE CHECK
MARK IS CHECKED.
USUALLY THE LEVEL OF QUALITY
THAT YOU NEED TO ACHIEVE FOR AN
EXTERNAL LICENSING DEAL IS
ACTUALLY HIGHER THAN BIG
PHARMACEUTICAL COMPANIES HAVE
INTERNALLY SO IT'S VERY, VERY
IMPORTANT TO COMMIT YOURSELF TO
ULTRA HIGH LEVEL OF QUALITY AND
ALSO MANY OF OUR TOOLS HAVE
RESEARCH PAPERS EXPLAINING OUR
APPLICATION KNOWLEDGE,
EXPLAINING HOW THEY WORK, SO
METABOLOMICS YOU CAN READ THE
APPLICATION AND NOTE HOW IT
WORKS AND THEN EXPLAIN CASE
STUDIES AND HOW MODELS WORK FOR
CHEMISTRY FOR THE TOOL, ALSO AND
1 OF MY FAVORITE JOURNALS, WE
PUT OUT THE APPLICATION OUT
EXPLAINING HOW IT WORKS.
NOW SEVERAL SMALLER CUTCHES
ACTUALLY ALSO PICKED UP THIS
MODEL AND STARTED DEVELOPING AND
PROVIDING TOOLS IN A VERY
SIMILAR MODEL SO DEEP
FLUORESCENT LEARNING.
I HIGHLY RECOMMEND LOOKING AT
THIS PAPER AND USING THIS
APPROACH BECAUSE IT'S PRETTY
VALIDATED.
DORA IS A SUPER COOL TOOL.
YOU CAN GET IT FOR $17 A MONTH
OR A 50% DISCOUNT.
PREVIOUS SPEAKER MARIS SA FROM
RECURSION, WAS SPEAKING FROM
BDECKER MI, AND WHY DON'T WE
WRITE A RESEARCH PAPER ABOUT
THAT WHILE I'M RESENTING SO
LET'S DO RBM 39, AND I THINK
THEY WENT FOR LYMPHOMA.
YOU CAN ADD ADDITIONAL PAPERS
HERE YOURSELF, SO THE OBJECTIVE
FOR SCIENTISTS IS USUALLY TO
WRITE A PAPER, SO WE ALLOW YOU
TO WRITE A PAPER HERE SO IT'S
ALWAYS IN THE FORM OF THE PAPER,
YOU CAN ADD YOUR OWN DATA HERE
BUT LET'S SAY WE GO AUTOPILOT SO
WE GIVE IT A SECOND AND IT WILL
PROVIDE YOU WITH A RESEARCH
PLAN, SO NOW, THE SYSTEM IS
PROVIDING RESEARCH PLAN BASED ON
MY REQUEST, SO IT WILL PERFORM
OMICS DATA ANALYSIS, GO THROUGH
THOUSANDS OF DATA SETS THAT HAVE
BEEN INTEGRATED INTO
METABOLOMICS, PERFORM NETWORK
ANALYSIS, HYPOTHESIS DEVELOPMENT
BUT LET'S SAY WE ALSO WANT TO
PROVIDE, ALSO PERFORM
COMPREHENSIVE LITERATURE REVIEW,
AND SUBMIT, GENERATE CONTENT,
LET IT RUN, SO NOW WE CAN SEE IT
SPAWNED AN ARMY OF AGENTS, THE
MASTER AGENTS OF THE PRINCIPAL
INVESTIGATOR, TRANSCRIPTOMICS,
KNOWLEDGE GRAPHS, SCIENCE
RESEARCHER, WEB REFERENCE FINDER
AND BY THE TIME I FINISH TALKING
IT WILL HAVE A PAPER WITH
ORIGINAL BIBLIOGRAPHY.
SO LET'S GO BACK TO THE
PRESENTATION WHILE WE'RE
WRITING.
SO I JUST WANTED TO COVER A
LITTLE BIT OF HISTORY OF
GENERATIVE AI AND DRUG
DISCOVERY.
SO IN 2016 THERE WERE 3
COMPETING GROUPS AS I REMEMBER
IT IN THIS FIELD, SO 1 WAS OURS,
BUT OF COURSE, VERY FAMOUS GROUP
IS [INDISCERNIBLE]'S LAB AND
[INDISCERNIBLE] LAB THE
ASTRAZENECA, AND PUBLISHED HIS
FIRST VERY, VERY FAMOUS PAPER
WITH CANNED WHAT CHEMICAL DESIGN
USING DATA DRIVEN CONTINUOUS
PRESENTATION OF MOLECULES, IT
WENT ON ARCHIVE IN 2016.
IN OCTOBER I HAD TO
[INDISCERNIBLE] BECAUSE MINE WAS
IN PEER REVIEW IN THE ACADEMIC
JOURNAL AT THE TIME AND SIMILAR
IDEA, AND NOW PUBLISHED THE
[INDISCERNIBLE] CENTRAL SCIENCE
FROM CHEN ALSO A VERY EARLY
WORKS IN 2017 MOLECULAR NOVEL
DESIGN THROUGH REINFORCE AM
LEARNING AND MANY OTHERS, WE
TOGETHER PUBLISHED MOSES, SOME
OF MY PAPERS, SO OUR FIRST 1 WAS
FROM 2016 BUT FOR SOME REASON IT
CHOSE 2017, WE USED
[INDISCERNIBLE] FOR SMALL
MOLECULE DRUG DESIGN BUT THEN
DOING EXPERIMENTS THERE AND THEN
REALLY BIG EXPERIMENTAL
VALIDATION STUDY FOR US FOR
GENERATIVE [INDISCERNIBLE],
ENFORCEMENT LEARNING, PLUS 2019,
HERE ARE THE TIMELINE OF SOME OF
THOSE EXPERIMENTS SO GREEN IS
INSILICO PAPERS, OUR FIRST
EXPERIMENTAL STUDY WAS
CONDITIONAL [INDISCERNIBLE],
WHERE WE DESIGNED OUR FIRST
SELECTIVE [INDISCERNIBLE] AND
PUBLISHED AND TESTED IT.
WE DID IT IN 2017, BUT MANAGED
TO PUBLISH IN 2018, AND IN 2019
IT WAS GENERAL.
THE GREATEST BOTTLENECK WE
QUICKLY REALIZED IS IT'S
EXPERIMENTAL VALIDATION, IT
TAKES YOU SOMETIMES 5 WEEKS TO
CREATE AND TRAIN NEW MODEL, NEW
GENERATIVE MODEL REGARDLESS OF
HOW COMPLEX IT IS, USUALLY IT
DOESN'T TAKE LONGER.
BUT IT CAN TAKE YOU SEVERAL
YEARS TO INSURE THE THAT ALL
VALIDATION EXPERIMENTS ARE
CONCERNED EMPLOY BY THE WAY, A
GRAY PAPER THAT THE PR ININATE
AND YOU ARE INTELIENCE THIS
APRIL HOW THE OF TOM
[INDISCERNIBLE] LAB IN
CAMBRIDGE, I HAVEN'T SEEN IT
PUBLISHED BUT WHEN I READ IT, I
WAS BLOWN AWAY HOW MUCH WORK
THEY PERFORMED, SO THEY
ACTUALLY LOOKED AT ONLY
VALIDATION EXPERIMENTS THAT WERE
-- THAT APPEARED IN LITERATURE,
OR EVERY MODEL PUBLISHED FOR
GENERATIVE CHEMIST RADIOY WITH
INPUT, OUTPUT, DESIGN TASK,
TARGET, KIT RATE, RESULT AND
YEAR PUBLISHED FOR DISTRIBUTION
AND ALSO FOR [INDISCERNIBLE]
LEARNING AND THESE ARE SOME OF
OUR EXPERIMENTS SO I WAS HAPPY
TO SEE THAT OUR WORK WAS VERY
WELL REPRESENTED SO WE DID
PUBLISH QUITE A BIT WITH
EXPERIMENTS.
AND HERE ARE SOME CASE STUDIES I
WOULD LIKE TO PRESENT, SO NEW
COMPOUNDS INFUSING AI, WORK
EXPERIMENTALLY, SO THAT WAS OUR
2019 PAPER.
WE DID THE EXPERIMENT 2018 FOR A
YEAR TO PUBLISH, ALMOST GOT
REJECTED, AND HOW PRETTY HIGHLY
SIGHTED SO HERE, WE WERE
CHALLENGED BY OUR PARTNER FOR
ANY TARGET OUT OF THE
[INDISCERNIBLE].
THEY GIVE US THE KINASE, 21 DAYS
TO GENERATE 6 COMPOUNDS AND THEN
WE SYNTHESIZE AND TESTED.
[INDISCERNIBLE] WAS THE LAST
AUTHOR, I WAS THE FIRST AUTHOR,
SO AT THAT TIME WE BECAME
FRIENDS DURING THIS EXPERIMENT.
PRETTY FAMOUS PAPER AND IN 46
DAYS WE MANAGED TO DOSE THE
MOUSE.
AND OUT OF THOSE 6 COMPOUNDS FOR
WORK, 2 WENT THROUGH
MICROSTUDIES OF MULTIPLE
ENDOCRINAL ASSAYS AND 1 WAS
TESTED IN MOUSE.
WE WERE ACTUALLY LIMITED BY THE
TIME AND ALSO THE ABILITY TO
SYNTHESIZE, SO OF COURSE SOME OF
THOSE MOLECULES SHARED
STRUCTURAL SIMILARITY WITH
NONCOMPOUNDS AND WE DO NOT DENY
THAT BUT THE FACT THAT GENERAL
ENFORCEMENT LEARNING MANAGED TO
DO THAT VERY QUICKLY WAS PRETERM
BIRTH IMPEDIMENTS PRESSIVE, THEN
IT WAS CRITICIZED BY SOME OF THE
COMPETING GROUPS AND WE
RESPONDED AND EXPLAINED THAT
IT'S SCALABLE.
BUT AT THAT TIME AGAIN, PEOPLE
WERE DEBATING THE POTENTIAL OF
GENERATIVE CHEMISTRY IN THE
INDUSTRY, AND WE HAD TO RESPOND.
BUT THEN ONCE WE MANAGED TO
RAISE SOME MONEY WE STARTED
DOING REAL PROGRAMS AND YOU'VE
SEEN SOME OF THE RESULTS, SINCE
2019, SO THE PAPER WAS PUBLISHED
AND SOME OF THE POCs, WE'VE
MANAGED TO PUBLISH WITHOUT GOING
INTO COMMERCIAL PROGRAMS, SO 1
REALLY COOL EXPERIMENT WAS THIS
1.
WE DID IT IN 2021 OF THE END OF
THE 2021.
SO WE USED -- SORRY, THE
BEGINNING OF 2022, AND WE USED
PANNA OMICS TO IDENTIFY CDK20 AS
A MARKER FOR CARCINOMA, USED
ALPHA FOLD PROTEIN STRUCTURE AND
CHEMISTRY 42 TO VERY RAPIDLY DO
2 ROUNDS OF SYNTHESIS, SO IN
FIRST ROUND WE GOT 7.3
MICROMOLAR HITS SO NOT GREAT.
GAVE IT BACK TO CHEMISTRY 42 FOR
LIAISON GABBED DRUG BASED DESIGN
AND GOT 180 NANO MOLAR HITS AND
NOT TOO BAD AND TESTED INVITRO
AND IT WORKED SO CHEMICAL
SCIENCE, SO CURRENTLY 1 ACADEMIC
GROUP IS TAKING THIS VERSION OF
THIS MOLECULE FORWARD WHICH WAS
VERY COOL BECAUSE THEY -- THEY
KNOW, THEY SEE IT REALLY, REALLY
WELL.
AND WE ALSO CAN USE AI TO
REPURPOSE EXISTING DRUGS AS SOME
COMPANIES IS ACTUALLY DEVELOP
BUSINESS MODELS AROUND THIS
PHILOSOPHY.
SO IN THIS PARTICULAR
EXPERIMENT, I PRESENTED IN MARCH
AT [INDISCERNIBLE] CONSORTIUM IN
2022 AND MET BRILLIANT
[INDISCERNIBLE] AND JEFFREY
[INDISCERNIBLE] WHO RUN
[INDISCERNIBLE], WE COMBINED IT
WITH THE DATA OMICS SET
TAKEN--THEY WE HAVE IN THE
DATABASE AND GOT SEVERAL NEW
TARGETS AND ALSO DRUGS TO
REPURPOSE, TESTED THEM IN THE
FLY MODEL OUT OF MAYO CLINIC
WITH [INDISCERNIBLE] AND OUT OF
THE 26 PREDICTED TARGETS, 18
FELT STRONG OR MODERATE RESCUE
AND IN FLY MODELS AND ONLY 2
SHOWED MODERATE ENHANCEMENT
MOTILITY AND LESS PHENOTYPE AND
[INDISCERNIBLE] AT THAT TIME,
DEAN OF THE MEDICAL SCHOOL AT
[INDISCERNIBLE] UNIVERSITY NOW
HE IS IN SHANGHAI, VERY FAMOUS
AMERICAN-CHINESE SCIENTIST IN
NEUROSCIENCE, HE ALSO WAS THE
CONFOUNDER OF [INDISCERNIBLE]
TECHNOLOGIES SO THEY PICKED 1 OF
THE TARGETS THAT WE'VE
IDENTIFIED DURING THE
EXPERIMENT, TOOK ALSO 1 OF THE
REPURPOSING CANDIDATES AND IN
UNDER 1 YEAR, THEY ENROLLED 64
PATIENTS IN THE CLINICAL TRIAL.
SO THE READ OUT FOR THIS 1, IS
EXPERIMENTED BEFORE THE END OF
THIS YEAR, SO FINGERS CROSSED.
BUT THAT'S ALSO THE POWER OF
CHINA, SO SOME OF THE DISEASES
ARE NOT THAT RARE AND IF YOU
WANT TO DO A QUICK INVESTIGATOR
INITIATED STUDY YOU CAN ACTUALLY
DO THAT XI -- AND I THINK THIS
IS WHERE THE POWER OF AI HAS TO
BE EXPLERRED AND EXPLOITED.
AND THEN CAN QUEE GO AFTER A
NOVEL TARGET WITH A NOVEL
MOLECULE OR REAL COMMERCIAL
PROGRAM AND IT'S VERY DIFFICULT
AND RARE.
AS A MATTER OF FACT IF YOU WERE
TO SURVEY MY TWITTER FOLLOWERS,
YOU KNOW IF THE CHINA EVER
DISCOVERED THE NOVEL MOLECULE,
THE ANSWER WOULD BE USUALLY NO
AND SOME PEOPLE DEBATE BUT THOSE
ARE USUALLY HOLDER TARGETS.
IF YOU ASK CHATGPT PREVIEW, THE
SAME QUESTION, THE ANSWER WOULD
USUALLY BE NO, AND THIS IS THE
PREROGATIVE OF VERY WELL
DEVELOPED COUNTRIES.
SO IF YOU LOOK AT ALSO ASK
CHATGPT HOW MANY COUNTRIES EVER
DISCOVERED A NOVEL DRUG, NOVEL
TARGET, NOVEL MOLECULE, THERE
ARE ACTUALLY NOT THAT MANY, AND
BEFORE TARGETED THERAPEUTICS
WHICH IS ACTUALLY PRETTY SCARY
AS WELL SO WE NEED TO GET MORE
LAYERS TO THE MOLECULAR CASINO
AND THAT'S WHY WE ALSO RELEASE
THE SOFTWARE AND WE WANTED TO
TRY AND GO AFTER THIS GOAL
OURSELVES FROM THE VERY
BEGINNING WITHOUT REPURPOSING
WITH NOVEL TARGET, NOVEL
MOLECULE, WE ACTUALLY THOUGHT WE
WOULD FAIL THAT'S WHY WE STARTED
HAVING A VIDEO DOCUMENTARY
AROUND THIS TARGET AND THIS
PROGRAM, BUT WE SUCCEEDED WHICH
WAS VERY UNEXPECTED.
THIS PROGRAM, IS DRIEBED IN THIS
PAPER, THAT WAS PUBLISHED IN THE
NATURE BIOTECH EIGHTH OF
MARCH 2024, PRETTY POPULAR
PAPER, IN THIS PAPER, WE
DESCRIBED HOW WE IDENTIFIED THE
TARGETS THAT HALLMARKS OF AGING
ASSESSMENT, KIND OF A LONG
STORY, I WON'T GO TOO DEEP INTO
THAT BECAUSE I'M PROBABLY A
LITTLE BIT OUT OF TIME,
SYNTHESIZED ONLY 79 MOLECULES
FOR THIS PROGRAM.
BY THE WAY OUR AVERAGE NOW PER
PROGRAM OVER 20 PRECLINICAL
CANDIDATES, WE SYNTHESIZE AROUND
70 MOLECULES PER PROGRAM.
SO IF YOU ARE TO BENCHMARK,
THAT'S AROUND THAT NUMBER EMPLOY
SO HERE WITH THE 79, ARE 055 IS
FIFTH MOLECULE SYNTHESIZED WITH
INDICATION PRIORITIZE EGG,
A-TINNIC FILLED THE WORK IN MANY
INDICATIONS BUT IPF WAS THE
CLASS PROBABILITY AND ALSO MOST
COMMERCIALLY TRACTABLE ROUTE
PERFORMED MULTIPLE INVITRO
SPRMS, MULTIPLE IN VIVO
EXPERIMENTS, NOMINATED 3
PRECLINICAL CANDIDATES IN 1
PAPER, THE INDICATION EXPANSION,
PHASE 0 IN AUSTRALIA, AND
HEALTHY VOLUNTEERS, 2 PHASE 1S
IN NEW ZEALAND AND IN CHINA AND
NOW, IF YOU WANT TO TALK TO THIS
PAPER, IT HAS LOTS OF
SUPPLEMENTARY MATERIALS, WE ALSO
CREATED THE CONVERSATIONAL
INTERFACE IN JUNE 2023, IT WAS
ACTUALLY VERY COOL, NOWA KAYS
YOU CAN DO IT WITH CHAT GPT, AND
I'LL SKIP THIS BUT FOR NOW WE
HAVE COMPLETED THE CHINA PHASE
2A AND OBSERVED VERY GOOD SAFETY
AND PROBABILITY, SO PRIMARY
OBJECTIVE WAS MET BUT WE ALSO
MET THE SECOND OBJECTIVE SO WE
SHOWED DOSE DEPENDENT INCREASE
IN VITAL FORCE CAPACITY WHICH
WAS VERY UNEXPECTED AND
CURRENTLY I'M NOT GOING TO SHOW
YOU DATA WHERE WORKING ON A
PROFILE PUBLICATION SO IF YOU
KNOW ANYBODY IN HIGH PROFILE
CLINICAL JOURNAL PING ME PLEASE,
WE'RE SUBMITTING BUT NOW THIS IS
THE PROGRAM THAT IS LIKELY TO
INCREASE THE CONFIDENCE IN THE
ENTIRE AI DRUG DISCOVERY.
I WILL SKIP A FEW THINGS.
BY THE WAY, IF YOU LIKE TO SEE
HOW HAN ULTRA MODERN ROBOTICS
LAB WORKS, PLEASE WELCOME TO OUR
LAB IN [INDISCERNIBLE].
WE HAVE CENTER CONNECTED ROOMS
FULLY AUTOMATED THAT WORK WITH
CELLS, TISSUES, ORGANOIDS THEY
CAN DO SAMPLE PROCESSING AND FOR
EVERY SAMPLE WE USUALLY GET
METHYLATION, TRANSCRIPTOMICS,
GENOME AND TYPES OF IMAGING AND
AI PROCESSES THIS DATA, FIX WILL
TARGETS AND FOR THE TARGETS THAT
WE MIGHT HAVE 2 COMPOUNDS, FIX 2
COMPOUNDS, INCUBATES THE
ORIGINAL SAMPLE WITH THOSE 2
COMPOUNDS AND PERFORMS THE
ENTIRE SEQUENCE 1 MORE TIME TO
SEE IF THE PREDICTIONS WERE
CONFIRMED.
IF THEY WERE AND THERE ARE SOME
TARGETS THAT DON'T HAVE 2
COMPOUNDS AND SCORE HIGHER WE
PERFORM CRSPR, AND THE SiRNA
STUDIES AND FOR THOSE THAT
SCORED HIGH AND WERE UNXCTED,
AND HAVE 2 COMPOUNDS, WE
ACTUALLY PROSECUTE THEM FARTHER
AS WELL AND NOW WE HAVE A LARGE
DATABASE OF TARGETS SO CURRENTLY
IF YOU NEED TARGETS FOR MULTIPLE
TYPES OF CANCER, FOR FIBROSIS,
MUSCLE WASTING, IBD, PLEASE
WELCOME TO TALK TO US.
>> ARE YOU FINALIZING SLIDES.
>> SURE.
SO I JUST WANT TO MENTION THAT
AGING RESEARCH IS ALSO VERY,
VERY IMPORTANT FOR DRUG
DISCOVERY, MANY BIOLOGICAL
PROCESSES HAPPEN IN TIME AND WE
GO THROUGH OUR LIFE FROM CRADLE
TO GRAVE.
DEEP NEURAL NETWORKS ARE A VERY
POWERFUL TOOLS TO ACTUALLY
CAPTURE THE PATTERNS FROM THE
CRADLE TO THE GRAVE EVEN WITHOUT
DISEASE OR WITH DISEASE AND WE
LIKE TO DISCUSS THE CAPABILITIES
OF AGING RESEARCH AS A PLATFORM
FOR DRUG DISCOVERY AT THE
CONFERENCE WE ORGANIZE, NOW IT'S
GOING TO BE THE 12th YEAR IN
COPENHAGEN, IT'S A 5 DAY EVENT.
PLEASE WELCOME.
USUALLY IT GETS SOLD OUT AT
LEAST 2 MONTHS IN ADVANCE, SO
BIG PHARMA, ACADEMIA ALL COME
TOGETHER, PLEASE JOIN AND HAPPY
TO HOST YOU THERE.
>> YEAH, THANK YOU SO MUCH ALEX,
THAT WAS QUITE IMPRESSIVE BASED
ON THE COMMENTS, IT SEEMS LIKE
THE 1 1 -- WORK YOU ARE DOING T
INSILICO IS AN INSPIRATION FOR
MANY FOLKS SO THAT LOOKS LIKE
THAT IS THE NEW STANDARD PROBE
DEVELOPMENTABLY, BUT WE WILL
JUST TAKE A COUPLE OF QUESTIONS.
ONE OF THE QUESTIONS WAS HOW
MUCH COMPUTING RESOURCES CAN I
EXPECT TO SPEND, AND SECOND
RELATIVE TO THAT IS YOU
MENTIONED THAT THERE ARE NO GOOD
BENCHMARKS FOR [INDISCERNIBLE]
MODELS, CFNS, AND CR, ARE WIDELY
USED FOR THEY APPLICABLE FOR
YOU?
>> SURE.
FIRST OF ALL MOST OF WHAT WE DO
ARE GENERATIVE MODELING, SO NOT
ONLY VIRTUAL SCREENING, VIRTUAL
KREENING IN OUR CASE
REINFORCEMENT LEARNING IS VERY
CHEAP.
SO USUALLY FOR 1 ACCESS SIZE,
YET TO A HIT, OR KIND OF A
REASONABLY DIFFICULT TARGET, YOU
WOULD SPEND ABOUT 72 HOURS IN
COMPUTE, MAYBE ABOUT -- MAYBE
SORRY ABOUT $5000 IN ADDITION TO
ANNUAL LICENSE FOR THE TOOL.
THAT WOULD INVOLVE GENERATION
AND SCREENING AT THE SAME TIME
BECAUSE YOU GENERATE USING 42
MODEL ANDS THEN YOU SCREEN USING
MORE THAN 700 MODELS.
WE ALSO HAVE ALL CHEMISTRY WHICH
ALLOWS TO YOU DO PHYSICS BASED
MODELING BUT THAT 1 IS SUPER
CHEAP AS LUSUALLY YOU'RE LEFT
WITH LIKE 20 MOLECULES AS OUT OF
CHEMISTRY 42 AND YOU WANT TO DO
HIGHER CONFIDENCE, YOU CAN DO
CERTAINLY--CERTAINLY FT DOCKING
ASK THAT WOULD COST YOU MAYBE AN
ADDITIONAL THOUSAND DOLLARS.
AND TRADITIONAL BENCHMARKS FOR
THE MODELS OF COURSE THEY ARE
APPLICABLE BUT WE HAVE OUR OWN
PIPELINE FOR [INDISCERNIBLE]
IDs, SO WE ALREADY ESTABLISHED
OUR OWN BENCHMARKS.
>> WE WILL TAKE 1 MORE, PROBABLY
LAST QUESTION AND IT YOU CAN
STAY ONLINE TO ANSWER SOME OF
THE OTHER QUESTIONS, THAT WOULD
BE GREAT.
ONE OF THE QUESTIONS WE HEARD IS
IN CASE OF AN ABSENT CRYSTAL
STRUCTURE, YOU HAVE SHOWN 1
SUCCESS.
HAVE YOU TRIED OTHER TARGETS
WHILE GOING WITH THE SAME
PIPELINE, IF YES, HOW MANY OF
THOSE RESULTED IN ACCESS?
>> IF YOU ARE TALKING ABOUT
ALPHA FOLD, WE PUBLISHED 1 ON
SIGNATURES 2 TARGET, POC STUDY,
VERY RARELY WOULD WE GO FOR
CRYSTAL STRUCTURE FOR A
COMMERCIAL PROGRAM.
USUALLY FOR A COMMERCIAL PROGRAM
WHEN YOU'RE GOING OR IF YOU'RE
PLANNING TO INVEST, YOU KNOW
$20 MILLION IN THE PROGRAM, YOU
PROBABLY GET 4 OR 5 CRYSTAL
STRUCTURES, BECAUSE IT'S, YOU
KNOW $30,000 IN 2 OR 3 WEEKS,
DEPENDING ON WHICH CRO YOU USE,
SO MY RECOMMENDATION IS ALWAYS
GET THE CRYSTAL.
>> I WOULD LIKE TO THANK ALL THE
SPEAKERS.
THANKS ALEX AGAIN, I WOULD LIKE
TO THANK THE SPEAKERS IN IN
PARTICULAR SESSION, SEAN, WOODY,
MARISSA AS WELL AS ALEX,
EXCELLENT PRESENTATIONS AND A
LOT TO THINK ABOUT FOR THE FIELD
IN GENERAL EMPLOY I WILL THANKED
OVER TO YOU BEFORE THE LUNCH
BREAK IF THERE ARE ANY
ANNOUNCEMENTS TO BREAK.
THANK YOU, EVERYONE.
>> YES, THANK YOU.
ALL RIGHT.
OH MY GOODNESS, HOW IMPRESSIVE
LIKE THESE 2 TALKS THIS MORNING
WERE AMAZING SO, THANK YOU FOR
THE SPEAKERS.
SO I HAVE PUT THE AGENDA UP
AGAIN, SO, WE'RE GOING TO TAKE A
SHORT LUNCH BREAK RIGHT NOW, AND
WE WILL BE BACK EXACTLY AT 12:30
TO START SESSION 4. OKAY, THIS IS GOING TO BE ON
EMERGING TRENDS IN THIS SILICO DRUG DISCOVERY AND WE WILL HAVE
THE NEWLY IMPLEMENTED NOBEL LAUREATE AFTER THIS LUNCH
SESSION EMPLOY SO 12:30, THANK YOU VERY MUCH, SO WE STAY ON TIME.
SEE YOU BACK HERE AT 12:30.
>> WELCOME BACK EVERYONE, LET'S
TART SESSION 4 WHICH WILL BE ON
EMERGING TRENDS IN INSILICO DRUG
DORPHY.
I WILL INTRODUCE MY COLLEAGUE
AND CO CHAIR OF THIS ORGANIZING
COMMITTEE, ALEX EY ZAKHAROV,
HERE WITH NCATS.
THE FLOOR IS YOURS.
>> EXCITED TO SEE EVERYBODY ON
ZOOM AS WELL AS VIDEOCAST.
>> LET ME OPEN UP SESSION 4 IN
WHICH WE WILL DISCUSS EMERGING
TRENDS IN INSILICO DRUG
DISCOVERY.
SO JUST A REMINDER, WE WILL HAVE
25 MINUTES FOR THE SPEAKER AND 5
MINUTES IF ARE Q&A, WE HAVE A
PRETTY BUSY SCHEDULE TODAY, AND
I WILL TURN ON MY CAMERA 2
MINUTES BEFORE Q&A, WE WILL DO
THE HARD STOP AFTER 30 MINUTES
IN TOTAL.
WITH THAT SAID, OUR FIRST
SPEAKER OF THE SESSION IS
BRYN TAYLOR, IS WITH JANUARY &
JANUARY INNOVATIVE MEDICINE, SHE
GOT OUR PHASH D AT THE
UNIVERSITY OF CALIFORNIA SAN
DIEGO IN 2020.
SHE WORKS ON EARLY STAGES OF
DRUG DEVELOPMENT FOR DIFFICULT
TARGETS IN ONCOLOGY, WE FOCUS ON
USING COMPUTATIONAL TOOLS TO
ADVANCE MODALITIES SUCH AS
TARGET PROTEIN DEGRADATIONS.
TAYLOR THE STAGE IS YOURS.
>> THEY THINKS LEXY.
I WILL GO AHEAD AND SHARE MY
SCREEN.
CAN YOU SEE THE CORRECT SCREEN?
>> YES.
>> GREAT.
THANK YOU VERY MUCH FOR THE
INTRODUCTION AND THANKS TO THE
ORGANIZERS ARE HAVING ME HERE.
TODAY SO AS LEXY SAID I WORK AT
JANUARY & JANUARY WITH THE
MEDICAL DISCOVERY GROUP IN
LA HOYA, AND WE WILL TALK ABOUT
UNDERSTANDING PROTAC
PERMIABILITY USING MODEL CITIZEN
LEAKULAR DISCIPLINARY MEDICARE
AND MEDICAIDICS IN SIMULATIONS
AND MODELS AND DEEP LEARNING.
SO AS A BRIEF BACKGROUND, SO
THEY ARE HETERO-- THEY HAVE 2
WAR HEADS AND A LINKER SO 1 WAR
HEAD WILL BIND THE PROTEIN OF
INTEREST, THE POI THERE AND THE
OTHER 1 BINDS AN E3 LIGASE, SO
THE E3 DISCIPLINARY LAS PINTAS
WILL TACK THE UBIQUITIN
TARGETING THE DEGRADATION BY THE
PROTEIN COMPLEXIOSOME.
SO THIS IS CONTRAST THE CLASSIC
SMALL MOLECULE INHIBITOR
APPROACH FOR TARGETING A PROTEIN
AND SIMPLY REMOVING ITSELF
FUNCTION, THIS 1 DEGRADE THE
ENTIRE PROTEIN ALTOGETHER.
SO AS YOU MIGHT GUESS, A COMMON
ISSUE WITH USING PROTAC'S DRUGS
WHICH IS DUE TO LOW VIABILITY
BUT THAT MAY BE PART TO THEIR
LOW PERMIABILITY.
SO I'M SHOWING A PROTAC TO
GREATER FIELD IN INDUSTRY, SO MY
FORMER COLLEAGUE LORI
[INDISCERNIBLE] DEMONSTRATED
THIS AT SEVERAL PHARMACEUTICAL
COMPANIES AND ASKED THEM TO
REPORT WHAT WAS THE MOST
SIGNIFICANT ADME OR PHYSIO
CHEMICAL PROPERTIES, FACING
DEVELOPMENT OF ORALLY
BIOAVAILABILITY DEGRADE ORS, SO
IN BLUE IS THE PRIMARY
CHALLENGE.
RED WAS SECONDARY CHALLENGE,
THIS WAS PEOPLE VOTING IN
SURVEY, PEOPLE AGREED IT WAS
PERMIABILITY, SOLUBILITY ALONG
WITH MODEL CITIZEN LEAKULAR
WEIGHT WHICH DOES CONTRIBUTE TO
PERMIA ABILITY AND SOLUABILITY
AND SECONDARY CHALLENGES, THESE
PROBLEMS ARE VERY COMPLEX, SO IT
SPANS EVERYTHING BUT INCLUDES
THINGS LIKE ABSORPTION, LIQUID
[INDISCERNIBLE] AND THINGS WE
WILL TALK ABOUT BRIEFLY.
SO AN EXAMPLE OF A PROTAC IS ON
THE RIGHT, IT'S A PART OF A
LARGER DATA SET PATENTED BY J&
J, AND BECAUSE IT'S PATENTED I
GET TO USE IT AS AN EXAMPLE
THROUGHOUT THIS TALK.
I WILL ALSO SPEAK ABOUT INTERNAL
DATA, I WON'T SHOW STRUCTURES
BUT I AM ALLOWED TO TALK
ABOUT
IT.
THESE ARE CANINE KIDNEY CELLS
AND DRUGS AND PROBLEM IS MDCK
CELLS EXHIBIT ENDOGENOUS
ACTIVITY WHICH LEADS TO AN
UNDERESTIMATION OF PERMIABILITY.
SO, TO GET AROUND THIS, WHAT WE
DO IN MANY OTHER GROUPS DO, IS
WE USE MDCK CELLS TRANSPORTERS,
SO WHENEVER I TALK ABOUT
CORRELATIONS WITH PERMIABILITY
THIS IS THE ASSAY WE ARE GETTING
OUR DATA FROM.
AND WHEN WE USE THIS
EXPERIMENTAL PERMITIABILITY DATA
FROM THESE KNOCK OUT MDCK CELL
LINES, WE ARE ABLE TO TRAIN
MACHINE LEARNING MODELS THAT
CORRELATE PRETTY WELL WITH
EXPERIMENT, THIS IS -- SO, ON
THE LEFT-HAND SIDE OF THIS SLIDE
IN BLUE, YOU CAN SEE ALL THE
DATA FOR A SERIES OF PROTAC AND
ON THE RIGHT AND ORANGE IS JUST
THE SET OF COMPOUNDS THAT THE
MODEL HAD NEVER SEEN AND WAS
SYNTHESIZED AFTER THE MODEL WAS
CREATED.
SO, YOU KNOW THIS IS IN BLUE
ACTUALLY JUST THE TEST SET OF
ALL DATA SO 25% AND THE
STATISTICS FOR THAT RSQUARED,
.77, THE RANK ORDER IS QUITE
GOOD AND YOU CAN SEE THAT WE'RE
ABLE TO GET A PRETTY GOOD
UNDERSTANDING OF HOW THESE
PROTAC AT LEAST IN THIS SPECIFIC
SERIES, SO IT'S NOT NECESSARILY
GENERALIZABLE TO THIS EVER, BUT
IN THIS SERIES WE'RE ABLE TO GET
A GOOD IDEA OF WHAT THE
PERMIABILITY WILL BE WHICH IS
REALLY EXCITING AND IS VERY
HELPFUL WHEN WE HAVE LARGE
LIBRARIES OF COMPOUNDS THAT WE
NEED TO SELECT JUST A SET TO
MAKE.
HOWEVER, THESE ARE WHAT A JUST
TALKED ABOUT HEAR LIGAND BASED
MACHINE LEARNING MODELS, 2 D,
FINGERPRINT BASED MODELS, AND
THERE ARE ACTUALLY LESS USEFUL
FOR IESHES DENTIFICATION OF WHAT
ARE ACTUALLY THE KEY
CONTRIBUTORS TO PERMIABILITY,
ESPECIALLY IF THE CONTRIBUTORS
AREN'T JUST A SINGLE PROPERTY,
OR OBVIOUS DESCRIPTORS, AND
THAT'S LIEWCIALLY WHAT WE FIND
IS THE CASE, THIS IS A COMPLEX
ISSUE, AND YOU KNOW FROM THE
INDUSTRY PERSPECTIVES PAPER THEY
MENTIONED EARLIER, SOME FEATURES
THAT MAY BE ASSOCIATED WITH
PROTECT PERMIABILITY THAT WE
HAVE LOOKED AT AND DON'T
NECESSARILY CORRELATE PERFECTLY
WITH THE PROTAC PERMIABILITY MAY
CONTRIBUTE SO THINGS LIKE OF A
SURFACE AREA OF 100-200.
MOLECULAR WEIGHT, UNDER 900
WOULD BE IDEAL, DONORS AND
ACCEPT ACCEPTORS, AND THE
HYDROGEN BONDS, YOU CAN SEE THE
TABLE THERE, LESS THAN 14
HYDROGEN BOND RECEPTORS, AND
LESS THAN 500 HYDROGEN BOND
DONORS AND LIP O PHYLLISITY
PLOAS A ROLE AND A PH OF AROUND
7.4 WOULD BE GREAT.
BUT THE QUESTION IS DOES IT GO
BEYOND THE DESCRIPTORS AND IS
THERE A COMPONENT OF THE
RIGIDITY OR FLEXIBILITY OF THE
PROTAC.
I BRING IT UP BECAUSE 1 OF THE
WAYS THEY ARE ABLE TO XIPT THE
CELLS IS THAT THEY
[INDISCERNIBLE].
SO IN A MEMBRANE LIKE POLAR
VIERNLT, THEY'RE ON THE LEFT IN
RED, THEY ASSUME THIS COLLAPSED
KIND OF STATE, SO YOU KNOW CRUMP
PELLED UP AND IN A POLAR
ENVIRONMENT SUCH AS
INTRACELLULAR OR EXTRA CELLULAR,
THEY'RE MORE YELLOW, SO BEYOND
THE ABILITY THOUGH, TO
TRANSITION BETWEEN THE STATES
BASED ON THE SOFULLENT
ENVIRONMENT, I THINK THE RATE OF
TRANSITION PROBABLY MATTERS.
AND THAT MEANS LIKE IF A PROTAC
IS ABLE TO WICKLY ADAPT A
COLLAPSED CONFIRMATION, GET THE
MEMBRANE AND QUICKLY ADOPT AN
ELONGALTIED CONFIRMATION IN THE
RIGHT ENVIRONMENT, IT MAY LEAD
TO A BETTER CHANCE OF GETTING IN
AND OUT OF MEMBRANE, NOT GETTING
STUCK IN THE MEMBRANE AND ALSO
BEING ABLE TO ATTACH TO ITS E3
AND POI BINDING PARTNERS AND
ACTUALLY INITTIAIT THAT
DEGRADATION SEQUENCE.
SO TO ASSESS THIS HYPOTHESIS, WE
CREATED AN UNBIASED MOLECULAR
DYNAMIC SAMPLING WORK LOAD TO
DETERMINE THE KEY COMBINATION IS
IN DIFFERENT SOLVENTS SO WE USED
WATER, MEL AN OLDER PEOPLE AND
CHLOROFORM WHICH HAD
CONCENTRATES OF 80, 32, AND 5
JUST ABOUT.
AND CHLOROFORM WITH THE
DIALECTIC OF 5, AND CELL
MEMBRANE, AND WATER WORKS WITH
THE INTRACELLULAR AND EXTRA
CELLULAR ENVIRONMENT AND
METHANOL WAS SOMEWHERE IN
BETWEEN.
AND WE RAN DYNAM DYNAMICS, ANDN
ABOUT 5 MICRO[INDISCERNIBLE] OF
MD BUT MD SIMULATIONS ARE GREAT,
THEY'RE FUN TO WATCH WITH SOME
POPCORN, BUT YOU KNOW IT'S AN
IMMENSE AMOUNT OF DISPARATE DATA
ESPECIALLY IF YOU SCALE UP
THROUGH OR 4 PROTAC, IF YOU ARE
LOOKING AT HUNDREDS THIS BECOMES
MORE COMPLICATED TO INTERPRET.
SO HOW DO WE CONDENSE THIS BIG
DATA DOWN TO A HUMAN
INTERPRETABLE FORMAT,
PARTICULARLY IN AN AUTOMATED
WAY.
SO THERE ARE MANY SUCH WAYS TO
DO THIS WONG IS TO USE THE
MARKER MODEL, IT'S A MARKER
MODEL THAT IDENTIFIES
KINETICALLY COMPOSITIONS AND THE
RATE BETWEEN THE STATES.
SO VERY BRIEFLY TO DO MD
SIMULATIONS, YOU CLUSTER AND USE
THE CLUSTER OF THE STATES SO
THIS SOUNDS LIKE A NORMAL
CLUSTERS WORK FLOW, WHAT IS MSM
IN THEORY ADD TO THIS, WELL IN
THE PROCESS OF MODEL REFINEMENT,
WE SELECT CLUSTERS THAT ARE MORE
SERIOUS STAKES, SO THEY'RE SO
STABLE THAT THEY MUST HAVE
FORGOTTEN WHERE THEY CAME FROM
BEFORE LEAVING.
SO WE SIGN THESE STATES.
AND THEN AFTER WE DEFINEITATES,
WE CAN ASSESS THE PROBABILITY OF
THE LIGAND STAYING IN THAT
COMPOSITIONAL STATE OR
TRANSITIONING TO ANOTHER STATE.
SO THIS -- THIS METHOD, YOU KNOW
A ALLOWS US TO IESHES DENTIFY
THE STATE OF OUR PROTAC AND
UNDERSTAND HOW WELL THE PROTAC
MOVES BETWEEN THOSE STATES.
SO THIS IS -- THIS IS THE KIND
OF THE PERFECT APPLICATION FOR
THIS QUESTION WE HAD BUT THE
ONLY ISSUE IS, YOU KNOW
CLASSICAL MARKER STATE MODELS
ARE WHEN MANY STATES ARE USED
BUT BECAUSE WE'RE ONLY
INTERESTED IN 2 STATES COLLAPSED
AND ELONGATED, WE USED
[INDISCERNIBLE] WHICH CAN
PRODUCE AN ACCURATE MODEL WITH
VERY FEW STATES SO VASM NETS
REPLACES AND OF COURSE MSCORE
SCREENING WITH A DEEP NEURAL
LEARNING NETWORK, WE DIDN'T COME
UP WITH ANY OF THIS ALTHOUGH THE
CITATIONS ARE AT THE BOTTOM,
THIS IS JUST WHAT WE OOH PLIED
TO THIS PROJECT.
SO ONCE TRAJECTORY ARE
DISCREETIZED INTO METASTABLE
STATES USING MARKOV STATE MODEL
AND ANALYZED WITH NEURAL
NETWORKS IT PROVIDES 2 STATES
ELONGATED YOU CAN SEE COLLAPSE
IN THE SALMON COLOR AND THE
ELONGATED IN THE TEAL AND I WILL
EXPLAIN LATER ON HOW THE STATES
ARE IDENTIFIED AND WHAT
COLLECTIVE VARIABLES WERE USED
BUT THIS IS WHAT WE WANT TO GET
OUT OF THIS ANALYSIS.
SO WE SELECTED COMPOUNDS OF A
RANGE OF PERMIABILITY VALUES,
THE DATA AND EVERYTHING, I'M
SHOWING HERE IS ACTUALLY FROM
OUR INTERNAL DATA SET SO I CAN'T
SHOW THE ACTUAL STRUCTURES, YOU
CAN SEE A NUMBER OF PROTAC HERE
HAVE A VERY LOW MDCK KNOCK OUT
VALUE, THIS IS BY NO MEANS A
PERMEABLE COMPOUND BUT IT IS THE
RANGE AND THE RANGE WE ARE
WORKING WITH.
SO FIRST WE HAD TO DEFINE WHAT
COLLECTIVE VARIABLES WE WERE
BUILDING THE MODELS ON AT ALL SO
HOW DID WE DEFINE STATES, HOW DO
WE DEFINE COLLAPSE VERSUS
ELONGATED SO WE ALREADY TALKED
ABOUT THESE NUMBERS OF PROTAC,
THAT MIGHT CONTRIBUTE TO
PERMIABILITY OR VIABILITY BUT
OTHER CHARACTERISTICS CAN WE
PULL FROM THE SIMULATION?
SO EXAMPLES INCLUDE SAMPLES,
BOND OR RSD, OR WE SETTLED ON
END-TO-END DISTANCE AND THAT'S
JUST MEASURING THE SIMPLE
PROTAC.
SO SIMPLE METRIC.
WE TESTED PROBABLY 2 DOZEN OF
THESE DIFFERENT DESCRIPTORS IN
ORDER TO TRY TO FIND SOME SORT
OF CORRELATION THAT WAS
GENERALIZABLE ACROSS AS MANY
PROTACS AS WE COULD FIND.
AND WHAT WE FOUND WAS IN THAT
PERMIABILITYS IS HIGHER WHEN THE
END-TO-END DISTANCE IS LOWER.
SO THE PLOTS BELOW SHOWED 3
DIFFERENT COMPOUNDS, THEY'RE ALL
IN CHLOROFORM AND THESE ARE JUST
MD SIMULATION DATA AND THE 1 ON
THE LEFT HAS LOWEST AVERAGE
END-TO-END DISTANCE AND HAS THE
HIGHEST PERMIABILITY SO
18.5-ANGSTROMS AND 10 TO THE
SIXTH, SO YOU WILL NOTICE THAT
THE DIFFERENCES, THE DISTANCES
ARE NOT VERY DIFFERENT, RIGHT?
SO THE LOWEST AND 18 AND A HALF,
THE HIGHEST IS 20.3, LIKE THIS
IS ALMOST IN THE NOISE HOWEVER,
THE COOL PART ABOUT THESE 3
PROTAC IS THEY ALSO HAD VERY
SIMILAR PERMEABLE VALUES, SO
EVEN A SLIGHT CHANGE IN
COLLAPSABILITY MAY AFFECT
PERMABILITY, SO THIS IS WHAT WE
STARTED WITH AND WHEN WE LOOKED
-- WHEN WE PERFORM THIS
ANALYSIS AND WHAT THE SPECIFIC
EXAMPLES PAIRS OF HIGH AND LOW
PERMEABLE COMPOUNDS WE DO SEE
SOME DIFFERENCES SO THIS WHAT I
SHOWED PREVIOUSLY WAS JUST THE
MD SIMULATION DATA AND LOOKING
AT 1 DESCRIPTOR, NOW WHAT WE'VE
DONE IS WE'VE DONE THIS MSN WORK
FLOW AND WE HAVE THE RESULTS,
SO, I'M SHOWING HERE A WONDERFUL
GREEN BLOB WHICH UNFORTUNATELY I
CAN'T SHOW THE EXACT STRUCTURE
OF, BUT IT HAS A HIGHER, YOU
KNOW IT HAS THE -- 1 OF OUR
HIGHEST PERMIABILITYS AND WE
FIND THAT IN CHLOROFORM.
THERE'S A SLIGHTLY HIGHER
PERCENTAGE OF COLLAPSE
CONFIRMATION, BY SLIGHTLY HIGHER
I MEAN 51%, SO IT'S ABOUT EVEN
BETWEEN COLLAPSE AND SALMON AND
ELONGATED IN BLUE.
AND REMEMBER, CHLOROFORM IS
APPROXIMATING THE MEMBRANE HERE.
IN CONTRAST WHEN WE LOOK AT
COMPOUND 2 WHICH IS NOTABLY
SIMILAR TO COMPOUND 1 EXCEPT FOR
THIS 1 R-GROUP.
COMPOUND 2 HAS MUCH LOWER
PERMIABILITY AND IN CONTRAST
ONLY 43% OF THE CONFIRMATIONS
ARE COLLAPSED.
SO, IT'S SAYING THAT IN THE
MEMBRANE, COMPOUND 2 WHICH HAS A
LOWER PERMIABILITY ISN'T FORMING
AS MANY OF THOSE COLLAPSE
CONFIRMATIONS AS THE MORE
PERMEABLE COMPOUND 1.
SO WE ALSO KNOW THAT INTERNAL
[INDISCERNIBLE] HYDROGEN BONDS
ARE ASSOCIATE WIDE INCREASE IN
PERMIABILITY.
SO THIS PAPER SHOWS THAT IN THE
SOLVENTS 2 INTERIN LECULAR
HYDROGEN BONDS FORM AND ALLOW
FOR A MORE COLLAPSED STATE WITH
A LOWER 3D POLAR SURFACA AREA.
SO WHEN WE LOOKED AT THE
FORMATION OF HYDROGEN BONDS
WITHIN THE METASTABLE STATES OF
THE PERMEABLE AND LESS PERMEABLE
COMPOUND THAT I JUST TALKED
ABOUT, WE DID FIND THAT THE MORE
PERMEABLE COMPOUND HAS MORE
HYDROGEN BONDS FORM NOTHING ITS
COLLAPSED STATE.
SO THAT MEANS THAT THE MORE
PERMEABLE COMPOUND IS ABLE TO
FORM THOSE INTRA MOLECULAR
HYDROGEN BONDS LEADING TO A MORE
COLLAPSED STATE WHERE THE LESS
PERMEABLE COP POUND ISN'T ABLE
TO.
AND AGAIN HERE ARE MORE BLOBS
THAT DON'T MEAN ANYTHING EXCEPT
THAT THESE REPRESENT THE SEND
ROADWAYS WE ARE SEEING IN SOME
OF THESE METASTABLE STATES FROM
THE MARKOV AND STATE ANALYSIS,
AND WE SEE THAT KEY HYDROGEN
BONDS ARE FORMING IN THE
COLLAPSED STATES SO THIS DOESN'T
-- THIS ISN'T LIKE REVOLUTIONARY
RIGHT HERE AND WE'RE SEEING THAT
THE WE ARE SEEING HYDROGEN BONDS
FORM WHEN WE LOOK AT THE
COLLAPSED FORM OF THE PROTACS.
AND THEN WE WORKED WITH OUR
EXPERIMENTAL COLLEAGUES WHERE WE
EVALUATED THE SAME COMPOUNDS IN
WATER AND CHLOROFORM, WE FOUND
THAT IN DSO, THERE ARE FEWER
HYDROGEN BONDS FORMED, WHICH IS
NOT SURPRISING BUT IT DOES MAKE
US MORE CONFIDENT IN THE
MODELING RESULTS WHICH SHOWED
THE SAME THING.
INCLUDING ACTUALLY THE SAME
INTRA MOLECULAR HYDROGEN BONDS,
SO I WILL WALK THROUGH THIS
FIGURE WITH YOU, AGAIN I HAD TO
REMOVAL THE COMPOUND INFORMATION
SO I SKETCHED IT AS BEST AS I
COULD AND THE ABCD JUST REFER TO
SPECIFIC ATOMS AND THE NUMBER
THERE ARE TEMPERATURE CO
EFFICIENT AND GREEN MEANS MORE
POSITIVE, RED MEANS MORE
NEGATIVE AND THE MORE POSITIVE
TEMPERATURE CO EFFICIENT IS
ASSOCIATE WIDE A STRONGER
MOLECULAR HYDROGEN BOND OR
INCREASED SHIELDING, SO ON THE
TOP THERE IN THE -- WITH THE
NUMBER, THE NMR RESULTS IN DMSO,
AND CDCL IS BOTTOM RIGHT IS
MD-SIMULATION CHLOROFORM
COLLAPSED SIN THROID SO YOU CAN
SEE THAT THE MOLECULAR BONDS
FROM NMR, ARE ARE IN THE
ENSEMBLES AND THERE ARE MORE
BONDS IN CHLOROFORM AND CDCL3
THAN IN WATER SORE BOTH IN
EXPERIMENT AND THE MSM
ENSEMBLES.
AND THIS GAVE US MORE CONFIDENCE
THAT WHAT WE'RE MODELING IS
AGREEING WITH EXPERIMENT MEANING
WE CAN USE THIS INFORMATION
WITHOUT USING NMR DATA IN THE
FUTURE AND WE CAN JUST USE
MODELING AND HAVE GOOD
CONFIDENCE IN THIS.
AND THIS GENERALIZED TO A NUMBER
OF THE COMPOUNDS WE ARE LOOKING
AT, IT WASN'T JUST THIS 1
EXAMPLE BUT I FIGURED, YOU ALL
DIDN'T NEED TO SEE MORE BLOBS OF
COMPOUNDS.
OKAY, SO, THIS ANALYSIS WAS
PERFORMED ACTUALLY ON SHOR
SHORTER MICROSECOND LONG
SIMMULESS AND CLUSTERED WITH,
YOU KNOW JUST STANDARD
CLUSTERING METHODS, BUT
INTERESTINGLY, WE DIDN'T SEE ANY
CORRELATION OF PERMIABILITY, AND
WE DIDN'T SEE ANY OF THE INTRA
MOLECULAR HYDROGEN BONDS
SUPPORTING THE NMR RESULTS, AND
SURE THOSE INTRA MOLECULAR
HYDROGEN BONDS APPEARED AND IT
WAS KIND OF -- DIDN'T CORRELATE
WITH ANYTHING, WE DIDN'T PULL
OUT ANY CORRELATION AND SEEMS
SAMPLING WITH SAMPLING AND
LONGER DETAILS, AND ALSO
INTERPRETING SIMULATION DATA
WITH MSNs, PROVIDED MORE
INFORMATION FOR ANALYSIS AND
COMPOUND DESIGN, SO IT SEEMED
PRETTY NECESSARY TO BE ABLE TO
INTERPRET THAT DATA.
SO FULL, OUR ONGOING EFFORTS
RIGHT NOW ARE THAT WE'RE USING A
DATA SET OF THE 135 IRAC PROTAC
AND DOING THIS WORK FLOW ON ALL
OF THEM AND YOU KNOW THAT'S
GOING TO BE PUBLICLY SHAREABLE
WHICH IS EXCITING BUT WE'RE ALSO
DOING THE SAME THING ON THE
INTERNAL SIMILAR NUMBER OF
PROTACS AND THE NICE PART ABOUT
THAT IS WE CAN ASSESS IT ON 2
VERY DIFFERENT SERIES AND SEE
FOR VERY DIFFERENT TARGETS ANDEE
IF THIS IS GENERALIZABLE.
NOTABLY WE'RE ALSO WORKING ON
IMPROVING THE PERMIABILITY
SCORE, SO I DIDN'T TALK TOO MUCH
ABOUT THAT TODAY, BUT WHAT I
MEANT BY PERMIABILITY CORE IS WE
LOOKED AT THESE RATIOS OF
CONFIRMATIONS AND PERCENTAGES OF
ELONGATED VERSUS COLLAPSE AND
KIND OF A RATIO OF RATIOS, THIS
IS GREAT, IT WILL BE NICE IF IT
WAS SIMPLE, IT'S PROBABLY NOT
THAT SIMPLE, IT'S PROBABLY MUCH,
MUCH, MORE COMPLEX, AND SO, YOU
KNOW THE FIRST THINK THIS WE
NEED TO LOCK AT IS, ARE 2 STATES
REALLY ENOUGH, COLLAPSED VERSUS
ELONGATED.
MY GUESS IS NO, BUT WE'RE
STARTING TO WITH THE 2 STATE
THEORY AND SEE HOW FAR WE CAN
GET WITH THIS.
WE'RE ALSO DOING COMPARISON OF
ALL THESE MATCHED PAIRS WITH
HIGH AND LOW PERMIABILITY, I
JUST MENTIONED 1 TODAY, AND LIKE
I SAID, YOU KNOW WE'RE
INTERESTED IN DOING SOME
GENERALIZABILITY ACROSS OTHER
SERIES AS WELL.
AND ANOTHER IMPORTANT ASPECT
THAT I DIDN'T TOUCH ON TOO MUCH
TODAY IS THE REASON WE'RE SO
INTERESTED IN PERMIABILITY IS
BECAUSE OF THE BIOAVAILABILITY
ULTIMATELY, THAT'S WHAT THE GOAL
IS AND WE KNOW JUST BECAUSE A
PROTAC DIDN'T HAVE HIGH
PERMIABILITY, MAYBE IT EVEN HAS
LOW PERMIABILITY, WE DO GET
COMPOUNDS WITH LOW PERMIABILITY,
TO HAVE BIOAVAILABILITY.
THIS IS A COMPLEX ISSUE,
HOWEVER, IF YOU GET INTO A
PERMEABLE SPACE, IT CAN USUALLY
HELP GET INTO A HIGHER
BIOAVAILABILITY SPACE?
SO HOW DO WE USE ALL OF THIS
INFORMATION TO DESIGN PROTAC
WITH HIGHER PERMIABILITY, DO YOU
WANT TO DESIGN AN OPTIMAL INTRA
MOLECULAR HYDROGEN BONDS AND ON
THAT NOTE, WE ONLY TALKED ABOUT
INTRA MOLECULAR HYDROGEN BONDS
TODAY, BUT THERE'S SO MUCH DATA
IN THE MARKERS THAT WE CAN USE
TO EXPLORE THESE PERMEABLE AND
IMPERMEABLE PROTACS, WE'RE JUST
SCRATCHING THE SURFACE HERE, SO
WITH THAT, I WANT TO THANK ALL
OF MY COLLABORATORS, J& J, AND
IN PARTICULAR I WANT TO THANK
[INDISCERNIBLE] HE'S A CURRENT
RESEARCH FELLOW AT THE FLAT IRON
INSTITUTE, HE WAS A Y& J SUMMER
INTERN AND HE DID HIS Ph.D. AT
UCSD WITH [INDISCERNIBLE], AND
HE IS STILL WORKING WITH US AND
WE HAVE SOME HOPEFULLY RESECTION
SIGHTING RESULTS COMING OUT IN
THE NEXT SEVERAL MONTHS, SO,
WITH THAT I WOULD LIKE TO THANK
YOU FOR YOUR TEAPGZ AND I CAN
TAKE ANY QUESTIONS.
>> THANK YOU VERY MUCH.
I THINK THIS IS GREAT
PRESENTATION AND REALLY GOOD
REVIEW OF THE PRODUCT.
SO WE HAVE SEVERAL QUESTIONS IN
THE CHAT, LET ME START WITH 1,
THE FIRST 1.
HAVE YOU TRIED DIRECTLY
SIMULATING THE PASSAGE OF
PRODUCTS FROM THE MEMBRANE.
>> YES.
NO, WE HAVEN'T.
BUT WE THOUGHT ABOUT IT THOUGH,
WE THOUGHT ABOUT PULLING A
PROTAC OR A MEMBRANE FOR
EXAMPLE, BUT WE HAVEN'T DONE IT,
I'M CURIOUS TO DO IT.
I THINK IT WOULD BE HARDER TO DO
ON A LARGER SCALE.
I WAS HOPING TO SCALE THIS UP TO
HUNDREDS OF PROTACS, I DON'T
KNOW HOW I WOULD SCALE THE POLE
ANCHOR BUT I GUESS WE COULD.
YEAH, SO, -- THAT'S A GREAT
IDEA.
WE HAVE DONE SOME OTHER WORK
WITH OTHER TOOLS IN MEMBRANES,
THAT DIDN'T REALLY WORK OUT TOO
WELL.
NNOW FOR ANOTHER QUESTION,
[INDISCERNIBLE] FOR THE
DIFFERENT TETRAMERS, DO YOU HAVE
THIS IDEA?
>> COULD YOU REPEAT THAT?
OH, I SEE.
THAT'S A GREAT QUESTION.
WE WE HAVE THOUGHT ABOUT THIS.
WE'VE ALSO IGNORED IT TO SOME
EXTENT, SO THAT'S DEFINITELY A
SCAPEGOAT ANSWER.
BUT NO, IT'S A GOOD QUESTION.
WE BASICALLY HAVEN'T ADDRESSED
IT.
WE UNDERSTAND IT'S AN ISSUE AND
SOMETHING TO CONSIDER BUT WE
HAVE NOT WORKED ON THAT
PARTICULAR ISSUE.
>> FOR THE TETRAMERS.
>> EXACTLY.
>> THE OTHER QUESTION IS WHAT IS
THE DISTANCE IN, HAVE YOU DONE
STUDIES WITH MOLECULAR
FLEXIBILITY DESCRIPTORS TO
CURATE YOUR RESULS, HAVE YOU
TRIED ANY MEDICAL FLEXIBILITY
DESCRIPTORS TO MAKE A
PREDICTION?
>> SO MOLECULAR FLEXIBILITY
DESCRIPTORS, ARE WE TALKING
ABOUT RSD OR RADIO GENERATION OR
THESE SORTS OF THINGS?
>> HAVE YOU TRIED ANY OF THOSE?
>> YEAH, WE TRIED ALL OF THEM.
WE TRIED PROBABLY 2 DOZEN
DIFFERENT MOLECULAR FLEXIBILITY
DESCRIPTORS, BASICALLY ANYTHING
WE COULD THINK OF AND THIS IS
LIKE INITIAL WORK, RIGHT WHERE
WE'RE TRYING TO CORRELATE
BASICALLY ANYTHING, ANY
DECRYPTER WITH -- TO SEE IF IT
WOULD CORRELATE, AND THEN THE
MOST RELIABLE ACROSS ALL THE
PROTACS.
>> GOTCHA.
ONE MORE QUESTION, HOW COULD 1
DETERMINE IF THE MD SIMULATION
[INDISCERNIBLE] ENOUGH FOR THE
ANALYSIS?
>> THAT'S A GREAT QUESTION.
DEPENDS ON YOUR SYSTEM.
AND YOU KNOW I THINK THE
GENERALLY, THE MORE DATA THE
BETTER, I'M NOT NECESSARILY THE
MSM EXPERTS AND THERE ARE MANY
WAYS TO ANALYZE THAT.
FOR THIS PARTICULAR SYSTEM, SO
WE WORKED WITH MSMs IN GRAD
SCHOOL AND LARGER PROTEINS FOR
THIS SMALLER SYSTEM WHICH IS
REALLY JUST, YOU KNOW, THESE
AREN'T HUGE, THEY'RE LARGE SMALL
MOLECULES BUT THEY'RE NOT GIANT.
IT SEEMS THAT A COUPLE
MICROSECONDS IS GOOD ENOUGH FOR
THESE, BUT LIKE I SAID, SO THIS
IS 2 DIFFERENT QUESTIONS, SORRY
I'M GETTING INTO 2 DIFFERENT
THINGS, WOKNOW IS HOW LONG THESE
CAN BE FOR THE SAME AMOUNT OF
THE BUILDING, JUST TOTALLY
DEPENDS ON THE SYSTEM, RIGHT?
FOR THIS PARTICULAR SYSTEM WHICH
I'M GOING TO ANSWER THE QUESTION
FOR, IT SEEMS TO BE THAT WE NEED
MORE THAN A MICROSECOND EACH
THOUGH THAT SEEMS QUITE LONG TO
ME BUT IT SEEMS TO GET ANY OF
THE NRGZ OUT THAT WE WANT TO,
YOU DO NEED MORE THAN A
MICROSECOND.
>> GOTCHA.
YEAH 1 QUESTION I WANT TO ASK
YOU, IT'S A BIT OFFTOPPIC BUT
THE PRODUCT HAS DIFFERENT
LINKERS AND DIFFERENT SIZE OF
LINKERS HAVE YOU LOOKEDDA THE
THAT PROBLEM, HAVE YOU USED KIND
OF LIKE MACHINE LEARNING FOR
INSILICO MINUTES DO YOU PREDICT
THAT BECAUSE SOMETIMES AFFINITY
IS [INDISCERNIBLE] AS WELL AS
ACUITY.
NYEAH, THE LINKER QUESTION,
YEAH, LINKERS MAKE A HUGE
DIFFERENCE, YOU'RE ABSOLUTELY
RIGHT, WE HAVE LOOKED AT IT IN
TERPS OF WE HAVE VERY RIGID
LINKERS AND FLEXIBLE LINKERS
THAT ARE THE SAME LENGTH SO
WE'RE TRYING TO CONTROL FOR
LENGTH OF LINKER BY LOOKING AT
RIGIDITY AND FLEXIBILITY BETWEEN
THE 2 AND YEAH, THE FLEXIBILITY
LINKERS ALLOWED FOR MORE COMPACT
PROTACS AND THEY CAN FOLD UP AND
THE REGULAR LINKERS CAN'T.
WE LOOKEDDA THE LINKER LENGTH,
TYPE, YEAH, THATIA A -- BUT YUF
BECAUSE A LINKER IS VERY
FLEXIBILITY DOESN'T NECESSARILY
MEAN IT WILL BE THE MOST
PERMIABILITY EITHER.
NOKAY.
SOUNDS GOOD.
SOMEONE ASKED ABOUT THE
[INDISCERNIBLE] OF YOUR MACHINE
LEARNING MODELS, IT'S POINTED
OUT [INDISCERNIBLE] AND THEY ASK
DID YOU TRY TO IMPROVE THAT
BEFORE TRY THE SIMULATION TO GET
INSIDE.
SO BASIC LYE YEAH, IT'S A MORE
GENERAL QUESTION WHEN YOU DO
MACHINE LEARNING AND THE SAME
PIPELINE, HAVE YOU TRIED TO
IMPROVE THE MACHINE LEARNING OR
IT'S TOTALLY FINE, LIKE WHATEVER
THE ACCEPTABLE METRICS FOR YOU,
IN IT CASE, IN TERMS OF
[INDISCERNIBLE].
>> YEAH, WE ARE ALWAYS TRYING TO
IMPROVE THE MACHINE LEARNING
MODELS FOR PERMIABILITY WITH
THIS PARTICULAR 2 D MODEL THAT
THE R-SQUARED SCORE IS PRETTY
GOOD.
WE -- IT'S GOOD ENOUGH FOR OUR
-- WE HAVE A CUT-OFF BASICALLY
IF IT'S PREDICTED TO BE ABOUT A
CERTAIN KIND OF VALUE THEN WE
MOVE IT FORWARD IF IT'S BELOW,
WE GET RID OF IT AND WE DON'T
MOVE IT FORWARD AND WE
CONTINUOUSLY CHALLENGE THE
MODEL, TOO.
SO THIS MODEL'S PRETTY GOOD IS
BASICALLY WHAT I'M SAYING
HOWEVER, IF YOU THROW IN
SOMETHING THAT THE MODEL HAS
NEVER SEEP BEFORE, SOMETHING
VERY DIFFERENT, VERY HIGH
PERMIABILITY OR SOMETHING AND
MAYBE A STRUCTURE BASED CHANGE,
THAT DOESN'T SEEM LIKE IT'S
GOING TO BE A HUGE DIFFERENCE,
THE MODEL WON'T BE ABLE TO
PREDICT THAT VERY WELL AND WE'VE
SEEN THAT HAPPEN BEFORE AND
THAT'S BASICALLY WHY WE PIVOTED
TO ALSO USING THESE PHYSICS
BASED METHODS BECAUSE YOU JUST
CAN'T CAPTURE IT ALL WITH 2 D
INFORMATION.
NSOUNDS GOOD, THANK YOU VERY
MUCH AGAIN BRYN FOR THE GREAT
TALK AND PRESENTATION.
>> THANK YOU AND THANKS FOR THE
GREAT QUESTIONS EVERYBODY.
NOW LET'S MEET SHERYL
ARROWSMITH, SENIOR SCIENCE
PROFESSOR MEDICAL BIOPHYSICS AT
THE UNIVERSITY OF TR RONT O AND
CHIEF SCIENTIST OF THE
STRUCTURAL GENOMICS CONSORTIUM
AT THE UNIVERSITY OF TORONTO.
SHE HAS PUBLISHED OVER 300
RESEARCH ARTICLES AND MEASURED
GUY ANALYTICS AS BEING AMONG THE
AWARDS TOP 1% OF HIGHLY CITED
SCIENTISTS IN 2018, 2019, 2022,
AND 2323.
SHE WAS ELECTED AS AAAS FELLOW
2015 AND FELLOW OF ROYAL SOCIETY
OF CANADA 2020.
SHERYL, PLEASE, THE STAGE IS
YOURS.
>> THANK YOU.
CAN EVERYBODY SEE AND HEAR?
>> YES,.
>> GREAT.
SO I'M VERY PLEASED TO TALK TO
YOU ABOUT THE WORK WE'RE DOING
ON DATA GENERATION IN SUPPORT OF
KOOSMUTATIONAL DRUG DISCOVERY.
SO THE SGC IS A PUBLIC
PARTNERSHIP OF DATA SCIENTISTS
AND A NUMBER OF PHARMACEUTICAL
COMPANIES AND LISTED BELOW HERE
AND ALL HAVE A COMMON INTEREST
AND PRETTY COMPETITIVE RELATED
TO RESEARCH DRUG DORPH.
SO IN THE CONTEXT OF
COMPUTATIONAL WORK AND THE POWER
OF ARTIFICIAL INTELIENCE AND
MACHINE LEARNING, I THINK THIS
YEAR'S NOBLE PRIZE IS IN PHYSICS
AND CHEMISTRY HIGHLIGHT THE
EXCITEMENT AND THE POWER OF
THESE METHODS.
IN PARTICULARLY THE AWARD IN
CHEMISTRY WAS AWARDING TO 50
YEAR-OLD CHALLENGE WHICH WAS
BASED ON THE DNA SEQUENCE OF
ENCODING A CHAIN AND PREDICTING
THE 3D STRUCTURE AND IT WAS IN
2021 THE GROUPS FROM DEEP MIND
AND DAVID BAKERS WORK IN PROTEIN
DESEEN SORT OF SIMULTANEOUSLY
SHOWED THAT USING PHYSICS BASED
AND AI DEEP LEARNING METHODS
THAT THIS COULD BE DONE.
SO, I THINK WE CAN TAKE SOME
LESSONS FROM THIS AND THIS IS
WHAT MY TALK, THE THEME OF MY
TALK WILL BE, SO WE THINK A
CHALLENGE IS GREAT AS THE
PROTEIN FOLDING PROBLEM IS THE
ABILITY TO PRODICT DRUG LIKE
COMPOUNDS THAT COMBINE TO END
MODULATE A GIVEN PROTEIN.
SO WE'RE DEFINITELY NOT THERE
YET.
THERE ARE SOME HINTS THAT EARLY
COMPUTATIONAL METHODS CAN
PREDICT SOME MOLECULES THAT BIND
TO I GIVEN TARGET BUT IT'S FAR
FROMENTIOUS SPECIALLY FOR
UNPRECEDENTED TARGETS THAT HAVE
NEVER BEEN LIGANDED BEFORE.
IT'S VERY FAR FROM SUCCESSFUL
RIGHT NOW.
SO HOW DO WE GET TO THIS POINT,
WE'RE AT THIS EARLY STAGE OF
DRUG DORPHY CAN BE LARGELY
COMPUTATIONAL AND TAKE SOME OF
THE BURDEN OFF OF THE EXPENSIVE
AND SLOW METHODS OF DRUG
DISCOVERY THAT HAVE TO BE DONE
IN THE LAB.
SO WE'RE GOING TO APPLY LESSONS
LEARNED FROM THE STRUCTURAL
BIOLOGY COMMUNITY THAT HELP TO
PROMOTE THE SUCCESS, I WILL TALK
ABOUT PILOT PROGGENTS THAT WE'VE
BEEN DOING TO ASSESS AI MACHINE
LEARNING ASSISTED PROTEIN
COMPOUND SCREENING MLGTS.
I'LL TALK ABOUT HOW TO ENABLE
THESE METHODS WITH OPEN CATAAND
PARTICULARLY HOW TO ARCHIVE,
STORE AND DISSEMINATE DATA AND
ALSO COMMUNITY BENCHMARK PRONG
ECTOMYOSINS WHICH ARE THE OTHER
-- PROJECTS WHICH ARE THE OTHER
KEY METHOD REQUIRED FOR KNOWING
WHEN YOU'VE BEEN SUCCESSFUL.
AND THEN IN THE END HOW THE
COMMUNITY CAN WORK TOWARDS THESE
GOALS MUCH SO WHAT DOES SUCCESS
LOOK LIKE, SO THE PROTEIN
FOLDING COMMUNITY KNEW THEY HAD
YOU KNOW METHODS THAT REALLY
WORKED EVENTUALLY AFTER 14
BIANNUAL COMMUNITY WIDE
COMPETITIONS CALLED CASP, THEY
FINALLY SHOWED THAT ALPHA FOLD 2
WAS VERY HIGHLY SUCCESSFUL IN
PREDICTING THE 3 DIMENSIONAL
FOLD OF THE PROTEIN.
AND IT WAS EXPERIMENTAL DAT AND
THE PROTEIN DATA BANK, THE PDB
AND TAKEN--THEY'S WHAT ERCH HAS
BEEN USING OVER MANY YEAR ANDS
FINALLY THERE WAS ENOUGH DATA,
THERE WAS ENOUGH 3 STRUCTURES IN
THE PDB ANDLET ABILITIES OF AI
COULD MAKE USE OF THAT EMPLOY SO
LET ME JUST GIVE YOU A LITTLE
BIT OF BACKGROUND ABOUT THE SGC,
AS OUR NAME SUGJEFFS STRUCTURAL
GENOMICS CONSORTIUM, WE STARTED
AS AN ORGANIZATIONS PIEZATION,
WE WERE AMONG THE LARNLEST
CONTRIBUTORS TO THE PDB FOR A
NUMBER OF YEARS AND ALSO
GREATEST SINGLE TRIBUTER TO THE
CASP COMPETITIONS WITH OVER A
HUNDRED PROTEINS IN THE MID2000S
EMPLOY AND AROUND 2010 OR SO, WE
STARTED USING THE PROTEINS THAT
WE MADE TO IDENTIFY BIOACTIVE
SMALL MOLECULES THAT BIND AND
MODULATE THESE PROTEINS WE CALL
THEM CHEMICAL PROBES AND
MORRECENTLY WE'VE STARTED TO
APPLY MACHINE LEARNING METHODS
TO TRY TO ACCELERATE THIS ENTIRE
PROCESS BECAUSE OF THE VALUE WE
RECOGNIZED OF CHEMICAL PROBES TO
THE RESEARCH COMMUNITY.
NOW ALL THIS WORK IS IN THE
PUBLIC DOMAIN, EVEN THOUGH WE
ARE CO FUNDED AND COLLABORATE
WITH A NUMBER OF PHARMACEUTICAL
COMPANIES AND SO THIS IS PRETTY
COMPETITIVE RESEARCH, AND WE'RE
ENDING UP OVER THE NEXT 10 YEARS
WE'RE AIMING TO DO A LOT MORE OF
THIS, WE CAN LEVERAGE MACHINE
LEARNING AND GLOBAL
COMPUTATIONAL COMMUNITY TO
PARTNER WITH THE DATA THAT WE
GENERATE, TO DEVELOP METHODS
THAT WILL ACCELERATE DRUG DORPHY
AND TO IDENTIFY A MODULATOR FOR
MOST HUMAN PROTEINS.
THAT'S THIS PROGEC CALLED TARGET
2035.
SO THE IDEAL MOLECULE WE WOULD
LIKE TO MAKE FOR EACH HUMAN
PROTEIN IS A PROBE OR SMALL LIKE
MOLECULE THAT SELECTIVELY
MODULATES THE ACTIVITY OF A
SPECIFIC PROTEIN.
THE CONCEPT IS PUBLISHED IN THIS
PAPER A NUMBER OF YEARS AGO,
THESE ARE HIGHLY, HIGHLY USEFUL
TOOLS, OFTEN THE TYPE OF TOOL 1
MAKES INITIALLY WHEN YOU ARE A
TARGET OF INTEREST TO INSURE
THAT PHARMACOLOGICAL INHIBITION
ARE NOW KNOCKED DOWN OR CHEMICAL
KNOCK DOWN WITH A PROTAC
DEGREATER GIVES YOU THE
PHENOTYPE THAT 1'S INTERESTED IN
EMPLOY SO WE MADE AND
COLLABORATED WITH THE
PHARMACEUTICAL INDUSTRY TO
COLLABORATE OVER 200 CHEMICAL
PROBES NOW.
AND IN THE LAST 10 YEARS AND SO
SOME OF THESE ARE PUBLISHED HERE
IN THESE PAPERINGS SO WE MADE A
NUMBER OF THEM OURSELVES, IN
COLLABORATION WITH BIG FARM ABIG
PHARMA DONATED A NUMBER OF
CHEMICAL PROBES, IT'S A NICE
KINASE, SOMEWHAT SELECTIVE SET
OF KINASE INHIBITORS WITH THE
GENOMICS SET SO THESE ARE ALL
PUBLICLY AVAILABLE COMPOUNDS AND
DATA THAT GOES WITH THEM.
AND THEY'VE BEEN USED BY
THOUSANDS OF PEOPLE AND
THOUSANDS OF PAPERS BY NOW.
SO NEVERTHELESS, IT'S STILL A
VERY SMALL NUMBER OF HUMAN
PROTEINS, AND SO THERE'S THIS
DARK PROTEOME, IF WE -- THIS IS
A NUMBER OF PUBLISHED PAPERS
GIVEN FOR EACH OF THE ROUGHLY
2000 HUMAN PROTEINS.
SO MOST OF THESE ARE
UNDERSTUDIED WITH NO
PUBLICATIONS ON THEM.
WE CAN PREDICT THEIR STRUCTURES
NOW BUT WE STILL DON'T KNOW WHAT
MANY OF THEM DO FUNCTIONALLY.
UNBIASED GENOMICS STUDIES SUCH
AS GWAS STUDIES, ET CETERA SHOW
MOST HUMAN PROTEINS HAVE SOME
KIND OF LINK TO A TEASE.
SO THERE IS A RATIONAL FOR
TRYING TO FIND A MODULATOR OR A
LIGAND TO MOST HUMAN PROTEINS IF
WE CAN.
AND THAT'S WHAT THE OBJECTIVE OF
THIS TARGET 2035 IS SHOWN HERE
SO ALL I WILL TALK ABOUT TODAY
IS WAYS WE CAN ACCELERATE
GETTING TO THIS POINT AND THESE
ARE JUST THE, AGAIN, THE USEFUL,
YOU KNOW THE GREAT NEWS IT WOULD
BE IF YOU HAD A MODULATOR OF
EVERY HUMAN PROTEIN MUCH THE
PROBLEM IS, THAT EACH CHEMICAL
PROBE A POTENT SELECTIVE IN CELL
MOLECULE COSTS ON THE ORDER OF 2
TO $5 MILLION EACH.
TAKES YEARS TO DEVELOP, AND WE
WILL JUST NEVER GET TO THE POINT
OF HAVING 1 FOR MOST HUMAN
PROTEINS, UNLESS WE LEVERAGED
THE POWER OF MACHINE LEARNING
AND NEW TECHNOLOGIES IN A
MASSIVE WAY AND DO THIS IN A
GLOBALLY GENERATED MANNER AND DO
THIS WITH ALL THE TECHNOLOGIES
TOGETHER.
THIS HAS BEEN COMMENT OFFICE OF
DIVERSITY IN A NUMBER OF PAPERS
RECENTLY ABOUT THE POTENTIAL OF
AI MACHINE LEARNING TO
ACCELERATE DRUG DISCOVERY,
HERE'S QUOTES WITH UNDERLYING
KEY PARTS THAT PARTICULARLY WE
ARE TRYING TO ADDRESS WHERE,
WHERE PREDICTIONS, COMPUTATIONAL
PREDICTIONS ARE MADE ABOUT
COMPOUNDS THAT ARE MADE TO A
TARGET NEED TO BE VALIDATED IN
AN EXPERIMENTAL SETTING,
SOMEBODY HAS TO DO THIS WHICH IS
AN EXPENSIVE UNDERTAKING AND A
LOT OF THIS, A LOT OF THE DRUG
DORPHY AND LIGAND DISCOVERY
TAKES PLACE IN INDUSTRY AND AS
WE JUST HEARD, THERE'S A LOT OF
KNOWLEDGE IN INDUSTRY, SO IF
INDUSTRY AND ACADEMIA CAN WORK
TOGETHER, I THINK WE WILL GET
THERE FASTER.
SO WE HAVE TOWARDS THAT GOAL,
WE'VE CARRIED OUT A NUMBER OF
RECENT PILOT PROJECTS ON
EXPERIMENT AL HIT FINDING.
THE IDEA IS TO FIND A
METHODOLOGY THAT WILL BE OF
GENERIC USE, IF YOU WILL, SO,
WE'VE FOCUSED ON THESE
BIOPHYSICAL BINDING ASSAYS AX
FINNITY AFFLICTION, AND WE'RE
TRYING TO GET AWAY FROM AND MORE
EXPENSIVE AND MORE TECHNICALLY
DEMANDING ASSAYS SUCH AS EXTRA
FRAGMENT SCREENING KLF-TWO WORKS
AND IS QUITE USEFUL BUT REQUIRES
SPECIAL SET UP AND MATERIALS,
AND HITS ARE VERY WEAK OR YOU
KNOW SPECIFIC FUNCTIONAL ASSAYS
FOR EXAMPLE WERE PLANT BASED
METHODS THAT REQUIRE ALSO A LOT
OF TWEAKING AND MAYBE DIFFERENT
FOR DIFFERENT PROTEINS.
SO THE METHODS SHOWN HERE, I
WILL WALK THROUGH BRIEFLY, EACH
OF THESE, WE'RE COLLABORATING
WITH INDUSTRY TO DEVELOP AND
DEMONSTRATE AND APPLIES TO APPLY
THEM AT SCALE ON A LOT OF
PROTEINS OVER THE NEXT 5-10
YEARS.
SO PILOT 1 IS DNA ENCODED
LIBRARY WITH SELECTION AND
MACHINE LEARNING ANALYSIS THAT
WOULD ENABLE 1 TO PREDICT
COMMERCIALLY AVAILABLE
COMPOUNDS, SO, THAT 1 COULD
PURCHASE THAT ARE PREDICTED TO
BIND THE PROTEIN OF INTEREST AS
OPPOSE TO SINGICIZING EXACTLY
THE MOLECULE OFF DNA THAT WAS
SELECTED BY THE PROTEIN BECAUSE
THE LATTER IS FAIRLY EXPENSIVE
AND RESOURCE INTENSIVE.
WE APPLIED THIS TO THIS LARGE
PROTEIN FAMILY CALLED WD40
REPEAT PROTEINS, THESE ARE
PROTEIN-PROTEIN INTERACTION
DOMAINS AND IN THE CENTER OF
THIS DENDRITIC CELL RAGRAM IS A
STRUCTURE OF 1 WDR PROTEIN THAT
ARE DATA PROTELLER PROTEINS
OFTEN BINDING A PEPTIDE LIGAND
OF ANOTHER PROTEIN IN THE
CENTRAL POCKET HERE.
WE CLONED EXPRESSED PURIFIED,
SOLVED A NUMBER OF STRUCTURES AS
SHOWN ON HERE AND THEN WE TOOK
16 OF THESE WDR PROTEINS AND 2
NONWDR PROTEINS SO THAT OUR
METHOD WAS NOT -- SO WE KNOW OUR
METHOD WAS NOT BIASED TOWARDS
JUST THIS 1 PROTEIN FAMILY.
WE COLLABORATED WITH X-SCN OR
THE LIBRARY IN THE FIELD AND
THEY DID LIBRARY SELECTIONS, WE
THEN COLLABORATED WITH COMPANY
CALLED ZEBI-AI AT THE TIME TO
USE MACHINE LEARNING TO ANALYZE
THE DATA, THE SELECTION DATA AND
PREDICT COMPOUNDS THAT WOULD
BIND TO OUR 18 TARGETS.
WE TESTED THOSE 18 TARGETS USING
A VARIETY OF BIOPHYSICAL METHODS
SHOWN HERE, EACH WAS TESTED BY
SURFACE PLASMA AND RESONANCE SO
THE SPR AND IN A SECOND, TO CALL
A COMPOUND, CONFIRM THE COMPOUND
IS NOT A BINDER, WE WANTED 2
BIOPHYSICAL METHODS TO BE SURE
IT WAS REALLY BINDING.
SO HERE'S THE RESULTS.
AT LEAST A THIRD OF THE TARGETS
SHOWING THE TARGETS IN BIND.
A COUPLE OTHER WEAKER BINDERS
AND OTHER PROTEINS DID NOT
RETURN A VALIDATED BINDER.
BUT, ALTHOUGH THESE WERE WEAKISH
BINDERS IN THE MICROMOLAR RANGE,
THEY'RE ALL NEW ADDRESSABLE
COMPOUNDS AND IT WILL BE IN THE
PAPER COMING OUT IN THE NEXT
JAMA AND IN THE BI ONY ARCHIVE.
SO ENCOURAGED BY THE SUCCESS LED
BY RAFAEL [INDISCERNIBLE] AT SBC
AT UNC CHAPEL HILL IS LEADING
OUR DEVELOPMENT OF AN OPEN
DELL ML COLLABORATIVE NETWORK IN
WHICH WE'RE DOING DELATWAL
SELECTIONS AND CORRELATING THE
DATA IN THE FORM OF FINGERPRINT
SELECTED MOLECULES AND IN A
DATABASE CALLED ARCHECK, I WILL
MENTION IN A MOMENT, BUT THIS
WILL BE AVAILABLE FOR ANYONE TO
DO MACHINE LEARNING AND PREDICT
COMPOUNDS THAT BIND TO THE
PROTEIN OF INTEREST AND ALSO
SHARE THESE MODELS FOR THE
COMMUNITY TO USE, AND SO, SOME
OF THE GROUPS THAT ARE DOING THE
DELL SCREENING ARE SHOWN ALONG
THE BOTTOM HERE.
SO THAT WILL BE GENERATING
PROTEIN SMALL MOLECULE
INTERACTION DATA FROM DELL ML,
THE SECOND PILOT WAS, AFFINITY
SELECTION MASS SPECTROMETRY,
EXPERIMENTS THAT ARE LARGELY
USED IN INDUSTRY, BUT NOT VERY
MUCH IN THE ACADEMIA.
OUR COLLEAGUE HUI PENG IS AN
EXPERT IN MASS SPECTROMETRY AT
THE UNIVERSITY OF TORONTO AND
USED IT IN THE FIELD OF SCIENCE
AND COLLABORATING THEY LEVON
HALABELIAN, AT TORONTO, THEY
WORKED TOGETHER TO BUILT THIS TO
BE ABLE TO SCREEN PROTEINS IN A
NUMBER OF DAYS AGAINST A NUMBER
OF COMPOUNDS.
WE'VE BUILT SEVERAL LIBRARIES, A
LARGE LIBRARY FROM
[INDISCERNIBLE] DIVERSITY
LIBRARY AND SMALLER LIBRARIES
FOR JUST SORT OF LIGAND --
ASSESSING LIGAND ABILITY OF
PROTEINS FOR EXAMPLE.
NOW INTERESTING THING ABOUT THIS
METHOD IS IT CAN BE -- IF THE --
IF THE ISOLATION OF THE COMPOUND
THAT BINDS TO YOUR PROTEIN OF
INTEREST IS DONE THROUGH A
COLUMN, WE CAN SLEKD AN ANTIMDR
BIEN XG THAT WAS QUITE
INTERESTING AND THESE ARE
EXAMPLES FROM KNOWN PROTEINS AND
COMPOUND PAIRS THAT WE IMU ONLY
1 WOULD BIND TO THE OTHER.
SO THIS WAS VERY EXCITING AS
WELL, AND IT TURNS OUT THAT IT
SEEMS THAT WHEN 1 -- WHEN YOU
GET JUST 1 PEAK OUT OF A
[INDISCERNIBLE] THAT WAS
SCREENED WITH THE PROTEIN, THAT
THERE'S A HIGHER PERCENTAGE OF
THAT COMPOUND VALIDATING OF
COURSE THIS IS A KREEN SO NOT
EVERY COMPOUND VALIDATES.
WE APPLIED THIS TO OVER 30
TARGETS SHOWN HERE WITH THE
VARIABLE LIGAND ABILITY SCORES
AS SHOWN, SOME OF THE STATISTICS
ARE SHOWN AT THE BOTTOM AND
ABOUT ALMOST A THIRD TO A HALF
OF THESE GAVE HITS AND MANY OF
THOSE WERE VALIDATED BY
BIOPHYSICAL METHODS.
SO HOW DO WE SCORE, SCALE,
DISSEMINATE THESE LARGE DATA SET
ANDS HOW DO WE MAKE THEM
ACCESSIBILITY TO THE COMMUNITY
TO USE.
SO BENJAMIN CANES IS A SCIENTIST
AT THE UNIVERSITY OF TR RONT O
AND HE THINKS A LOT ABOUT THIS
AREA.
WE WANT TO MAKE THE DATA
AVAILABLE TO EVERYONE AND WE
WANT PEOPLE TO USE IT AND THEN
GIVE BACK THEIR MODELS TO THE
COMMUNITY TO USE, AND SO, BENIA
MIN CAME UP WITH THE CONCEPT OF
ARTIFICIAL INTELLIGENCE READY
CHEMICAL KNOWLEDGE BASE OR AIR
CHECK, THIS IS A DATABASE WHOSE
PROTOTYPE IS HOSTED AT GOOGLE
CLOUD,OOSE AVAILABLE WITH NO
RESTRICTIONS AND THE USE OF THE
DATA IF YOU AGREEN CELLS TO,
JUST A FEW BASIC POINTS, THE
DATA IS FORMATTED FOR DATA
SCIENTISTS WHICH IS THE KEY
THING.
SO THE DATA IS UNIFORM, ALWAYS
STORED IN THE SAME WAY AND TELL
BE PRIMARILY TO TYPES OF DATA AT
LEAST IN THE BEGINNING TO
DELL ML AND DELL SELECTIONS AND
ASMS DAILY BASIS THEA AND WE
HIGHLY ENCOURAGE, DON'T REQUIRE
BUT WE HIGHLY ENCOURAGE, USERS
TO MAKE THEIR MACHINE LEARNING
MODELS USING THIS DATA OPEN
SOURCE AND TO EVEN DEPOSIT IT IN
THE DATABASE.
SO, THE AIR CHECK TEAM IS IN THE
LOWER LEFT HERE, THIS A SCREEN
SHOT OF THE FROM THE WEBPAGE AND
ALEX'S GROUP FROM UNC HAS USED A
NUMBER OF THESE DATA SETS TO
SUCCESSFULLY CREATE AN ML MODEL
THAT PREDICTS A COMPOUND THAT IS
VALIDATED TO BIND, TO TARGET A
COUPLE TARGETS OF INTEREST, AND
SOME OF THE TARGETS ARE SHOWN
HERE AND THIS IS KIND OF
ENCOURAGING THAT WE CAN TRY TO
FOSTER THIS ACTIVITY IN A SORT
OF GLOBAL NETWORK AMONGST THE
COMMUNITY.
THE THIRD PILOT, WAS 1 OF PURELY
COMPUTATIONAL METHODS.
WE WERE ASKED BY COMPANIES EARLY
ON WHETHER WE COULD TEST THEIR
PREDICS OF COMPOUNDS THAT WERE
BOUND TO A GIVEN PROTEIN, WE
WORK WITH ADAM WISE AND SICLICA,
AND AGAIN ABOUT HALF OR A THIRD
OF THE CASES THEY IEE, AUDIENCE
DENTIFIED COMPOUNDS THAT CONFIRM
OUR PROTEIN OF INTEREST.
NOW THESE HITS ARE WEAK, THEY'RE
QUITE WEAK IN FACT, BUT THEY
WERE MEASURABLE.
AND THEY WERE ALSO MORE POTENT
THAN FRAGMENTS TEND TO BE AND
THEY WERE PROGRESSABLE AND WERE
FIRST IN CLASS FOR THEIR TARGETS
SO THIS ALSO LED TO THE, YOU
KNOW, IT'S ENCOURAGING, I THINK,
IT WAS ENCOURAGING FOR THE FIELD
AS I SAID IN THE BEGENERATEDDING
WE'RE STILL NOT THERE, AND THERE
IS A LOT OF HYPE IN THE FIELD
ABOUT AI AND MACHINE LEARNING
AND DRUG DISCOVERY, SO HOW DO WE
KNOW WHO TO WORK WITH IN THIS
AREA AND HOW DO WE KNOW WHEN OUR
METHODS OR COLLEAGUES METHODS
ARE REALLY WORKING.
AND FROM THE CASP AND PROTEIN
FOLDING FIELD, THAT IS A
BENCHMARKING EXERCISES, SO FROM
THE PROTEIN TRUCTURE PREDICTION,
BENCHMARKING EXERCISE CALLED
CASPS, CRITICAL ASSESSMENT OF
STRUCTURE PREDICTION, WE MODELS
AND MATT [INDISCERNIBLE] IN OUR
GROUP COLLABORATING WITH JOHN
[INDISCERNIBLE] WHO HAS
DEVELOPED AND RUN THE CASP
COMPETITION FOR MANY, MANY
YEARS, THEY WORK TOGETHER TO
CREATE THE CACHE CHALLENGE WHICH
IS A PROTECTIVE CHALLENGE IN
WHICH A TARGET IS ANNOUNCED, THE
COMPUTATIONAL COMMUNITY MAKES
PREDICTIONS AND IS ABLE TO
PREDICT UP TO A HUNDRED
COMPOUNDS AND THEN WE WILL TEST
THEM EXPERIMENTALLY AND SEE
WHICH METHOD HAS WORKED THE
BEST.
SO THERE'S 6 CACHE COMPETITIONS
SO FAR.
TWO HAVE COMPLETED AND THE DATA
IS PUBLICLY RELEASED, THAT WAS
THE 2 PARKINSON'S TARGET AND
SARS-COV-2 XP13 HELIX CASE, AND
YOU CAN FIND THOSE ON THE
WEBSITE AND A NUMBER ARE UNDER
WAY, WE'RE -- THE COMPOUNDS ARE
BEING TESTED OR PREDICTIONS ARE
BEING CARRIED OUT RIGHT NOW.
SO THIS IS A SYSTEMATIC
SELF-CONSISTENT ANALYSIS FOR
EACH PROTEIN.
AND THE -- IT'S A LOT OF
EXPERIMENTAL VALIDATION NEEDED
FOR THIS BUT WE THINK THAT IT'S
USEFUL TO MONITOR OVER TIME HOW
WELL THE COMPUTATIONAL COMMUNITY
IS GOING TO BE ABLE TO PREDICT
COMPOUNDS THAT BIND.
SO I WILL END IT NOW WITH JUST
AN OPEN CALL TO JOIN US IN THIS
EFFORT.
TARGET 2035 IS MEANT TO BE A
GLOBAL COLLABORATIVE EFFORT,
PUBLIC-PRIVATE PARTNERSHIP, IT
IS AN OPEN SCIENCE PARTNERSHIP,
AND WE ENCOURAGE ANYONE WHO WAS
INTERESTED TO REACH OUT.
SO 1 KEY AREA IS AN OPEN CALL
FOR PROTEIN TARGETS,
SPECIFICALLY THE PHYSICAL
RECOMBIN ANT PROTEIN THAT IS IN
A GOOD STATE AND IF GROUPS CAN
PROVIDE US WITH PROTEINS, WE
WILL DO THE SCREENS BY ASMS OR
DELL-ML, WE WILL PUT THE DATA IN
AIR CHECK AND WE WILL VALIDATE
OR CONFIRM WHETHER OUR PREDICTED
HITS ARE ACTUALLY BINDING TO THE
PROTEIN OF INTEREST AND THE
RESULTS, THE ONLY CAVEAT PERHAPS
IF YOU VIEW IT AS A CAVEAT IS
ALL THE RESULTS WILL GO IN THE
PUBLIC DOMAIN WITH NO
RESTRICTIONS ON THEIR USE AND IF
YOU HAVE A PROTEIN, YOU WOULD
LIKE PUT THROUGH THIS PROCESS,
NICOLE BURGESSBROWN WHO'S ENAIL
IS HERE CAN COORDINATE THAT.
AND IN AN AREA WHERE WE WOULD
LOVE TO HAVE A COLLABORATORS AND
AND PARTICIPANTS IS CALLED MAIN
FRAME, AND WE ARE WORKING ON
DISCOVERING MANY PROTEINS THAT
ARE IN THE DARK PROTEOME, A
NETWORK OF MACHINE SCIENTISTS,
WE WILL BE RUNNING BENCHMARKING
CHALLENGES AND EXPERIMENTALLY
TEST COMPOUNDS, THAT ARE
PREDICTED FOR FREE AND TOP LABS
THAT ARE SUCCESSFUL WILL BE
INVITED TO DEDICATED CONFERENCE
TO DISCUSS THIS, AND AGAIN, THE
CONTACTS HERE ARE
[INDISCERNIBLE], AND MATTHIEU,
AND ALEX, AND I WANT TO END HERE
WITH FUTURE DISCOVERY AND WHAT
WE THINK THEY LOOK LIKE EMPLOY
TARGETS WE THINK THE WHOLE HUMAN
PROTEOME IS A GOOD TARGET AND WE
WANT TO SWITCH FROM THE BURDEN
OF HAVING TO DO ALL THE INITIAL
PROTEINS, SMALL MOLECULE
INTERACTION WORK IN EXPERIMENTAL
METHODS TO A LARGELY INSILICO
METHOD AND WE THINK THAT BY
HAVING ENOUGH DATA AND
BENCHMARKING TO DEVELOP MACHINE
LEARNING METHODS, WE WILL
ACCELERATE THAT, AND THEN I
THINK NEXT WE'RE GOING TO HEAR
ABOUT THESE EXCITING AREA OF YOU
KNOW AUTOMATION AND FURTHER
DEVELOPMENT OF COMPOUNDS FOR
DRUG DISCOVERY, GUIDED BY AI IS
AUTOMATION AND WE THINK THAT
WILL ALSO BE IMPORTANT, THE
ACCELERATION CONSORTIUM HERE IN
TORONTO HAS A LOT OF ACTIVITY IN
THAT AREA.
AND THEN, YOU KNOW A PUBLIC
DOMAIN TOOL FOR EACH HUMAN
PROTEIN COULD THEN LEAD WHOEVER
TO MAKE THE DRUGS THAT WE NEED
FROM IT, SO MANY UNMET HUMAN
CONDITIONS.
SO I WOULD LIKE TO THANK, ALL MY
COLLEAGUES AT THE SBC TR RONT O
AND ALSO THE OTHER SBC SITES AND
IN PARTICULAR WHAT I SPOKE ABOUT
TODAY ARE PARTICULARLY
VALIDATING PREDICTED HITS AND
SGC TR RONT O ARE THE FOLKS
LISTED HERE ON THE LEFT AND THE
[INDISCERNIBLE], CACHE, AND THE
FOLKS ARE LISTED HERE AND THE
ASMS PLATFORM AND THE FUNDERS.
HAPPY TO TAKE QUESTIONS.
>> GREAT PRESENTATION.
COUPLE MINUTES LEFT FOR THE
QUESTION, SO LET ME ASK FROM THE
CHART, SO WE HAVE 1 SITUATION,
YES, TYPICALLY THE POOLS FOR THE
ABILITY LIMITS THE EFFECTIVE
CONCENTRATION OF EACH COMPOUND.
WHAT IS THE POST SIZE FOR YOUR
ASMS WORK.
I BELIEVE THEY ARE USING POOLS
OFIFY HUNDRED COMPOUNDS.
>> 500.
>> ALL RIGHT.
SO WHILE PEOPLE TYPING THE
QUESTION, LET ME ASK 1.
YOU MENTIONED THE DEFINITION OF
THE CHEMICAL PROBLEM ON THE
SLIDE WHICH IS GREAT BUT LIKE
FROM YOUR EXPERIENCE, WHAT WOULD
LIKE AND FROM YOUR OPINION, I
WOULD SAY WHAT WOULD YOU
CONSIDER THE REQUIREMENTS TO BE
A PROBE, IN VIVO ACTIVITY, CELL
ACTIVITY, AND TARGET ENGAGEMENT
AND WHAT THE FINISH WILL GIVE.
>> IN THE FIRST INSTANCE, CELL
ACTIVITY, DEFINITELY TARGET
ENGAGEMENT, AND WE DO MEASURE
THAT, BEFORE WE CALL SOMETHING A
CHEMICAL PROBE, WE SHOW THAT IT
GETS INTO THE CELL ENGAGING THE
TARGET, DOES NOT HAVE
UNINHIBITORS TENDED OFF TARGET
CENTER FOR EXCELLENCE ON AGINGS,
SO WE TESTED AGAINST A NUMBER OF
CELL LINES AND MAKE SURE IT'S
NOT JUST A TOXIC COMPOUND AND BY
O PHYSICALLY CHARACTERIZE THAT
THE COMPOUND INTERACTS WITH THE
PROTEIN OF INTEREST USUALLY WITH
A RECOMBIN ANT PROTEIN AS WELL.
>> GOT IT AND VERY LAST
QUESTION, VERY QUICK, YOU
MENTIONED THE NAME CODE
ELABORATE, WHAT IS YOUR
EXPERIENCE IN REPRODUCE ABILITY
OF THE [INDISCERNIBLE] LAB
RAKERY RESULS.
>> REPRODUCIBILITY?
>> YES, SO HOW IS IT
REPRODUCIBLE BECAUSE I THINK WE
TOUCH BASE PARTLY AS THE
EFFORTS, SO IF YOU HAVE ANY
INSIGHT CAN YOU COMMENT ON
THAT,.
>> SO IF YOU DO THE SAME ASSAY
TWICE.
>> YES, BASICALLY IF YOU RUN IT
A COUPLE TIMES, AND THE
OFFSEASON, THE ACTIVITY AND
WHAT'S THE KEY RATE?
>> YES, SO, DON'T ACTUALLY -- I
DON'T ACTUALLY KNOW THE ANSWER
TO THAT QUESTION IN THAT CASE
BECAUSE WE'RE NOT DOING THE OFF
DNA, RIGHT IN SO, WE'RE DODGING
THAT ISSUE, AND GOING STRAIGHT
TO A MACHINE LEARNING MODEL FROM
THE SELECTION DATA AND SAY WHAT
COULD WE BY THAT LOOKS LIKE IT
WOULD BIND BASED ON THE
COMPOUNDS THAT BOUND WITH DNA
ATTACHED TO THEM AND IT'S NOT A
HUNDRED PERCENT, I MEAN AND WE
DON'T KNOW HOW MUCH OF IT IS THE
MODELS WHICH ARE STILL BEING
DEVELOPED AND IMPROVED ON, OR
WHETHER THERE'S SOMETHING, YOU
KNOW THERE'S SOME ISSUE WITH THE
DNA BEING THERE OR THAT MAY ALSO
CONFOUND THANK YOU SO MUCH 1
MORE TIME ONCE AGAIN, THAT'S A
GREAT PRESENTATION AND LET ME,
MOVE FORWARD TO OUR NEXT
SPEAKER, LET ME INTRODUCE
MILAD ABOLHASANI, AND HE IS WITH
THE UNIVERSITY FACULTY SCHOLAR
IN THE DEPARTMENT OF CHEMICAL
AND BIOMOLECULAR ENGINEERING IN
AT NORTH CAROLINA STATE
UNIVERSITY.
HE RECEIVED HIS Ph.D. FROM THE
UNIVERSITY OF TORONTO IN 2014,
AT NCITATE UNIVERSITY HE LEADS A
DIVERSE RESEARCH GROUP FOR THE
STUDY OF SELF-PRIMING COLLABS,
HE RECEIVED ENORMOUS AMOUNT OF
THE AWARDS INCLUDING THE AWARD,
IACL 3555, [INDISCERNIBLE] AWARD
FROM MACHINE LEARNING IN
CHEMICAL SCIENCE ENGINEERING AND
MANY HOURS.
SO MILID, THE STAGE IS YOURS.
>> THANK YOU LEXY FOR THE KIND
INTRODUCTION AND THANK YOU
ORGANIZERS FOR THE INVITATION TO
TALK ABOUT SOME OF THE WORK I'VE
BEEN DOING IN MY LAB FOR THE
PAST 7, 8 YEARS TO ACTUALLY WAY
THE APPROACH AND READING HUMAN
KNOWLEDGE, AI AND ROBOTICS, SO
WE ARE LOOKING FOR A NEW
MATERIAL MOLECULE, NO LONGER
DEALING WITH THIS UNIVERSE, WE
WILL HAVE A MAP OF THE UNIVERSE,
WE KNOW THE TARGET PROPERS OF
THE MATERIAL WE'RE LOOKING FOR,
WE DON'T KNOW HOW TO PFAFFIGATE
IT, OR FIND IT, WE DON'T EVEN
KNOW WHAT THAT MOLECULE IS, SO
MOST OF THE TIME WE'RE JUST
BLINDLY SEARCHING THROUGH THIS
SPACE WITH THE AEROSPACE
TECHNIQUE, SO THAT'S 50% OF THE
REASON WHY, THE MOLECULAR DORPHY
EFFORTS ARE [INDISCERNIBLE]
EXPENSIVE.
THE OTHER 50% OF THE PROBLEM IS
THE WAY WE NAVIGATE THIS
CHEMICAL UNIVERSE, THIS IS THE
WAY THE COMMERCIAL APPROACH
[INDISCERNIBLE] SCIENCES HOW YOU
TRAIN SCIENTISTS IN THE LAB,
THEY SPEND 70-80% OF THE THEIR
TIME DOING TASKS, PIPETTING,
READING SAMPLES, RUNNING
REACTIONS AND CHARACTERIZING
MAYBE 10 OR 20 SAMPLES A DAY, AT
THE END OF THE DAY ISSUE THEY GO
BACK TO THEIR OFFICE AND LOOK
THEA THE DATA AND USE THEIR
INTUITION TO DECIDE WHAT IS THE
NEXT EXPERIMENT TO RUN, SO THIS
IS OUR CONVENTIONAL CLOSED LOOP
SCIENCE AND IT'S VERY SLOW AND
RESOURCE INTENSIVE.
NOW IMAGINE, DEALING WITH HIGH
DIMENSIONAL PROBLEMS, 10, 20
DIFFERENT PARAMETERS YOU'RE
DEALING WITH, THE APPROACH THAT
WE TYPICALLY TAKE IS 1 PARAMETER
AT A TIME CAN 1 EXPERIMENT AT A
TIME.
SO THIS IS THE OTHER 50% OF THE
PROBLEM, MAIN SYMMETRY, IS OF
COURSE IS VERY, VERY EXPENSE AND
I HAVE SLOW.
SO LOOKINGA THE THIS PICTURE
THERE'S A LOT OF ROOM FOR
IMPROVEMENT, FOR EXAMPLE, WE CAN
LEVERAGE THE ADVANCEMENTS IN
ROBOTICS AND LAB AUTOMATION TO
BRING OUR HUMAN SCIENTISTS OUT
OF THE LAB, NOT OUT OF THE LOOP
OF EXPERIMENTAL SCIENCES, AND
THEN, HAVE THIS END-TO-END
AUTOMATED EXPERIMENTAL WORK
FLOW, INCREASE THROUGH PUT AND
REPRODUCIBILITY AND RELIABILITY
OF EXPERIMENTS AND THEN USE
RECENT ADVANCE WANTS IN MACHINE
LEARNING AND AI TO BETTER GUIDE
THE DECISION MAKING PROGRESS,
BETTER SAMPLE THE INFINITE
CHEMICAL UNIVERSE AND HAVE MORE
INFORMED SAMPLING OF THAT
REACTION IN SPACE.
SO WE INTEGRATE ROBOTICS
AUTOMATION WITH AN AI AGENT, YOU
CREATE WHAT WE CALL A
SELF-THRIVING LAB.
SO NOW WHAT YOU GET OUT OF THE
SELF-DRIVING LAB, IS
ACCELERATION, CONVENTIONAL STAT
EDGYS AND WE CALL THIS A ROBOTIC
CO PILOT, LIKE THE AI CO PILOT,
IT'S ROBOTIC CO PILOT, BRIDGES
THE GAP BETWEEN THE PHYSICAL
WORK.
BUT MOST LABS ARE MOST
SUSTAINABLE, SO REDUCE YOUR
FOOTPRINT OF THE DISCOVERY AND
IF YOU REDUCE YOUR PROCESSES,
CAN YOU REDUCE THAT TIME FROM
LAB TO MARKET.
SO DEPENDING ON HOW YOU
INTEGRATE HUMAN KNOWLEDGE OF
THESE ROBOTICS AND AUTOMATION,
IT CAN HAVE DIFFERENT HUMAN AI
ROBOT COLLABORATIONS IN YOUR
EXPERIMENTAL DISCOVERY WORK
FLOW, YOU CAN START FROM THE
PEACEFUL OPERATION WITH THE NEW
IRNLT VENTION AND HYPOTHESIS AND
EVOLVES, OVER TIME AND WHERE WE
ARE NOW, I'M GOING TO TALK ABOUT
TODAY IS THIS CLUES OF
AUTONOMOUS LABS THAT THE HUMAN
SCIENTIST IS STILL THE DRIVER
AND THE SELF-DRIVING LAB IS THE
CO PILOT THAT BRIDGES THE GAP
BETWEEN THE DIGITAL WORLD AND
THE PHYSICAL WORLD.
SO THE DIGITAL WORLD IS YOUR
CHEMICAL FORMATSICS AND THAT YOU
CAN DO INSILICO SCREENING AND
MAYBE OPTIMIZATION BUT THAT
INFORMATION IS STILL UNDER
DIGITAL AWARD.
SO THIS THE SELF-DRIVING LAB
TAKES THAT AND TESTS THAT IN A
CLOSED LOOP FORMAT, AND VERIFYS
THE HYPOTHESIS AND MAYBE REJECTS
IT OR COMES UP WITH A NEW
HYPOTHESIS AND ONCE YOU GATE AT
THE END OF THE DAY THERE'S A LOT
OF BENEFITS WITH THE ROBOTIC
PART AND REDUCE TIME SOLUTION
BUT HUNDRED TO 1000 TIME GOING
FROM 10 PLUS YEARS AND WITH THE
MOLECULAR DISCOVERY DOWN TO A
FEW MONTH ANDS GOING FROM A
HUNDRED MILLION DOLLARS TO LESS
THAN A MILLION DOLLARS DOWN FOR
YOUR DISCOVERY AND THEN WITH THE
ROBOTIC CO PILOT, YOU CAN HAVE
THE PRECURSOR FORMULATION.
SO JUST DOING WHAT WE'VE DON
DOING FOR THE PAST CENTURY USING
FLAS AND HAVING A LOT OF LACE IN
THE CHEMICALS WE ARE USING THIS
IS AN OPPORTUNITY TO THINK ABOUT
[INDISCERNIBLE], CHEMICAL
COMSUMPTION TRYING TO REENGINEER
DESIGN OF OUR AUTOMATION
PLATFORMS FOR THE MARKER DORPHY
WITH THIS PERSPECTIVE OF
NEUTRALIZING THE CHEMICAL
COMSUMPTION SO WE CAN MINIMIZE
THE [INDISCERNIBLE] AH
TONMATIONS.
NOT EVERY AUTOMATION SOLUTION IS
PRECISE.
WE HAVE TO MAKE SURE THE DATA WE
ARE GENERATING THROUGH THIS
AUTOMATION TECHNOLOGIES ARE
PRECISELY REPRODUCIBLE BECAUSE
THAT'S THE DATA WE WILL PROVIDE
TO THE AI AGENT.
THE AI AGENTS TRAIN UNDER THE
DATA YOU ARE GENERATING IN THE
LAB AND IF YOU HAVE NOISY DATA,
INSTEAD OF ACCELERATING SCIENCE,
YOU END UP DECELERATING YOUR
CONSCIENCE AND YOU HAVE TO THINK
ABOUT WHAT ARE THE MOST
APPROPRIATE TECHNIQUES THAT
PROVIDE THROUGH PUT THAT IS
NEEDED TO PROVIDE DAILY BASIS
THEA FOR THAT AI AGENT.
AND AT THE END OF THE DAY, WE
WHAT WE WANT TO HAVE IS THE
RELIABLE EXPERIMENTAL DATA AND
WE HAVE TO THINK ABOUT HOW DO WE
ACTUALLY TRANSLATE KNOWLEDGE
BETWEEN DIFFERENT MODALITIES OF
RUNNING REACTIONS, DIFFERENT
TYPES OF HARDWARE, DIFFERENT
ROBOTICS, YOU CAN RUN THE SAME
ORGANIC SYNTHESIS USING
[INDISCERNIBLE] FLASK OR
[INDISCERNIBLE], OR NEUTRALIZED
BATCH REACTORS.
NOW DEPENDING ON THE ENVIRONMENT
REAC, YOU MIGHT GET DIFFERENT
RESULTS, SO HOW DO YOU AUGMENT
THESE DATA, FROM 1 LAB TO
ANOTHER 1.
SO WE COME UP WITH LANGUAGE SO
THEY CAN TRANSFER KNOWLEDGE AND
AUGMENT DATA AND ACCESS
DISCOVERIES AND ALL THOSE THINGS
TO CONSIDER WHEN WE THINK ABOUT
THESE WHEN DESIGNING AN
AUTONOMOUS LAB.
SO THINK ABOUT THIS FOR DATA IN
MY LAB, WHICH IS THE GOLD
STANDARD TO USE, THE FLOW
REACTORS BASICALLY MICROFLUIDIC
REACTOR, IT'S BEEN AROUND
FOREMORTHAN 30 YEARS BUT
BASICALLY CONTINUOUSLY FLOWING
THE PRECOURSORS OF YOUR
REACTIONS AND THE REACTIONS ARE
HAPPENING AT A VERY SMALL
CHANNEL AND THE REACTION HAPPENS
FOR A SPECIFIC REACTION TIME AND
AT A SPECIFIC TEMPERATURE AND
THATIA A TIME THAT YOU'RE
REGIONS ARE SPENDING IN A VERY
SMALL MICROCHANNEL THAT THEY'RE
FLOWING.
AND 1 OF THE ADVANTAGES, THAT
YOU HAVE, SO, IN BASICALLY A
SIMPLE AWORDS WE CAN NEUTRALIZE
REACTIONS WHEN YOU RUN THEM IN A
FLUID REACTOR YOU CAN ACCESS
KINETICS OF A REACTION, SO IF A
REACTION TAKES HOURS OR DAYS IN
A BATCH REACTOR AND HEAT
TRANSMITTED FLUID, IT RUN THE
SAME IN A MANNER OF MINUTES IN A
MICROFLUIDIC REACTOR.
SO IT RUN HUNDREDS OF REACTIONS
AT 3 OR 4 ORDINANCE NUMBERERS OF
MAGNITUDE IN A BATCH REACTOR AND
THEN WE CAN PRECISELY AUTOMATE
THE REACTION, CONTROL ALL THE
REACTION CONDITIONS AND
INTEGRATE DIFFERENT ONLINE
CHARACTERIZATION THROUGH
CHARACTERIZE THE COMPOUND THAT
LEAVES THE REACTOR.
SO THESE ARE SOME OF THE MOST
FLUIDIC ADVANTAGES WHEN WE WERE
TALKING ABOUT THE PREVIOUS SLIDE
FOR THE HL MA MAPS.
AND THIS MAY BE HALF A
MICROLITER UP TO 1 OR 2 MICROLIT
EARS.
AND YOU HAVE A AUTOFORMAT WE
CALL A SINGLE DROPLET, MULTIFAYE
I FLOW IN SIMPLE WORD WE HAVE 1
LIQUID DROPLET OR PLUG AND WE
ARE OSCILLATING THIS BACK AND
FORTH, IT'S LIKE SHAKING A VILE
AND IN THIS CASE YOU CAN HAVE A
SMALL REACTIVE FOOTPRINT, THEY
CAN HAVE A DROPLET OF 2-4 AND
RUN REACTIONS IN THE ORDER OF
SECONDS, DAYS OR HOURS OR MAYBE
EVEN MONTHS SO IF YOU'RE DEALING
RELATIVELY SMOA CLEM CHAL
REACTION, WE TYPICALLY USE THE
SINGLE DROPLET FORM AT IF WE ARE
DOLING WITH RELATIVELY FAST
REACTIONS OR DO CONTINUOUS
MANUFACTURING WE USE THE
CONTINUOUS MULTIFACE FLOW.
SO IN MY LAB, THEY DEVELOP THIS
BESPOKE FLOW REACTORS AND USE
ROBOTICS.
WE USE LIQUID [INDISCERNIBLE]
ROBOTS, AND THE SOLUTIONS OF THE
REACTIONS AND THEN THE DEVELOPER
ON BESPOKE ONLINE
CHARACTERIZATION TOOLS,
CHROMEATOGRAPHYS AND MASS SPEC
AND THEN USING A AI EAMENT TO
KRIEF THIS TO THE EXPERIMENTAL
WORK FLOW, BUT ON A THE
HIGHLIGHT HERE IS THE
SELF-DRIVING LAB IS NOT
REPLACING THE HUMAN SCIENTIST IN
THE LAB, IT IS ACTING AS A
ROBOTIC CO PILOT FOR THE HUMAN
SCIENTISTS AND AUTOSTILL
PROVIDING THE FRAME FOR THE
ACCESS EMPLOY AND WHEN WOE USE
THIS SELF-DRIVING LAB FOR THE
TECHNOLOGY, MY LAB APPLIED 3
DIFFERENT RESEARCHIARYS, 1 IS
NOW SHY, AND THE THIRD RESEARCH
TRANSFER WE USE THE DATA MACHINE
LEARNING MODELS WE DEVELOP IN
THE AREAS TO BETTER UNDERSTAND A
FUNDAMENTAL MECHANISMS OF THE
REACTIONS AND PROCESSES
HAPPENING AND FOR EXAMPLE, WE
CAN INSTALL FORMATION OR THE
TRANSITION MINNIAL REACTIONS AND
THE CATALYSIS AREA THAT WE'RE
STUDYING.
SO FOR THIS PRESENTATION, I WILL
FOCUS ON THE CATALIAISONS AREA,
THIS IS HOW WE CAN USE ROBOTICS
TO ACCELERATE OUR EXPERIMENTAL
FORK FLOW, SO HERE AS A CUSTOM
BUILT ROBOTIC SOLUTION INSIDE A
ENVIRONMENT, CAN AUTOMATICALLY
REDO SOLID AND FLUIDIS PENSING,
ORGANIC REACTIONS FOR EXAMPLE,
CCAND CROSS [INDISCERNIBLE]
REACTIONS THAT CAN ACTIVATE
REAGENTS AND STARTING PRECURSOR
ANDS START RUNNING THE INITIAL
DISCREET, SCREENING OF THE
DIFFERENT PARAMETERS, DIFFERENT
[INDISCERNIBLE] AND GIVING--YOU
LAND SOLVENTS AND BASE.
AND THEN THEY CAN BASICALLY
SELECT OR FIND THE HIT
CONDITIONS AND THEN RUN THOSE IN
OUR FLOW REACTORS FOR FINE TUNE
OPTIMIZATION OF THE REACTION
CONDITIONS.
AND THEN ONCE THIS IS FINISHED
WE USE MOBILE AND STATIONARY
ROBOTIC ARMS TO TRANSFER SAMPLES
TO DIFFERENT STATIONS FOR DOWN
STREAM PROCESSING FOR EXAMPLE,
IF YOU DO ANY SORT OF BI ONY
ASSAYS THEY CAN DO THE TRANSFER
FOR BIOASSAY STATIONS SO WE CAN
HAVE THESE 24/7 OPERATION OF OUR
SELF-GRINDING LABS, BUT AS I
MENTIONED WE USE FLOW REACTORS
TO RUN THE SYNTHESIS SO WE CAN
DO HUNDREDS OR THOUSANDS OF
DIFFERENT REACTIONS FOR DATA
THAT'S JUST 2 EXAMPLE, WE USE OF
DROPLETS IN A VERY SMALL FLOW
REACTORS, AND THEN THE DIAMETER
OF TUBE HERE IS 750-MICRONS, AND
THE REACTION CAN HAPPEN
TYPICALLY IS AN ORDER OF HALF A
MICROLITER, AND AS I SAID WE CAN
DO UP TO A THOUSAND REACTIONS OR
ORGAGIC SYNTHESIS A DAY EMPLOY
SO WE ARE IN THE WORKING
SELF-DRIVING FIELDS FOR THE PAST
7 OR 8 YEARS AND THE EXAMPLES OF
THESE LABS WE DEVELOP BOTH FOR
INORGANIC MATERIALS AND FOR
ORGANIC SYNTHESIS.
SO I WILL TALK ABOUT CATALYSIS,
AND ORGANIC TRANSFORMATIONS IS
HOW EFFECTIVE IS THE LIGAND
YOU'RE USING IN OUR ORGANIC SING
SIS, FOR EXAMPLE, COUPLING,
FORMULATIONS OR ORGAN C
COUPLING.
SO WHEN YOU HAVE A LIGAND USING
CATALYSIS FOR EXAMPLE, YOU CAN
SELECT THE YIELD THE REACTION.
SO TYPICALLY WHAT CHEMISTS DO,
THEY HAVE A LIST OF CONDITIONS
THEY RUN SO THEY DO THE MANUEL
SCREENING, OR THE EXTERNAL DAT
THAT ARE WITHIN THE BOUNDARIES
OF OF THE SILICO IN YIELD, OR
THE BEST SOURCE OF THE LIGAND
YOU'RE TESTING AND THEN COMPARE
THE OPTIMAL POINTS THAT YOU
CAN'T IMPROVE THE YIELD OR
SELECTIVITY OF THE YIELD OR
LIGAND WITHOUT A SPECIFIC POINT
WITHOUT COMPROMISING THE OTHER 1
BUT WHAT YOU WANT TO HAVE IS A
METHOD THAT CAN GIVE YOU THIS
VERY TARGETED SELECTION OF
EXPERIMENTS THAT YOU CAN
MINIMIZE YOUR CHEMICAL
COMSUMPTION IS IN VERY SHORT
AMOUNT OF TIME SHOW THIS OPTIMAL
POINTS FOR A GIVEN LIGAND OR
CALLOUS, YOU SO KULAK AT THE
DORPHY FOR THE SYNTHESIS.
SO I WILL SHOW YOU IN THE
FIXTURE FEW SLIDES HOW WE DESIGN
THE SELF-DRIVING LAB THAT CAN
AUTONOMOUSLY TEST PERFORMNESS
FOR THE LIGAND AND USE THAT
INFORMATION TO FURTHER ACCESS
DISCOVERY OF NEW LIGANDS FOR
ORGANIC SENTH SIS.
SO 1 PART OF THE SELF-DRIVING
LAB IS THE HARDWARE, AND I WILL
TALK ABOUT HARD REACTION, SO
FIRST PART IS HUMAN USER, THEN
AI GUIDED EXPERIMENTAL PLANNING
AND ROBOTIC RECONFIGURURATION
AND THEN ONLINE DATABASE, AND DO
SPRPTS IN ORDER TO INTELLIGENTEE
NAVIGATE THAT CHEMICAL THAT I
WAS TALKING ABOUT AT THE
BEGENERATEDDING OF MY SLIDES
MUCH SO IN TERMS OF REACTION, WE
USE 2 DIFFERENT TYPES OF
REACTORS, SINGLE DROPLET REACTOR
BUT THESE ARE MUTEERALLIZED
REACTORS THAT THEY ARE RUN
REACTIONS IN THIS HALF AIAN
MICROLITER AND THEN WE CAN
REDUCE THE CHEMICAL COMSUMPTION,
WITH THE KINETICS AT 5-10 TIMES
AND RUN REACTIONS UP TO 1000
ORGANIC SYNTHESIS A DAY.
SWE CAN RUN HIGH TEMPERATURE,
HIGH PRESSURE REACTIONS RELIABLY
IN THESE PLATFORMS AND WHAT
WE'VE DONE IN THE PAST WE CAN
SHOW ASK USE THESE PLATFORMS TO
RUN A RANGE OF DIFFERENT ORGANIC
SYNTHESIS FROM THIS TO COUPLING
AND SiRNA REACTIONS FOR THE
KACCTAL SIS AND CROSS SUPT G
REACTIONS AND THIS SHOWS THE
DIVERSITY OF DIFFERENT REACTIONS
ORGANIC 60 SIS THAT YOU CAN DO
THESE FLOW CHEMIST RADIOY
PLATFORMS.
SO HERE'S AN EXAMPLE OF THIS
FULLY AUTOMATED FRONTAL PLATFORM
USING ROBOTICS FOR LIQUID
HANDLING OF THE LEFT, AND
RUNNING THE ACTUAL REACTION, AND
DOING ANALYSIS ON THE RIGHT WITH
THE ONLINE ELFSD MASS SPEC TO
CHARACTERIZE THIS REACTION.
AND HERE'S HOW YOU CAN USE THIS
FOR THE PREPARATION AND THEN
THIS IS WHERE YOU RUN THE
REACTION IS IN A FEW
MICROLIGHTERS CAN YOU DO GAS OR
LIQUID REACTION, SINGLE PHASE OR
MULTIPACE REACS.
IT CAN ALSO COUPLE THIS REACTOR
WITH A DIFFERENT BAN GAPS FOR
THE TRANSFORMATION AND ONCE YOU
OPERATING GLOBALLY MYSELF
REACTION, USING AUTONOMOUS
ACTION, CAN YOU SCALE UP GOING
FROM SINGLE RAPID CONTINUOUS
FLOW AND ACKNOWLEDGES DIRECTLY
TRANSFERABLE, IT'S A SCALABLE
KNOWLEDGE THAT YOU DISCOVER AND
YOU DON'T NEED TO DO ANY
ADDITIONAL OPTIMIZATION, YOU IT
CAN JUST DIRECT THE TRANSFER THE
LIAISON IMAND CALLOUS AND THE
REACTION TO THE DISCOVER TO THE
FLOW REACTOR AND CAN PRODUCE THE
TARGET COMPOUND WITH THE OPTIMAL
CONDITIONS WE'VE DISCOVERED, SO
NOW THAT WAS THE HARDWARE
REACTION NEUTRALIZATION SITE OF
THE CELLS DRIVING LABS, THE
SECOND PART IS HOW YOU ACTUALLY
MAKE A DECISION WHILE
CONTRIBUTING TO THE EXPERIMENTAL
RUN, THIS IS WHERE WE DO A LOT
OF PROCESS OPTIMIZATION TO MAKE
SURE WE HAVE THE MOST OPTIMAL
FINE TUNE AI AGENT FOR
SELF-DRIVING LAB SO IN THIS CASE
I'M SHOWING THE FRAMEWORK WHERE
I WANTED TO IMPROVE, THE PAIR TO
OPTIMAL POINTS FOR THE FRONT OF
SELECTIVITY AND YIELD OF A
SPECIFIC CHEMICAL REACTION, IN
THIS CASE OUT OF WHOM YOU HAVE
ALL DIFFERENT FOSTER NURSED
FOCUSED ON LIGANDS TO PRODUCE
THE HIGHS WHICH ARE THE STARTING
CHEMICALS IF ARE A LOT OF
ALCOHOLS AND OTHER APIs.
SO IN THIS CASE, BASICALLY
THEY'RE USING HYPER VOLUME
IMPROVE AM OR EXPECT HYPER
VOLUME IMPROVEMENT AND YOU START
YOUR REACTION WITH
INITIALIZATION OF SOME EITHER
RANDOM SELECTED EXPERIMENTS OR
DESIGNING EXPERIMENTAL METHODS
TO USE THE VISUALIZATION OF YOUR
OPTIMIZATION AND THEN YOU CAN
MODEL THE FUTURE EXPERIMENTS AND
ROUTE THEM BASED ON EXPECTED
HYPER VOLUME IMPROVEMENT WHICH
IS BASICALLY IN SIMPLE TERMS
WHAT IS THE PROBABILITY OF
RUNNING THE EXPERIMENT IN THE
FUTURE TO IMPROVE THE YIELD AND
SELECTIVITY OF THIS REACTION IN
AND THEN WE SELECTED TOP RANK
EXPERIMENTS, TO THE HARDWARE
OURSELVES TO RUN THAT REACTION
AND UPDATE THE SURROGATE MODAND
HE WILL RUN THROUGH THIS LOOP
UNTIL THEY CANNOT FURTHER
IMPROVE THAT PAIR TO OPTIMAL
POINT.
NOW IN ORDER TO FINE TUNE THE AI
AGENT OF THE SELF-DRIVING LAB,
WE NEED A LOT OF MODELING HERE
TO MAKE SURE THE BRAIN OF THE
SELF-DRIVING LAB IS FINE TUNED
AND IT'S THE MOST OPTIMAL
MODELING AND THE DECISION MAKING
ON THE ANSWER THERE SO THAT WE
CAN FIND THE MAP OR PERFORMANCE
OF THE GIVEN LIGAND IN THE
SHORTEST AMOUNT OF TIME
POSSIBLE, MINIMIZING
EXPERIMENTAL COST OF LIGAND AND
CALLOUS DORPHY.
SO I'M NOT GOING TO GO THROUGH
ALL THE DETAILS OF RESULTS I'M
SHOWING HERE BUT WE LOOKED AT
DEFINITE SURROGATE MODELING
ARCHITECTURES AND WE LOOK AT
DIFFERENT DECISION MAKING
POLICIES AND WE DID MORE THAN
6000 SIMULATIONS TO FINE TUNE
THE LIGANDS, AND AFTER THAT WE
ARE FINE TUNING THE AGENT WITH
THE FULLY AUTOMATED PLATFORM
THAT TARTS WITH AUTOMATICALLY
HANDLING YOUR SOLUTIONS AND THEN
WE BASICALLY PERFORMED
REACTION SAYS AND HALF THE
MILLILITER SCALE VOLUME. THIS
IS AGAIN REACTION OF MAKING ALDA
HIDESSA HIDES THAT IT CAN USE
AGAIN TO USE TO FORM AND MAKE
DIFFERENT AND DIFFERENT ALCOHOLS
AND APIs.
AND THEN THE PRODUCT OF THE
AUTOMATICALLY ANALYZED ONLINE TO
UPDATE THE SURROGATE MODEL OF
THE AI AGENTS SO IT COULD SELECT
THE NEXT EXPERIMENT TO
AUTONOMOUSLY MAP THE PERFORMANCE
OF THE SPECIFIC LIGAND OR
CALLOUS GIVEN THIS REACTION.
SO HERE'S A PICTURE OF THE
ACTUAL EXPERIMENTAL PLATFORM,
DIFFERENT THEREFOREY PUMPS
THERE, THE EMBASSY MANIFOLD FLOW
REACTOR AND THE ONLINE ANALYSIS
AND IN THIS CASE, IT'S GCMS, BUT
WE CAN DO THE SAME THING WITH
THE FETAL COMPARTMENT MASS, SPEC
NA, AND ONLINE TMR, AND
INTEGRATED WITH THE SELF-DRIVING
MAPS.
HERE'S AN EXAMPLE PERFORMANCE OF
THE SELF-DRIVING CATALYSIS LAB
SHOWING IT CAN MAP THE SPACE FOR
THE LIGAND FOR THIS
HOMODPEENIOUS ORGANIC SYNTHESIS
AND WITHIN 30 AUTONOMOUSLY
PERFORMED PRPLTS OUR
SELF-RUNNING LAB COULD MAP THE
PERFORMANCE OF THIS LIGAND FOR
THE GIVEN REACTION, AND IF YOU
ARE GOING TO DO SAME THING, WITH
THE HIDE HIGHLY TRAINED CHEMIST,
THAT WILL TAKE YOU ABOUT 9-12
MONTHS AND THEN WE DID THIS FOR
MULTIPLE DIFFERENT LIGANDS AND
THEN, THE TRANSFER FOCUSED ON
KNOWLEDGE IS THE SELF-DRIVING
LAB IN HALF A MIL PLOA REACTOR
TO LARGER KALE BATCH REACTOR,
WITH OUR INDUSTRIAL PARTNER TO
SHOW THE KNOWLEDGE OF SCHEME AND
ABILITY TO COMES OUT OF THIS
SELF-DRIVING LAB, AND WE SHOWED
THAT THE -- IF YOU USE THE SAME
LIGAND, THAT YOU DISCOVERED IN
YOUR SELF-DRIVING LAB WITH THE
SAME REACTION CONDITIONS AND
RIGHT IN THE LARGEST SCALE BATCH
REACTOR, YOU GET THE SAME
SELECTIVITY AND YIELD, SO THAT
KNOWLEDGE AND SCALABILITY WAS
VERY IMPORTANT FOR US, WE SPENT
ABOUT A YEAR AND A HALF TO MAKE
SURE THAT THE KNOWLEDGE WE
DORPHED IS THE SMALLER SCALE
REACTOR IN THE SELF-DRIVING LAB
IS SCALABLE AND DIRECTLY
TRANSFORRABLE TO OUR TREAL
PARTNER.
AND THEN YOU CAN DO A LOT OF
THINGS WITH THE DATA YOU
GENERATE WITH THE SELF-DRIVING
LAB, HERE'S A DIGITAL TWIN OF
THE ACTUAL HOMOGENIUS CATALYTIC
REACTION WITH DIFFERENT LIGANDS
YOU CAN TRAIN A GP MODEL FOR
EVERY LIGAND THAT YOU HAVE
TESTED AND THEN YOU CAN USE THAT
DIMINISHITTAL TWIN TO VISUALIZE
THE REACTION SURFACE TO STUDY
THE EFFECT OF DEFINITE
PARAMETERS OF THE DIFFERENCE OF
THE REACTIONS, THIS IS A GREAT
TOOL FOR BETTER UNDERSTANDING OF
WHAT'S GOING ON IN THE SPECIFIC
REACTION YOU'RE STUDYING.
AND THEN GOING BACK TO THE FLOW
REACTOR, I MENTIONED IT CAN GO
FROM SINGLE DROP CONTINUOUS
FLOW, WE BASICALLY USE THE
OPTIMIZED CONDITIONS TO SHOW
THAT WE CAN DO THIS ON DEMAND SO
WE CHANGE UP SELECTIVITY OF A
REACTION, USING THE CONDITIONS
OF THE DISCOVERY FOR
SELF-DRIVING LAB, THEY COULD ON
DEMAND, ON THE FLY CHANGE THE
REACTION CONDITIONS WITH THE
SAME LIGAND TO CHANGE THE FDA,
OF THE HYPE THAT YOU'RE
PRODUCING IN THIS FLOW REACTOR
AND LATER ON WE WILL ALSO USE
THIS METHOD TO SHOW THAT WE CAN
USELESS DO A HIGH MUTEILATION IN
THE FLOW FOR ALPHA BRANCH MEANS
AND WE SHOW WIDE OUR AUTONOMOUS
LAB WE CAN DISCOVER THE CALLOUS
AND THE CO CALLOUS TO ACCELERATE
THE [INDISCERNIBLE] 70 TIMES
COMPARE TO THE BEST BATCH
REACTIONS BEFORE.
SO THIS IS EXAMPLES OF WHAT YOU
CAN DO WITH THE LAB BUT WHAT IS
THE FUTURE OF AUTONOMOUS LABS SO
WHAT YOU'RE WORKING ON RIGHT NOW
AND NATIONAL LAB PARTNERS AND ON
A VISION AND WE CAN HAVE THIS
NETWORK OF CONNECTED AUTONOMOUS
LABS PERFORMING SIMILAR
CAMPAIGNS, SHARING DATA OVER THE
CLOUD AND FURTHER ACCELERATING
DISCOVERY OF NEW MOLECULES OF
CALLOUS AND MATERIALS.
BUT IN ORDER TO DO THIS AND
IMPLEMENT THIS VISION, WE NEED
TO WORK ON THE REPRODUCIBILITY,
THE KNOWLEDGE TRANSFER, AND THE
DATA AUGMENTATION WITHIN
DIFFERENT ROBOTS.
SO HERE'S AN EXAMPLE OF THE
DIFFERENT LANGUAGE OF DIFFERENT
ROBOTS, LIKE USING BATCH
REACTIVE, LOW REACTIVE TYPES OF
DEFINITE ARTICLE TECHNIQUES SO
HOW DO WE AUGMENT THIS
HETEROGENEOUS SOURCE OF DATA AND
HARREGARD, YOU NEED TO WORK ON
THE EXTRACTION, TOO MUCH
ABNORMALITIES INSTRUCTION, TOO
LITTLE ABSTRACTION IS NOT GOOD,
YOU HAVE TO DO A LOT OF
DEFINITIONS OF DIFFERENT DETAILS
OF THESE AUTONOMOUS OPERATION
AND THAT'S SOMETHING WE ARE
WORKING ON AND DEFINITELY NEEDED
TO IMPLEMENT THIS, AT THE
DIVISION OF THESE AUTONOMOUS
LABS.
VERY IMPORTANT THING WE HAVE TO
ALWAYS THINK ABOUT IS WHEN WE
DESIGN A SELF-DRIVING LAB, YOU
HAVE TO PERFORM A LOT OF BUNCH
MARKING TESTS BOTH ON THE
HARDWARE AND SOFTWARE SIDE OF
THIS LAB SO MAKE SURE THAT WE
CAN BASICALLY HAVE HAD APPLE TO
APPLE COMPARISON AND THE
PERFORMANCE OF THESE AUTONOMOUS
LABS FROM THE CHEMICAL
COMSUMPTION TO THE INILLEGALSIC
O OPTIMIZATION MODELS THAT
THEY'RE PERFORMING.
AND THEN, I WILL END BY
MENTIONING THAT WE NEED TO WORK
ON THIS KNOWLEDGE TRANSFER
BETWEEN DIFFERENT TYPES OF
ROBOTS AND AUTONOMOUS LABS,
THAT'S THE ONLY WAY WE CAN
REALLY DIGITALIZE AND THE
DISCOVERY OF NEW MOLECULES,
CALLOUS CAN PROCESSES, IF WE CAN
NOW TRANSFER KNOWLEDGE AND
PROTOCOLS, RECIPES, AND DATA
BETWEEN DIFFERENT LABS OTHER
EVERYTHING WE DO, ONLY LIMITED
TO OUR OWN IN-HOUSE DATA THAT WE
ARE GENERATING WITH OUR OWN
ROBOTS.
SO AT THE END I HOPE I WAS ABLE
TO CONVINCE YOU THAT THESE
AUTONOMOUS LABS ARE GREAT
ROBOTIC CO PILOTS TO HELP US
CLOSE AUTONOMOUS SCALE KNAP IN
MOLECULAR DISCOVERY, POTENTIALLY
100 TO THOUSAND TIME COMFORT TO
THE CONVENTIONAL WAYS TYPICALLY
APPROACH MOLECULAR AND CALLOUS
DISCOVERY.
AT THE END I WANT TO THANK MAY
GROUP MEMBER WHO IS DID ALL THE
WORK I PRESENTED TODAY.
AND I WANT TO THANK OUR FUNDING
SOURCES, MY COLLABORATORS, WE
WILL THANK YOU ALL FOR LISTENING
AND I WILL BE HAPPY TO TAKE ANY
QUESTIONS.
>> GREAT.
THANK YOU SO MUCH.
SO GREAT, SO FASCINATING.
THERE ARE A COUPLE QUESTIONS IN
THE CHAT.
LET ME ASK THE FIRST 1, YEAH
THAT'S INTERESTING, HOW DO YOU
MAKE SURE WHAT THE CELL
[INDISCERNIBLE] LAB DOES NOT
TURN INTO THE SKY NET?
>> THAT'S A QUESTION OF
CYBERSECURITY OF THESE
AUTONOMOUS LABS AND THERE'S
ALWAYS A LOT OF ACTIVITIES AND
ACTIVE PROGRAMS WORKING ON THE
CYBER INFRASTRUCTURE, CYBER
INFRASTRUCTURE OF THESE
AUTONOMOUS LABS EVEN AT THE
UNIVERSITY LEVEL, WE HAVE A LOT
OF INTERNAL PROTOCOLS ON HOW TO
EVEN REMOTELY OPERATE THIS
AUTONOMOUS LABS AND THEN THERE
ARE A LOT OF THINGS YOU HAVE TO
CONSIDER WHEN YOU ARE WORKING ON
INDUSTRY SPONSORED PROJECTS, HOW
DO YOU MAKE SURE THAT YOUR DATA
IS SECURE, SAFE OR KIND OF
SERVERS, YOU USE, BUT THE
ANSWER, I CANNOT ANSWER LIKE IN
THE NEXT 2 MINUES BUT THE ANSWER
IS THE ROBUST CYBER
INFRASTRUCTURE AND A LOT OF
FUNDING SOURCES, FEDERAL FUNDING
SOURCES IN THE U.S. ARE ACTUALLY
COMING UP WITH NEW PROGRAMS ON
PROMOTING THE CYBER
INFRASTRUCTURE FOR
EXPERIMENTATION AND CLOUD LABS
AND AUTONOMOUS LABS.
>> GREAT.
THEN OUR QUESTION IS, THE
MICROFLUID APPROACH IS GREAT TO
APPLY IN THE CHEMISTRY REACTION,
HOW WOULD YOU APPLY THIS TO YOUR
ENZYME REACTIONS OR BIOLOGICAL
REACTION FOR THE DRUG DORPHY.
DO YOU NEED TO BUILD DIFFERENT
WORK FLOW TO EACH DIFFERENT
REACTION.
NIS THAT'S A GREAT QUESTION.
SO IF YOU DON'T START EVERY
SINGLE REACTION IN FLOW, WE
START -- I BRIEFLY MENTIONED
THAT.
WE START WITH AN INITIAL
[INDISCERNIBLE] KREENING SO IF
YOU HAVE A REACTION THAT INVOFS
SOLID PRECIPITATION OR SOLID ARK
DEGZ, WE WILL RUN IT IN FLOW.
BUT IF THERE'S A REACTION, WE
KNOW THERE'S NO PRECIPITATION
AND THERE'S NO SOLID ADDITION,
WE RUN THAT REACTION IN FLOW, SO
WE DON'T FORCE THE REACTIONS IN
FLOW, BUT WHEN THE REACTION IS
ONLY SOLUTION PHASE, THERE'S NO
PRECIPITATION CHALLENGE AND
THERE'S NO SOLID EDITION DURING
THE REACTION AND IT'S ALL
SOLUTION PHASED AND RUN THE
REACTION IN FLOW, NOW IN TERMS
OF THE DESIGN OF THE REACTOR,
THE FLOW REACTORS HAVE EVOLVED
SIGNIFICANTLY IN THE LAST 20
YEARS NOW..
WE ARE AT A LEVEL FOR THE FLOW
REACTORS THAT WE CAN USE ORGANIC
AND INORGANIC SYNTHESIS WITHOUT
THE NEED TO REDESIGN THE WORK
FLOW, WE CAN USE THE SAME
DESIGN, SAME PLATES, MOST OF
THOSE ARE TEFLON TUBING AND IT'S
THE DESIGN OF THE HEATING PLATE
THAT WE TYPICALLY CHANGE FOR
DIFFERENT REACTIONS, SO THE
HEATING PLATE IS REUSABLE AND
TUBES ARE VERY EXPENSIVE TO
CHANGE.
>> LAST QUESTION, AND IF YOU CAN
ANSWER SOME QUESTIONS IN THE
CHAT, THAT WOULD BE GREAT, BUT
THE LAST QUESTION, QUICK 1, SO
WHEN WE SHOULD EXPECT THE CELL
DRIVING LABS WILL BE FUNCTIONAL
COMLITELY IN WHO IS YOUR
FORECAST, IT'S WORKING TODAY, WE
CAN RUN OR IT'S LIKE 5 DAYS,
FROM NOW, WHAT IS YOUR LIKE
EXPECTATION?
WHAT YOU WANT TO DESIGN?
>> THE WHOLE FIELD OF
SELF-DRIVING LAB IS IN THE
EARLY, EARLY STAGES AND THERE
ARE A LOT OF DIFFERENT VERSIONS
IN HOW TO EVOLVE THESE
AUTONOMOUS LABS.
THERE'S A VISION THAT WE JUST
NEED ROBOTS AND YOU CAN GO IN
AND USE EVERYTHING IN AN
EXISTING LAB TO DO HA AND
THERE'S THE OTHER VISION THAT
YOU NEED NEW TOOLS, NEW HARDWARE
AND NEW AUTOMATION TECHNOLOGIES
TO INCREASE EFFICIENCY AND
REDUCE CHEMICAL COMSUMPTION OF
YOUR REACTION, SO I WOULD SAY
MAYBE THERE'S A HANDFUL OF FULLY
ATON MOUSE LABS FULLY
OPERATIONAL AROUND THE WORLD AND
IT'S GOING TO PROBABLY TAKE
ANOTHER DECADE TO SEE GOING FROM
HANDFUL TO HUNDREDS AROUND THE
WORLD.
SO WE'RE STILL IN THE EARLY DAYS
OF THESE AUTONOMOUS LABS.
>> GOT IT.
THANK YOU SO MUCH FOR GREAT TALK
AND PRESENTATION.
WITH THAT SAID LET ME MOVE
FORWARD TO OUR LAST SPEAKER IN
THE SESSION.
DAVID BAKER, LET ME BRUCE HIM SO
HE'S THE DIRECTOR OF INSTITUTE
FOR THE PROTEIN DESIGN AND
HOWARD HUGHES INVESTIGATOR,
PROFESSOR OF BIOCHEMIST RADIOY
AND PROFESSOR OF GENOMIC
SCIENCE, BIOENGINEERING,
COMPUTER SCIENCE AND PHYSICS AT
THE UNIVERSITY OF WASHINGTON.
HIS RESEARCH GROUP IS FOCUSED ON
DESIGN OF MICROMOLECULAR PAPERS,
HE'S BEEN GRANTED OVER A HUNDRED
PATENTS AND CONFOUNDER OF 21
COMPANIES.
DAVID BAKER AND RECIPIENT OF OF
THE BREAK THROUGH PRIZE IN THE
LIFE SCIENCE AS A MEMBER OF THE
NATIONAL AKAH DEEMIA OF SCIENCE
AND ART AND SCIENCE AND AS YOU
MAY KNOW, HE'S RECENTLY GOTTEN A
NOBEL PRIZE ON BEHALF OF ALL
ORGANIZATIONING COMMITTEE
MEMBERS AND ALL OF US HERE AT
NCATS WE CONGRATULATE YOU ON
RECEIVING THE NOBEL PRIZE.
IT'S HUGE HONOR FOR US TO HAVE
YOU HERE AND PLEASE, THE STAGE
IS YOURS.
>> I DON'T THINK WE CAN HEAR
YOU.
>> CAN YOU SAY --
>> CAN YOU HEAR ME NOW?
>> OKAY, YEAH, WELL, THANK YOU
FOR THE INTRODUCTION AND LET ME
JUST TRY AND SHARE MY SLIDES.
LET'S SEE, HERE WE GO.
LET'S SEE SLIDE SHOW.
CAN YOU SEE MY SLIDES.
>> YES NPERFECT.
THANK YOU NI WILL NOT TALK ABOUT
SMALL MOLECULE DESIGN BUT ABOUT
PROTEIN DESIGN, BUT THERE'S MAUL
PROTEINS SO IT'S CLOSE.
SO THE LAST FEW YEARS WE HAVE
BEEN FOCUSING ON DEVELOPING NEW
DEEP LEARNING BASED METHODS FOR
PROTEIN DESIGN, SO GIIVE A
PROBLEM WE WANT TO SOLVE, WE
START BY SPECIFY THE PROBLEM WE
WANT TO SOLVE AND THEN IMENERATE
PROTEIN BACKBONES THAT SOLVE
THAT PROBLEM USING RF DIFFUSION
THEN WE,A SIGN SEQUENCES TO
THOSE PROTEINS USING PROTEIN
MPNN, AND THEN WEE CHECK TO SEE
IF THEY ENCODE THOSE STRUCTURES
BY USING ROSETTA FOLD OR ALPHA
FOLD.
AND IF THEY DO, TO MAKE THE
PROTEINS WE HAVE TO MAKE SIN
SETTHETIC GENES AND THEN WE PUT
THEM INTO BACTERIA OR YEAST
ANDEE IF THEY WORK.
SO EVERYTHING I WILL TELL YOU
ABOUT STARTS WITH A CALCULATION
THAT WE MAKE A SYNTHETIC GENE,
MAKE THE PROTEIN AND TEST IT.
SO I WILL SAY JUST A FEW WORDS
ABOUT HOW RF DIFFUSION WORKS, SO
IT'S VERY SIMILAR TO METHODS
LIKE, DOLLY FOR GENERATING
IMAGES WHERE YOU TAKE MANY, MANY
DIFFERENT IMAGES, YOU NOISE THEM
TO DIFFERENT EXTENTS, YOU TRAIN
A NETWORK TO REMOVE THE NOISE
AND THEN ONCE THAT'S BEEN DONE,
CAN YOU START WITH RANDOMINIZE
AND DENOISE IT TO GENERATE
IMAGES, IN THE SAME WAY WE TOOK
ALL THE STRUCTURALLY ARES IN THE
PROTEIN DATA BANK, NOISED THEM
TO DIFFERENT EXTENTS, TRAINED
DURING RF DIFFUSION TO MOVE THE
NOISE AND THEN WE TART
COMPLETELY RANDOMINIZE AND
REMOVE THE NOISE AND GENERATE
PROTEINS STRUCTURES THAT LOOK
LIKE THEY WERE IN THE PDU BUT
THEY'RE ACTUALLY NOT.
OOPS, LET'S SEE.
SO I JUST TRIED A COUPLE
DIFFERENT APPLICATIONS OF THIS,
THE FIRST IS DESIGNING BINDERS.
SO WHAT WE CAN DO IS TAKE THE
TARGET, THE INSULIN RECEPTOR AND
CARRY OUT THIS DIFFUSION PROCESS
AND WHAT IS DOES IS IT DPENERATE
ACE BACKBONE THAT CONFORMS TO
THE SURFACE OF THE TARGET AS CAN
YOU SEE HERE, SO AS THE NOISING
HAPPENS, IT'S BEEN TRAINED ON
PROTEIN-PROTEIN COMPLEXES SO IT
KNOWS WHAT THEY SHOULD LOOK LIKE
AND YOU CAN SEE THE STRUCTURE
FITS NICELY AGAINST THE TARGET,
SO THIS APPLYING THIS TO THE TNF
RECEPTOR, YOU KNOW A MAJOR DRUG
TARGET FOR INFLAMMATORY DISEASE,
AND THE GEARS, YOU SEE THE
DIFFUSION PROCESS, IT GENERATES
A STRUCTURE WHICH IS VERY, VERY
SHAPED COMPLIMENTARY TO THE
RECEPTOR, AND AFTER -- SO THIS
IS AFTER TAKING THE BEST
DESIGNS, REDOING THE -- ADDING
NOISE, DENOISING, TESTING
ANOTHER SMALL HANDFUL OF
DESIGNS, WE GET DOWN TO 8 PICK O
MOLAR AND WE HAVE NEAT PROPERS
IN ANIMALS, THEY GIVE COMPLETE
PROTECTION AGAINST LPS INDUCED
INFLAMMATION, THEY'RE ACTUALLY
MORE EFFECTIVE THAN ENBREL WHICH
IS PRETTY COOL BECAUSE THEY'RE
NOT BLOCKING THE TNF RECEPTOR 2,
WHICH IS ANTIINFLAMMATORY, AND
WE CAN TAKE THESE COMPOUNDS AND
THESE DESIGNS AND DO THIS
PARTIAL DIFFUSION METHOD TO
GENERATE BINDERS TO OTHER DEATH
RECEPTOR FAMILY MEMBERS AND WE
CAN ALSO CONVERT THESE INTO
QUITE SELECTIVE AGONISTS BY
BRINGING -- THAT BRING TOGETHER
MULTIPLE COPIES OF THE RECEPTOR.
WE'VE BEEN MAKING BIEBDERS FOR A
NUMBER OF YEARS AS I'VE SPOKEN
WITH MANY PEOPLE AT THE NIH
ABOUT THIS AGAINST THE MAJOR
DIFFERENT PANDEMIC FAMILIES FOR
EXAMPLE, INFLUENZA, CORONA
VIRUS, WE HAVE A COMPOUND MAKING
ITS WAY TO THE CLINIC THERE,
MERSYSTEM, WE HAVE VERY POTENT
NEUTRALIZER VIRUSES, NIPA AND
HINDRA, I HAVE TALKED ABOUT THAT
BEFORE AND I WILL NOT TALK ABOUT
IT TODAY, BUT MORE RECENTLY
WE'VE BEEN BINDING THEM TO
NCI--AND VENOM TOXINS AND
SUZANNEA TORRES HAS MADE THESE
THAT NEUTRALIZE THE COMPONENTS
OF SNAKE VENOM AND GIVE
PROTECTION IN ANIMALS WHEN
ADMINISTERED, EVEN WHEN
ADMINISTERED SUBSTANTIALLY AFTER
THE TOXIN.
SO 1 THINK THIS I DIDN'T SAY IS
THAT DESIGN PROTEINS ARE SUPER
STABLE AND HAVE REALLY LONG
SHELF LIFE SO FOR APPLICATIONS
WHERE YOU KNOW YOU NEED SAY AN
ANTIVENOM THAT, CAN LAST FOR A
LONG TIME, THESE COULD BE VERY
GOOD.
THESE ARE BINDERS AGAINST TOXIN
TCSL THAT CAUSES TOXIC SHOCK AND
THESE AGAIN ARE EXTREMELY POTENT
AND VERY POTENT PROTECTION FROM
LETHAL TOXIN ADMINITRATION IN
ANIMALS SO YOU CAN SEE HERE, AND
THESE ARE BINDERS THAT PROTECT
AGAINST THE TOXIN.
WE CAN NOT ONLY CONDITION THIS
PROCESS ON THE TARGET BUT ALSO
WE CAN TELL WHAT TYPE OF FOLD WE
WANT IT TO MAKE, SO IN THIS
CASE, WE CAN MAKE IT -- WE CAN
SPECIFY AN ANTIBODY OR A NANO
BODY FOLD AND WHAT THE DIFFUSION
PROCESS GENERATES IS A NANO BODY
STRUCTURE AND HERE'S A CRYOEM
STRUCTURE OF THE DIFFUSED NANO
BODY THAT BOUND TO A NANO BODY.
I SHOULD STRESS THIS IS NOT
TAKING AN ALREADY EXISTING
ANTIBODY ASPECT GEN STRECTURE
AND REDESIGNING IT, THIS IS
REBUILDING IT FROM SCRATCH SO
THESE BINDING MODE AND THE LOOP
IS COMPLETELY KNEW AND IT'S
DISCUSSED BY THE DIFFUSION STOP
PART, YOU CANEE THERE'S A VERY
CLOSE SIMILARITY BETWEEN THE
STRUCTURE AND THE FUSE, SO WE
CAN PUT BINDERS TOGETHER TO MAKE
AGONIST, HERE'S AN AGONIST THAT
IS THIS GREAT COLLABORATION
WHICH IS VERY POTENT, IL-21
AGONIST AND IT TURNS OUT, WE'RE
FINDING THAT THESE DESIGN
PROTEINS ARE HAVING KIND OF MUCH
MORE POTENT EFFECTS IN ANIMALS
THAN THEIR NATURAL COUNTERPART
AND SO FOR EXAMPLE, WITH IL-21,
THERE IS A MOUSE SURVIVAL CURVE
IN A TUMOR MODEL, AND YOU CAN
SEE WHAT THE ANALOGUE WE'RE
GETTING MUCH MORE PROLONGED
PROTECTION AND THAT'S WHAT MIKE
IS STEPHANIE HAVE FOUND THAT THE
STAT SIGNAL ACTIVATION ALLOWS
FOR MUCH, MUCH, LONGER AND THE
PROTEIN BYPASSEDS TIGHTLY TO THE
RECEPTOR AND IT'S TABLE SO IT
DOESN'T GET TURNED OVER WHEREAS
MOST NATURAL SIGNALING MOLECULES
ARE MEANT TO TURNOVER QUICKLY SO
THERE COULD BE A NUMBER OF
ADVANTAGES OF THESE
THERAPEUTICS.
WE CAN MAKE THESE PROTEINS, WE
CAN CAGE THEM IN VARIOUS WAYS SO
THEY'RE NOT ACTIVE SYSTEMICALLY,
THIS IS SHOWING CAGING WITH THE
MATE RICK METAL O PROTEASE, AND
THESE ARE ONLY ACTIVE WHEN A
TARGETING SYSTEM SET PRESENT FOR
EXAMPLE, PDL1, SO HERE WE HAVE A
CAGED MIMIC THAT UNTIL AND
UNLESS PDL 1 IS PRESENT, IS
COMPLETELY INACTIVE, IF PDL 1 IS
OPEN ON A TARGET CELL THEN IT
CAN OPEN UP AND ACTIVATE ON AN
EFFECTOR CELL.
AND WE'RE FINDING FOR OTHER
TYPINGS OF COMPOUNDS LIKE
LEPTINS, WE'RE FINDING VERY
PROFOUND IN VIVO CENTER FOR
EXCELLENCE ON AGINGS.
AND WE CAN TARGET THESE
COMPOUNDS TO TUMORS BY ATTACHING
THEM TO BINDING PROTEINS AND
ALSO GET POTENT POTENTIATION OF
SIGNALING.
WE CAN USE THESE BINDERS FOR
TARGETED DEGRADATION, SO WE
FIGURED OUT HOW TO DESIGN
BINDERS TO PROTEIN SWITCH
UNDERGO ENDOCYTOSIS AND
TRANSSIGNIFYITOSEIS.
WE CONFUSE THESE WOPROTEINS
WHICH BINDS A TARGET AND THEN
WHAT HAPPENS S&P THE DESIGN
PROTEIN BINDS THE TARGET ON THE
1 END AND THE ENDOSIGNIFYITOSING
ON THE OTHER AND INTERNAL LIGHT
TAKES IT TO THE LYSOSOME TO
DEGRADE THE TARGET.
SO WE CAN TARGET AND TRARNZ
SIGNIFYITOSING IN DIFFERENT
TISSUES AND THIS IS LOOK BEING
AT TISSUE DEGRADATION IN THESE
DEFINITE TISSUES.
THIS, WE CAN GREATLY ENHANCE THE
POTENCY OF ANTAGONISTIC
ANTIBODIES WITH INFIESING THIS
SORT OF TARGETED DEGRADATION
SIGNAL SO THIS IS A PDL 1
ANTIBODY WHICH HAS SOME EFFICACY
ON ITS OWN BUT IF YOU LOOK AT,
BUT WHEN YOU FUSE IT TO, SO
HERE'S THE TUMOR SIZE FOR
EXAMPLE, WHEN YOU FUSE IT TO 1
OF THESE RECYCLING RECEPTORS YOU
GET GREAT POTENTIATION OF THE
EFFECT.
THESE ARE NOT ONLY BLOCKING THE
TARGET, THEY ARE DEGRADING IT.
AND WE'VE RECENTLY FIGURED OUT
HOW TO MAKE THIS WHOLE PROCESS
CATALYTIC SO WE CAN -- SO, 1
COMPOUND CAN LEAD TO THE
TURNOVER OF MANY, MANY DIFFERENT
TARGET MOLECULES, IN THIS CASE,
IGGs OR DISEASES THAT INVOLVE
OVERABUNDANCE OF IGG.
AND I JUST WANT TO SHOW YOU
QUICKLY THAT WE CAN ALSO MAKE
BINDERS TO WHAT'S TYPICALLY BEEN
VERY HARD FOR DRUG DISCOVERY
WHICH IS COMPLETELY DISORDER
PROTEINS AND WE CAN DO THAT IN 2
WAYS AS SHOWN HERE, WE CAN JUST
JOURNAL DIFFUSION PROCESS WE CAN
LET THE TARGET SAMPLE DIFFERENT
COMFIRMATIONS AS WELL AND WE CAN
GET -- WE GET OUT QUITE POTENT
AND SPECIFIC BIEBDERS FOR A
VARIETY OF DIFFERENT TARGETS.
WE CAN USE THIS APPROACH IN
AMYLOID DEDISEASE OR DISEASES
LIKE ABETTA AND TAU AND WE FIND
THESE SUPPRESS AG GREEN CELLS
IMAIGZ, THAT'S WHAT YOU'RE
LOOKING AT HERE IS AGGREGATION
OF ABETTA FOR EXAMPLE AS YOU ADD
INCREASINGA, MOUNTS OF BINDER.
AND WE CAN ALSO MAKE THESE
EXTENDED GROUPS AS ILLUSTRATED
HERE FOR THIS DISCIPLINARY
NOEVERIN BINDER WHERE THEY HAVE
POCKETS FOR EACH OF THE AMINO
ACIDS SOEE CAN MAKE BINDERS TO A
WIDE VARIETY OF DIFFERENT
PROTEIN ANDS PEPTIDES, THEY'RE
SHOWN HERE, THIS BYPASSEDS QUITE
TIGHTLY FOR THE CRYSTAL
STRUCTURE AND DESIGN MODEL FOR 1
OF THESE IS AGAIN THEY'RE
REMARKABLY SPECIFIC, SO WE CAN
TARGET VERY SPECIFIC PROTEINS
AND 1 RECENT THING, THIS IS WORK
BY K. JOE WU, AND THEY MADE
SPECIFIC BINDERRINGS FOR EACH OF
THE 4 ISOFORMS WHICH IS REALLY
HARD BECAUSE THEY ONLY DIFFER BY
A FEW RESIDUES AT THE
C-TERMINUS, SO I WOULD SAY NOW
TARGETED DISORDER REGIONS HAS
KNOWN FROM THE HARDEST PROBLEM
TO THE EASEST PROBLEM BECAUSE WE
CAN KIND OF FORCE THEM INTO
WHATEVER STATE WE WANT.
SO, YOU KNOW THAT'S WHAT I WANT
TO TELL YOU ABOUT TODAY AND
REALLY EXCITED ABOUT
COLLABORATIONS AND USING THESE
TECHNOLOGIES.
AND I THINK I DON'T HAVE MUCH
TIME SO PEOPLE WHO DID THE WORK
ARE LISTED HERE AND I MENTIONED
SOME OF THEM ALREADY AND I WILL
BE HAPPY TO TAKE ANY QUESTIONS.
>> REALRY IMPRESSIVE
PRESENTATION, THANK YOU VERY
MUCH DAVID I THINK WE HAVE
MULTIPLE QUESTIONS IN THE CHAT.
SO, I WILL START WITH THE FIRST
1.
MOST OF YOUR EXAMPLES HAVE
HELIXES AND ONLY 1 EXAMPLE WITH
THE [INDISCERNIBLE] IN THEM, ARE
THOSE DE NOVO DESIGN BIASES TO
THE [INDISCERNIBLE]?
>> YEAH, IT'S AN INTERESTING
QUESTION, WE JUST POSTED A
PREPRINT THAT'S ON DESIGN OF
BINDERS THAT ARE EXCLUSIVELY
AGAINST BETA SHEET CONTAINING
PROTEINS AND THE BASIC IDEA IS
IF YOU HAVE AN ID FOLD YOU WANT
TO TARGET FOR EXAMPLE, A COMMON
FEATURE IS THE EDGE BETA STRAND
AND SO NOW WE CONDITION THE
DIFFUSION PROCESS TO BASICALLY
BUILD A BETA STRAND WHICH PAIRS,
SO, -- AND WE'RE FINDING THAT'S
A PRETTY POWERFUL APPROACH FOR
BETA CONTAINING TARGETS SO THE
EDGE OF THE BETA STRAND IS
POLAR, BUT A COMPLIMENTARY BETA
STRAND IS A GOOD WAY TO DO THAT
SO I THINK IF YOU MAKE THE
PROTEIN SMALLER IN THE DIFFUSION
PROCESS, THERE IS A BIAS TOWARDS
ALPHA HELIX BUT I WOULD SAY WHEN
WE MAKE BINDERS NOW, I WOULD SAY
IT'S ABOUT 50/50.
NGOT IT.
THANK YOU SO MUCH.
>> AND OUR QUESTION, HOW ABOUT
VIABILITY, THE ABILITY AND AG
GREEN CELLS IMAIGZ AND OTHER
RELEVANT IN VIVO EFFECT,
PROTEINS TAKEN INTO
CONSIDERATION FOR THESE PROTEIN
BINDERS SO HAVE YOU THOUGHT
ABOUT THAT COMPLICITY IN YOUR
MODEL?
>> RIGHT.
A COUPLE OF THESE HAVE GONE TO
CLINICAL TRIALS.
SO WE KNOW SOMETHING ABOUT
IMMUNO GENERATEDISSITY, I WOULD
SAY IN GENERAL THESE SMALL
PROTEINS ARE VERY STABLE, VERY
SOLUBLE AND THEY'RE OBVIOUSLY
SMALL.
IF YOU PUT THEM INTO AN ANIMAL
THEY WILL BE GONE IN 10 MINUTES,
THEY GET FILTERED OUT IN THE
KIDNEY, EXCEPT FOR THE AMOUNT OF
PROTEIN THAT THE PROTEIN IS
BOUND TO TO THE RECEPTOR, SO
LIKE I SAID WITH THE IL-21 MIMIC
WHAT THE DUGANS FOUND WAS THAT
YOU COULD STILL MEASURE ACTIVITY
AFTER 24 HOURS SO THE PROTEIN
THAT BINDS THE TARGET IS STAYS
THERE, EVERYTHING ELSE GETS
WASHED OUT.
AS FAR AS IMMUNO GENESISSITY, WE
SEE THE RESPONSES SO WE THINK
IT'S BECAUSE THEY'RE VERY
SOLUBLE AND STABLE SO THEY DON'T
GET PICKED UP BY THE PROCESS BUT
IT'S CLEAR THAT THAT WILL ALWAYS
HAVE TO BE CHECKED AS ANYTHING
GOES THROUGH THE DEVELOPMENT
PIPELINE, SO WE HAD AN IL2 MIMIC
THAT WENT THROUGH -- YOU KNOW
PRETTY EXTENSIVE CLINICAL TRIALS
AND THEY DID NOT OBSERVE MAJOR
NEUTRALIZING ANTIBODIES AND
WE'RE DEVELOPING METHODS NOW FOR
ELIMINATING THE T-CELL EPITOPES
IN THE PROCESS BUT SO FAR IT
HASN'T BEEN A BIG ISSUE AND
AGGREGATION TYPICALLY IS NOT A
PROBLEM FOR THESE PROTEINS.
NGOT IT.
THERE IS A SIMILAR QUESTION
REGARDING IMMUNO GENERATEDISIT
OF THESE DESIGN OF PROTEINS BUT
I THINK YOU TOUCHED BASE ON THAT
AS WELL.
DO YOU HAVE PLANS FOR USING YOUR
MODELS LIKE,A ROUND DIFFUSION IN
THE FIELD OF [INDISCERNIBLE]
BIOLOGY IN.
>> YEAH, WE'RE DOING QUITE A LOT
OF THAT.
AND YOU KNOW SYNTHETIC BIOLOGY
MEANS A WIDE VARIETY OF THINGS,
BUT YEAH, SO, I JUST FOCUSED ON
BINDER DESIGN, AND THERAPEUTICS
FOR THIS TALK, BUT FOR SYNTHETIC
BIOLOGY, I MEAN WE'RE MAKING
ORGANIZATIONS THOGINAL RECEPTOR
LIGAND PAIRS FOR EXAMPLE, TO ARK
LOW FOR THE ORTHOGONAL
SIGNALING, WE ARE MAKING ENZYMES
FOR NEW SYNTHETIC APPROACHES AND
I THINK YEAH, WHEN I THINK ABOUT
SYNTHETIC BIOLOGY, I THINK
ABOUT, I SORT OF THINK THAT'S
WHAT YEAR DOG.
A LOT OF SYNTHETIC BIOLOGY IS
MIX XG MATCHING PARTS THAT ARE
ALREADY IN NATURE BUT THE NEXT,
THE FUTURE OF SYNTHETIC BIOLOGY
IS BUILDING EVERYTHING FROM
SCRATCH TO DO WHAT YOU WANT.
>> I SEE, THE FOLLOW UP QUESTION
TO THAT IS ARE THERE ANY PLANS
TO USE ANY [INDISCERNIBLE]
PROTEIN BINDERS?
>> SO WE CAN INCORPORATE NATURAL
AMINO ACIDS AND WE DO THAT IN
SOME CASES TO GET A COVALENT
INTERACTION WITH THE TARGET WE
DO THAT FOR ENZYMES BECAUSE WE
CAN GET UNNATURAL CHEMISTRIES IN
THAT WAY, YOU TAKE THE LITTLE
BIT OF A PRICE, BECAUSE OF THE
MANUFACTURING GETS HARD, THE
MAIN WAY WE'VE DONE THIS IS MY
COLLEAGUE [INDISCERNIBLE] IS
LEADING THE GROUND ON THAT
BECAUSE WE CAN USE THESE SAME
TECHNIQUES TO MAKE THE PEPTIDES
AND THERE WE CAN, SENTENCE WE'RE
CHEMICALLY SINCEICIZING THEM, WE
CAN USE A WIDE RANGE OF AMINO
ACIDS.
>> GOT IT.
YOU MENTIONED FRAMING THE
PROTEIN, PROTEIN COMPLEX DATA,
THIS DATA TYPE, IT'S THE BIGGEST
BOTTLENECK TO IMPROVE THE
PERFORMANCE, BASICALLY, I THINK
IT'S ASKING ABOUT THE
[INDISCERNIBLE], RIGHT?
>> YEAH, I THINK 1 OF THE THINGS
I THINK EVERYONE KNOWS, BUT YOU
KNOW THE REASON WHY, THE STUFF
WE'RE DOING AND THE DEEP LINE
STUFF WORKS IS BECAUSE THE BDB
IS SUCH AN AMAZING RESOURCE AND
IT HAS THE WORK OF, YOU KNOW 10S
OF THOUSANDS OF PEOPLE OVER 60
YEARS WITH THE COST OF 10S OF
BILLIONS OF DOLLARS AND IT'S NOT
ONLY AN INCREDIBLE ARK MOUNT OF
DATA BUT IT'S INCREDIBLY MORE
CURATED SO MORE STRUCTURES WOULD
HELP.
SO I THINK ANOTHER PLACE IS IF
YOU WANT TO TRAIN A MODEL TO
PREDICTIVE AFFINITY WE DON'T
HAVE THOSE CORRESPONDING DATA
SETS FOR AFFINITY, AND SO, IT'S
HARD TO TRAIN MODELS TO DO THAT.
SO MORE STRUCTURES WOULD BE
GOOD.
ONE PLACE WHERE THAT WILL BE
GOOD IS WHERE DESIGNING TCRs
NOW TO MHCs AND THERE ARE ONLY
ABOUT A HUNDRED STRUCTURES SO
THEY'RE MORE STRUCTURES WILL BE
USEFUL, CERTAINLY MORE
STRUCTURES OF PROTEINS MORE
MOLECULAR COMPLEXES AND MORE
DIVERSE 1S AND WE SPENT QUITE A
BIT OF EFFORT TO GET PHARMA
CUTCHES TO MAKE THOSE AVAILABLE,
BECAUSE YOU KNOW THEY OBVIOUSLY
HAD A LOT OF STRUCTURES OF
PROTEIN SMALL MOLECULE COMPLEX
ANDS THAT WOULD CERTAINLY HELP,
YOU KNOW FOR BECAUSE WE'RE
EXCITED ABOUT SMALL MOLECULE
DESIGN USING THESE METHODS BUT
THAT IS DEFINITELY DATA EMILY
THED NOW.
YAW THAT WOULD BE GREAT TO SEE
THAT PROGRESS IN THAT FIELD AS
WELL.
IS THE OFF-TARGET
[INDISCERNIBLE] PROTEINS A
PROBLEM IN THE FIELD AND IF IT
IS, HOW IS IT THAT AGGRESSIVE
IN.
>> IT DOESN'T REALLY SEEM TO BE.
THIS IS WHAT YOU THINK WOULD BE
THE HARDEST PROBLEM BECAUSE THIS
IS LIKE DISCIPLINARY NOEVERIN A
VERSUS DISCIPLINARY NOEVERIN B
AND THESE ARE SOME RELATED
PROTEINS AND PEPTIDES, AND THESE
ARE ALL DISORDERED SO THERE'S NO
STRUCTURE, WE'RE JUST DOG AN
EXPERIMENT NOW, IT'S PRETTY
COOL.
SO WE HAVE MADE ABOUT 250
BINDERS TO ABOUT 250 DIFFERENT
TARGETS NOW AND WE HAVE THIS
GREAT COLLABORATION WITH THE
MENS LAB BUT THEY'RE DOING PULL
DOWNS ON THOSE, SO WE WILL
REALLY LEARN WHAT THE
SPECIFICITY IS, BUT SO FAR, --
SO FAR THEY LOOK TO BE PRETTY
SPECIFIC, I MEAN THE OTHER THING
WE CAN DO IS KNOCK OUT THE
TARGET PROTEIN AND SEE IF
THERE'S ANY RESIDUAL BINDING IN
THE CELL SO WE WILL LEARN A LOT
ABOUT SPECIFICITY, BUT WE'VE
DONE THIS AGAINST ALL
EXPERIMENTS AGAINST PURIFIED
PROTEINS, THEY'RE USUALLY PRETTY
SPECIFIC.
>> IS ANY FREE RADICALS
GENERATED ON DESIGNING BINDERS
FOR THE RNA SPECIFICALLY, LIKE
RNA BINDING PROTEINS >> YEAH,
YEAH, WE ARE DESIGNING PROTEINS
NOW, WE HAVE BINDERS, NOT CLEAR
HOW SPECIFIC THEY ARE, WE ARE A
LOT FURTHER ALONG WITH DNA
BINDING PROTEINS AND THERE WE
CAN MAKE THE DNA BINDING
PROTEINS AND WE'RE INCORPORATING
NUCLEACE SITES INTO THEM TO SAY
-- WITH THE BINDER STUFF NOW, I
TELL MY STUDENTS THAT I CAN'T
WORK ON THOSE NOW TO SOLVE A
PROBLEM BUT WE ARE INCORPORATING
CLEAVE TARGETS INTO IT.
>> WHAT IS YOUR VISION IN TERMS
OF USING SYNTHETIC PROTEINS IN
HUMANS?
>> WELL, I THINK, YOU KNOW, I
THINK THAT I'M VERY OPTIMISTIC
ABOUT THESE KIND OF TECHNIQUES
REALLY TRANSFOR THE PURPOSING
MEDICINE.
I MEAN YOU CAN GET -- YOU CAN
JUST GET MUCH MORE
SOPHISTICATION LIKE A WAS
SHOWING YOU COULD GET, RIGHT
NOW, YOU KNOW WE TREAT
AUTOIMMUNE DISEASE IN CANCER
WITH THESE KIND OF GLOBAL IMMUNE
SUPPRESS ANTS OR GLOBAL IMMUNE
ACTIVATORS, YOU CAN GET MUCH
MORE PRECISION OR MAKE MEDICINES
THAT ACT IN THE RIGHT TIME,
PLACE AND BODY WITH MORE
INTRICATE CONTROL MECHANISMS
WITH THESE DESIGN APPROACHES
THAN YOU CAN JUST BELLING OFF
ANTIBODIES.
>> I SEE.
DO YOU SEE YOURSELF THAT THE
MORE DIFFUSION MODELS,
CRYSTALLOGRAPHY [INDISCERNIBLE]
IN THE NEAR FUTURE, LIKE THE
QUALITY OF THAT, WHATEVER YOUR
GENERATE WOULD BE MATCHING THE
QUALITY OF THE PDB?
>> WELL WHEN WE SOLVE STRUCTURED
FOR THESE DESIGN PROTEINS, THEY
ARE OFTEN ESSENTIALLY IDENTICAL
TO THE CRYSTAL STRUCTURES ARE
IDEBTICAL TO DESIGN MODELS,
WE'RE GERONTOLOGYSTSING, IT'S
NOT ALWAYS TRUE BUT WE'RE OFTEN
-- WE'RE GETTING REALLY, REALLY
HIGH ACCURACY SO ON THIS 1 THERE
ARE A LITTLE BIT OF DIFFERENCES
SO THIS 1 ISN'T RIGHT ON BUT WE
HAVE SOME OF THE SIDE CHAINS ARE
POINTING IN THE WRONG DIRECTION
BUT THERE ARE OTHER EXAMPLES I
SHOWED HERE, LET'S SEE, LIKE I
THINK I HAD A PICTURE OF A
CRYSTAL STRUCTURE, WHERE WAS IT
IN I DON'T KNOW WHERE ELSE I HAD
BUT A LOT OF THESE ARE -- I
GUESS I DON'T HAVE EXACT THINGS
BUT A LOT OF THESE ARE JUST
EXACTLY SPOT-ON.
OH YEAH, THIS IS 2 CRYSTAL
STRUCTURES AGAIN WHICH ARE
ALMOST --
>> I SEE, YEAH, IT'S VERY
IMPRESS OF RESULT.
IN THE FUTURE WHICH RESEARCH
DIRECTION DO YOU THINK WILL BE
YOUR TOP PRIORITY RELATED TO
THESE ISSUES.
THAT'S AGAIN THE AUDIENCE
REALLY.
>> WELL, LET'S SEE, TODAY, I'M
KIND OF INTERESTED IN A BROAD
RANGE OF THINGS EMPLOY AS FAR AS
WHAT I TALKED ABOUT TODAY AND
MAKING PROTEIN THERAPEUTICS, I
GUESS LIKE I SAID, MY INTEREST
IS REALLY IN MAKING CONDITIONAL
MEDICINE, THINGS THAT ARE MUCH
MORE TARGETED AND SELECTED
EMPLOY I'M ALSO INTERESTED, YOU
KNOW I TOLD YOU ABOUT MAKING
SITE SPECIFIC PROTEASES, SO IF
WE COULD MAKE -- IMAGINE, YOU
KNOW THINK ABOUT THE COST AND
THE LOAD ON THE BOOED, AND
MAKING THERAPEUTIC ANTAGONISTIC
ANTIBODIES, BUT IT MAKES SMALL
PROTEINS THAT JUST WENT IN AND
DESTROYED THE TARGET FOR
ANTIVIRALS OR AGAINST TUMOR
PROTEIN CELL SURFACE PROTEINS, I
THINK THAT'S GOING TO BE A
THING.
I'M ERROR EXCITED ABOUT MAKING
MODEL CITIZEN LEAKULAR MACHINES
SO IN OUR CELLS WE HAVE MODEL
CITIZEN LEAKULAR CHAPERONS LIKE
HSP90, AND 70 FOR QUALITY
CONTROL, THERE'S NOTHING LIKE
THAT IN PRODUCK, BUT I THINK WE
CAN DESIGN MACHINES LIKE THAT
THAT CAN GO AROUND AND CLEAN
THINGS UP.
>> GOT IT AND I GUESS THE LAST
QUESTION I WILL TAKE, DO YOU
INCORPORATE THE DYNAMIC NATURE
OF THE PROTEINS IN YOUR
COMPUTATIONAL DESIGN PROCESS FOR
INSTANCE THE DEFECTORS OF ANY
[INDISCERNIBLE] OF DIE
NAMES--NAMESSICS?
?
YEAH, WELL, IT'S HARD.
THERE JUST ISN'T A LOT OF DATA
ON DYNAMICS, YOU CAN DO MD
SIMULATIONS BUT IN TERMS OF
ACTUALLY TRAINING ON DYNAMICS
DATA, IT'S -- THERE ISN'T REALLY
A LOT OF, THE NMR DATA ISN'T
COLLECTED IN A WAY YOU CAN TRAIN
ON, SO I THINK THERE WE ARE
LIMITED BY TRAINING DATA.
I THINK THINGS LIKE ENZYMES, I
DIDN'T TALK ABOUT CATALIAISONS
DESIGN BUT UNDERSTANDING HOW
THINGS MOVE IS CLEARLY
IMPORTANT.
NGOT IT.
THANK YOU SO MUCH FOR YOUR TIME
TODAY.
IT'S A REALLY GREAT PRESENTATION
AND ANSWER TO THE QUESTION,
REALLY APPRECIATE IT.
>> OKAY, ALL RIGHT, THANKS!
>> THANK YOU VERY MUCH.
SO I GUESS IF THAT'S THAT, WE
ARE GOING TO CLOSE THIS SESSION
NOW AND WE WILL HAVE THE SLIDE
30 MINUTES BREAK UNTIL 3:00 P.M.
AND WHEN WE ARE REALLY EXCITED
TO GO TO OUR PANEL DISCUSSION SO
PLEASE STAY TUNED.
COMING BACK AND BE READY TO ASK
QUESTIONS AND DISCUSSIONS.
BACK TO YOU.
>> THANK YOU ALEXEY, THAT WAS
SUCH AN AWESOME SESSION, I HAVE
THE AGENDA UP LIKE ALEXEY SAID,
WE WILL BE BACK AT 3:00 SO
EVERYONE GETS A BIT OF A BREAK
BUT MAKE SURE TO COME BACK AT
THRESHOLD BECAUSE IT WILL BE A
SUPER INTERESTING SESSION, WE
WILL DISCUSS A LOT OF THE GAPS
IN THE FIELD AND WE'RE GOING TO
TAKE A LOT OF QUESTIONS AS WELL.
ESPECIALLY THE 1S WE COULDN'T
ANSWER IN TODAY, AND YESTERDAY.
ALL RIGHT, BACK AT 3:00.
>> HI, EVERYONE, WELCOME BACK,
WE'RE SUPER EXCITED TO HAVE YOU
BACK WITH US TODAY FOR A FINAL
SESSION HERE ON BRIDGING THE
GAPS IN TRANSLATION.
SO FOR THIS I WOULD LIKE TO
INTRODUCE THE CHAIR ISSUES
MODERATOR AND THE SPEAKER IN
THIS SESSION DR. ROMMIE AMARO,
ROMMIE, HOLDS THE DISTINGUISHED
POSITION OF DIRECTOR OF
UNIVERSITY OF CALIFORNIA AT SAN
DISCIPLINARY OAGY, HE AS A
PRESIDENTIAL EARLY CAREER,A
WARDS OF SCIENCE AND ENGINEERS
AND OTHERS AND HER SCIENTIFIC
INTERESTS LIE AT THE COMPUTER
AIDED DRUG DISCOVERY AND
BIOPHYSICAL SIMULATION.
ROMMIE, THE FLOOR IS YOURS.
WE ARE EXCITED TO HEAR THE
SESSION.
NTHANK YOU.
LET ME SHARE MY SLIDES.
OKAY, HOPEFULLY YOU ARE STARTING
TO SEE THOSE.
CAN YOU SEE EVERYTHING?
>> THANK YOU.
>> NO PROBLEM.
HELLO, EVERYONE.
I'M HAPPY TO BE HERE, I THINK SO
FAR IT'S BEEN A GREAT 2 DAYS AND
WE'RE BRINGING UP THE KACCT BOOS
NOW, AND WHERE WE WANTED TO
REALLY FOCUS IS IN BRIDGING THE
GAPS IN TRANSLATION, AND WE HAVE
OUR SESSION TODAY, I'M GOING TO
GIVE A LITTLE BIT OF AN INTRO,
EVERYBODY WILL GET 20 MINUES
INCLUDING MYSELF AND THEN PAT
WALTERS FROM RELAY THERAPEUTICS
IS GOING TO GIVE A 20 MINUTE
PERSPECTIVE ON DANGERS OF
IMPORTANT DATA, RUSS ALTMAN WILL
GIVE A PERSPECTIVE ON LANGUAGE
MODELS, AND THEIR EVALUATION OF
REGULATORY SUBMISSIONS, THEN WE
HAVE JOEL FROM GSK WHO WILL TALK
ABOUT TEAM ENVIRONMENT FOR
BETTER TRANSLATION AND THEN, WE
ARE GOING TO GO INTO -- WE WILL
GO DIRECTLY INTO A PANEL
DISCUSSION WITH ALL OF US HERE
INCLUDING MARTY HEAD FROM AMJEN
WHO WAS 1 OF THE OPENING
SPEAKERS YESTERDAY.
SO IT SHOULD BE GOOD.
SO WE WILL LET FOLKS GIVE A
TWEBT MINUTE TALK AND INSTEAD OF
HAVING Q&A, WE'RE GOING TO SAVE
ALL THE Q&A DIRECTLY FOR THAT
LAST 40 MINUTE SESSION AND I
ALSO WANT TO SAY TO INVITE FOLKS
AND REMIND AUDIENCE MEMBERS TO
PLEASE SUBMIT QUESTIONS FOR THAT
BECAUSE I THINK WHAT WE WANT TO
DO IS HAVE A GOOD DISCUSSION
ABOUT TOPICS THAT ARE OF
INTEREST TO FOLKS WHO ARE
ATTENDING AND MAYBE YOU KNOW GET
INSIGHTS THAT AREN'T NECESSARILY
PART OF LIKE A TYPICAL
PRESENTATION.
OKAY, AND THEN I HAVE TO CONFESS
THAT WHEN I WAS LISTENING TO
EVERYTHING, I WAS GETTING SO
EXCITED FOR THE SCIENCE BECAUSE
I CAN'T HELP IT.
INSTEAD OF JUST -- BECAUSE WE
HAVE 40 MINUTES AT THE END WHERE
WE CANIAMER AND DISCUSS
DIFFERENT CHALLENGES SO I WANT
TO GIVE A LITTLE BIT OF A VIEW
ABOUT WHAT WE ARE WORKING ON IN
THIS SPACE AND I THINK IT, YOU
KNOW IT'S, IT'S HARD TO FOLLOW
DAVID BAKER, IT'S NOT THE FIRST
TIME I'VE BEEN IN THIS POSITION,
DARN IT AND NOW WITH HIS NOBEL
PRIZE, CONGRATULATIONS DAVID
AGAIN.
BUT HE DID MENTION AT THE END
AND IT'S COME UP, YOU KNOW WHAT
ABOUT DYNAMICS AND YOU KNOW
DYNAMICS IS SOMETHING THAT
MYSELF AND GROUPS HAS BEEN SUPER
INTERESTED IN AND WE'VE HEARD A
BIT ABOUT IT AND IT'S ESPECIALLY
WHAT WE'RE INTERESTED TO DO IS
USE DATA CENTRIC PHYSICS BASED
MOLECULAR DEMONSTRATIONS AS A CO
CALLED COMPUTATIONAL MICROSCOPE
AND SO THIS MEANS WE CAN
INTEGRATE ALL TYPES OF DIFFERENT
EXPERIMENTAL DATA SETS COMING
FROM DIFFERENT MODALITIES, THEY
COULD BE DIFFERENT STRUCTURAL
DATA SETS TAKEN AT RESOLUTIONS,
LIKE CRYOEM, ET, LIPID OMICS,
FLI COLSIS COMICS, WE CAN
COMBINE ALL THESE DIFFERENT DATA
SES TAKEN IN DIFFERENT GROUPS
FROM DIFFERENT REPOSITORIES AND
THEN BUILD THESE SORT OF
SYSTEMS, THESE ATOMIC LEVEL
REPRESENTATIONS OF BIOLOGICAL
SYSTEMS THAT REALLY ARE SORT OF
ALLOWS FOR THE SORT OF COHESIVE
UNDERSTANDING OF DRUG TARGETS
AND AND THEN ALL WE'RE DOING IS
APPROX MIGHTING IT DOWN TO THE
ATOMS, WE HAVE THE FUNCTION HERE
THAT DESCRIBES EACH OF THE
INTERACS WITH EVERYTHING ELSE IN
THE SYSTEM AND I THINK JOHN
MIGHT HAVE TALKED A LITTLE BIT
BUT I'M NOT SURE IF HE TALKED A
LITTLE BIT ABOUT DYSFUNCTIONAL
FORM BUT THERE'S A LOT PEOPLE
ARE DOING TO TRY TO UPDATE THIS
FUNCTIONAL FORM WHICH FOLKS HAD
BEEN USING FROM THE 70S BUT IN
ANY CASE, THEN WE DO, WE
GENERATE OFTEN TONS OF DATA,
COULD BE TERRA BYTES OF DATA, OF
JUST SORT OF STRUCTURAL DYNAMIC
DATA SETS, THROUGH THE
INTEGRATION OF NEWTON'S EQUATION
OF TIME WITH THESE OTOMIC
COORDINATES AND SO THE ANALYSIS
OF ANOTHER PLACE WHERE THERE'S A
LOT OF JUST SORT OF OF COURSE
CODING HAS TAKEN PLACE BUT WHERE
MACHINE LEARNING HAS COME INTO
IT AND INTERPRET THE EXPERIMENTS
WE COULD ADVANCE OUR
UNDERSTANDING OF BIOLOGY AND
INCLUDING THE DEVELOPMENT OF NEW
SMALL MOLECULE DRUGS THAT FOR
EXAMPLE COMBINED CRYPTIC POCKETS
THAT AREN'T PRESENT IN THE
CRYSTAL GRAPHIC OR CRYOEM
STRUCTURES BECAUSE WE KNOW ALL
THESE TARGETS ARE DYNAMIC AND
OFTEN THEY DO OPEN UP NEW
POCKETS FOR SMALL MOLECULE
DISCOVERY, WHAT I WANTED TO TELL
YOU ABOUT TODAY ARE OUR EFFORTS
MORE ON PROTEIN DESIGN, AND AS
DAVID MENTIONED THERE'S BEEN A
HUGE SMALL PROTEIN BINDERS AND I
WILL ACTUALLY TAKE IT A LITTLE
BIT FURTHER AND TALK ABOUT HOW
SIMULATION CAN ALLOW US TO SEE
THE UNSEEN FOR TARGETS FOR
IMMUNOGEN DESIGN.
I DON'T WANT TO SPEND TOO MUCH
TIME ON JUST THE METHOD BUT
REALLY NOW SORT OF A MULTISCALE
COMPUTATIONAL MICROSCOPE
ACTUALLY THAT LETS FOLKS PROBE
DRUG TARGETS AND THEIR
ENVIRONMENTS FROM THE QUANTUM TO
THE MACROSCALE REALM.
WHAT I WANT TO TALK ABOUT TODAY
IS THE EFFORTS IN THE NANO
SCALE.
SO THE EXAMPLE I WANT TO TELL
YOU ABOUT QUICKLY IS WITH THE
SARS-COV-2 VIRUS, WE ALL KNOW
ABOUT THIS, IT'S A LIPID
PROFESSIONAL VIRUS, IT HAS
STRUCTURAL PROTEINS LIKE THE
EPROTEIN AND THERE'S A FEW TILED
INSIDE THE MEMBRANE, THERE'S
M-PROTEIN, HUNDREDS OF THOSE ON
THE INSIDE OF THE VIRAL MEMBRANE
THAT PROVIDE STRUCTURE AND THIS
IS AN EXAMPLE OF 1 OF THE
STRUCTURAL MODELS WE BUILT FOR
THE WHOLE VIRUS, WE AND MANY
FOLKS ARE INTERESTED IN THE
TBLIEK O PROTEINS INCLUDING THE
SPIKE PROTEIN BECAUSE IT SITS ON
OUTSIDE OF THE VIRUS AND PLAYS A
KEY ROLE IN INFECTION BUT IT
ALSO IS A KEY ELEMENT OF
IMMUNITY BECAUSE THAT'S WHERE A
LOT OF OUR ADAPTIVE IMMUNITY
RESPONSES ARE DIRECTED.
SO WE BUILT A COMPREHENSIVE
MODEL THAT INCLUDED SORT OF AN
INTEGRATIVE MODEL I SHOULD SAY
THAT INCLUDED BOTH CRYOEM
RESOLVED PARTS OF THIS AS WELL
AS USED PREDICTIONS FOR OTHER
BITS THAT WERE NOT ABLE TO BE
EXPERIMENTALLY RESOLVED THAT
INCLUDES THE GLYCANS THAT ARE
PRESENT IN THESE SYSTEMS WHICH
ARE ALSO HIGHLY MOBILE, THERE'S
ALSO THE TRANSMEMBRANE PART SO
REALLY THE ENTIRE END-TO-END
SEQUENCE IS REPRESENTED AT THE
ATOMIC LEVEL AND THEN WE CAN
LIKE I SAID SAYING BEFORE,
ANIMATE THE DYNAMICS.
THIS IS HOW WE GET THE PROTEINS,
THIS IS REALLY FORTUNATELY OR
UNFORTUNATELY COMPUTING AND
SIMULATION IS REALLY THE ONLY
WAY TO GET AT THESE HIGH
RESOLUTION DYNAMICAL DATA SETS,
THERE IS TIME RESOLUTION CRYOEM
NOW BUT YOU WILL ONLY RESOLVE
SOME STATES AND THAT'S STILL
SORT OF A WORK IN PROGRESS,
SIMULATIONS ALLOW US TO VIEW THE
DYNAMIC SYSTEMS WITH RELATIVELY
HIGH FIDELITY AND SO WE HAVE
BEEN INTERESTED, SO AGAIN GOING
BACK TO THE SPIKE PROTEIN, YOU
KNOW MOST OF WHAT WE HAVE
DESIGNED IS ACTUALLY TARGETING
THE ENTIRE SPIKE PROTEIN WHICH
INCLUDES THAT S1 DOMAIN SO THE
SPIKE PROTEIN HAS THE S-1 DOMAIN
WHICH POPPED OFF THERE AND THEN
THAT PEELS OFF DURING THE
PROCESS OF INFECTION AND THEN IS
LEFT WITH THE S2 DOMAIN SHOWN
HERE IN THE MIDDLE SO THIS IS
REALLY SORT OF THE INSIDE CORE
OF THE SPIKE PROTEIN AND SO WE
YOINED A TEAM OF RESEARCHERS,
JASON AND OTHERS AND TRIED TO
TARGET S2 THIS INSIDE CORE
POTENTIALLY FOR PAN CORONA VIRUS
VACCINE DEVELOPMENT BECAUSE WE
ALL KNOW THAT VERY UNFORTUNATELY
S1 MUTATES LIKE CRAZY AND SO THE
MOST CONSERVED PARTS OF THIS
BEAST ARE ACTUALLY ON THE INSIDE
CORE OF THE PROTEIN SO S2 IS
HIGHLY CONSERVED ACROSS THE
FAMILY SO IT'S BEEN THOUGHT IT
WOULD BE A POTENTIALLY GOOD
TARGET.
IT ALSO HAS IMMUNOGENS AT THE
TOP THERE, AND SOME I'M SHOWING
HERE IN RED AND ALSO IN YELLOW
AND HAVE YOU 1 IN THE STALK THAT
ARE THERE SO IT'S ALSO NEWT O
GENIC, 1 CHALLENGE HAS BEEN
STABILITY OF THIS.
SO YOU PRESS IT IF IT'S AN MRNA
CONSTRUCT, SO IT BASICALLY FALLS
APART SO IT HAS THE PREFUSION S2
WHERE YOU HAVE THE EPITOPE
SYSTEM UNSTABLE AND THIS IS THE
CASE EVEN FOR THESE REALLY SORT
OF HIGHLY DESIGNED AND OPTIMIZED
STABLE HEXAPRO MUTANTS OF S2,
THEY ALSO JUST SORT OF FLOP
APART, SO REALLY FURTHER
STABILIZATION IS REQUIRED IN
ORDER TO USE THIS AND AN ANTIGEN
AND SO THIS IS WHERE WE WANT TO
BRING IN MODEL CITIZEN LEAKULAR
SIMULATION AND NOT ONLY DID WE
JUST DO IN THIS CASE WE WANT TO
USE A SORT OF MORE ADVANCED OR
ENHANCED SAMPLING TECHNIQUE
CALLED WEIGHTED ONSEMBLE
MOLECULAR DYNAMICS WHICH IS A
WAY OF OVERCOMING 1 OF THE
CENTRAL LIMITATIONS OF THE
METHOD OF MD SO YOU KNOW 1 OF
THE ISSUES OR CHALLENGES WITH
THESE MANAGES IS THAT THEY ARE
PHYSICS BASED AND THEY'RE SLOW,
SO COMPUTING THOSE IS RATHER
SLOW AND COMPUTATIONALLY
INTENSIVE.
IDEALLY WE WOULD HAVE ENOUGH
DATA THAT WE COULD BUILD FASTER
SURROGATE MODELS WITH ML BUT
WE'RE NOT THERE YET AND SO WE'RE
STILL -- WE AND MANY OTHERS IN
THE FIELD ARE IN THE PROCESS OF
GENERATING HIGH QUALITY DATA
SETS THAT CAN BE UTILIZED LIKE A
MACHINE LEARNING AND AI
APPROACHES BUT WE'RE NOT THERE
QUITE YET.
SO IN ANY CASE, WE TURN TO
WEIGHTED ONSEMBLE MD WHICH IS AN
ENHANCED SAMPLING.
IT'S CALLED PAST SAMPLING,
APPROACH THAT BASICALLY HELPS TO
GET US OUT OF THESE BASINS, I
LOST MY MOUSE BUT THAT'S OKAY,
IT HELPS GET US OUT OF THESE
WELLS THAT WE'RE USUALLY STUCK
IN WITH DYNAMICS BY SORT OF
SPAWNING MANY TRAJECTORIES AND
FOLLOWING THE 1S THAT ARE SORT
OF SUCCESSFULLY PROGRESSING
ACROSS THE PREDEFINED REACTION
COORDINATE IN WHICH CASE WHICH
WAS SORT OF THE OPENING OF THE
APEX, SO, HERE IS THE S2 SUBUNIT
AND WHAT I'M SHOWING, WHAT I'M
ABOUT TO SHOW IT'S A TRIEMER,
THE SPIKE PROTEIN IS A TRIMMER,
IT'S 1 OF MANY OF THESE TBLIEK O
PROFUSION PROTEINS ARE TRI MERS,
SO WE USED THIS TO BASICALLY
EXPLORE SORT OF THE OPENING
PATHWAY.
SO OUR THOUGHT WAS IN OUR
STRATEGY WAS, IF WE COULD LEARN
SOMETHING ABOUT THE MECHANISM OF
OPENING, OF THIS POTENTIAL
IMMUNOGEN, COULD WE THEN USE
INFORMATION ABOUT THE MECHANISM
AGAINST IT IN ORDER TO BASICALLY
TOP THAT PROGRESSION FROM
HAPPENING.
AND SO, HERE'S JUST ANOTHER VIEW
OF THIS MOLECULE, SORT OF
PROGRESSING ALONG LAWEDDER DALE
WE DEFINE AS OUR REACTION
COORDINATE, SO WE TART UP HERE
IN THE UPPER LEFT QUADRANT, SORT
OF OUR CLOSED CONFIRMATION AND
WE'RE PROGRESSING TOWARDS THE
OPENING STATE.
AND YOU KNOW WHEN YOU RUN
SIMULATIONS YOU ACTUALLY GET
MANY ENSEMBLES OF PATHWAYS AND A
LOT OF WORK HAS TO BE DONE TO
TRY TO TEASE OUT, YOU KNOW WHICH
IS THE DOMINANT PATHWAY, BUT IN
ANY CASE, WE CAN VIEW THAT, AND
SO BY LOOKING AT THAT OPENING
PATHWAY, WHAT WE SAW WAS WHEN
WREE LOOKED CLOSELY AT THE
MECHANISM OF OPENING WE CAN SEE
THAT IT'S SORT OF UNZIP INDEED
AN ASIMETRICAL MANNER AND WHEN
WE DID A CLOSER ANALYSIS, WHAT
WE FOUND WAS THAT IF YOU LOOK AT
THE CENTRAL HELIXES, STRATIFIED
AS A LADDER AS I'M SHOWING HERE
WITH THE HELIXES IN PURPLE AND
THE RESIDUES IN YELLOW AND
ORANGE, WHAT YOU CAN SEE IS THAT
THERE ARE PARTICULAR POSITIONS
AND PARTICULAR THAT TOP 1 V991
AND THEY WERE QUICKLY FALLING
APART.
SO, WHAT WE DID THEN WAS WE
ACTUALLY LEARNED THE MECHANISM
AND TRUST TRIED PUTTING
TRYPTOPHAN MUTATIONS INTO THOSE
LOCATIONS IN BOTH THAT TOP RUNG
AS WELL AS THE FOURTH RUNG.
AND WHEN WE DID THAT, WHAT WE
FOUND WAS A HIGHLY STABILIZED
NETWORK OF INTERACTIONS BETWEEN
THE DIFFERENT -- BETWEEN NOT
ONLY EACH OF THE SORT OF CENTRAL
HELIXES BUT ALSO THEIR SORT OF
NETWORK OF INTERACTIONS, BETWEEN
THE RESDUES THAT ARE SORT OF
UNDERNEATH IT AS WELL AS GOING
DOWN TO THE FOURTH LEVEL AND
ABOVE AND EVEN AN OUTSIDE
NETWORK OF STABILIZED RESIDUES
FURTHER.
IT BECAME -- SO WE MADE THESE 2
SUGGESTIONS AND THEN SIMULATED
THOSE AND WHEN WE SAW IT STOPPED
THE OPENING, WELL IT DIDN'T STOP
THE OPENING BUT IT SLOWED THE
OPEN BEING RELATIVE TO WHAT WE
HAD BEEN WOSH OTHER WITH WE THEN
HANDED IT TO JASON AND HIS TEAM
AND THEY WERE ABLE TO EXPRESS
THE DOUBLE MUTANT, WHAT THEY SAW
WAS THAT IT WAS ACTUALLY -- I'M
SORRY I LOST MY POINTER, THEY
SAW THAT ACTUALLY IF YOU LOOK AT
DIFFERENTIAL SCANNING AT
TEMPERATURE PLOTS THESE 2
MUTATIONS CREATED A HIGHLY
TEMPERATURE STABILIZED
CONSTRUCT, IN FACT THE MOST
TABLE THAT WE KNOW OF MOW AND
THE OTHER COOL THING WAS BECAUSE
OF THE FLEXIBILITY OF THIS
CONSTRUCT INITIALLY THEY WERE
NEVER ABLE TO GET HIGH
RESOLUTION STRUCTURES OF JUST
THE S2 BECAUSE IT WAS JUST TOO
DYNAMIC HOWEVER WITH THESE 2
MUTATIONS THEY WERE ABLE TO GET
THE HIGH RESOLUTION STRUCTURE
WHICH IS REALLY COOL AND IT
VALENTINEDICATEED ALL THE
FINDINGS WE HAD ABOUT THE
MUTATIONS AND THE STRUCTURAL
INTEGRITY IN THE PREFUSION
STATE, WE THEN PASS THIS TO
[INDISCERNIBLE] AND HIS TEAM AND
THEY DID -- THEY IMMUNIZED MICE
AND DID THE SERA EXPERIMENTS AND
THEY FOUND THAT IT WAS JUST AS
GOOD AGAINST THESEY RECOMBIN ANT
VIRUSES AGAINST ORIGINAL WUHAN
STRAIN BUT HAD IMPROVED
PERFORMANCE VERSUS OMICRON, AND
ACTUALLY WITH THESE BINDING
AFFINITY INTERACTIONS THEY --
THAT ARE SHOWN ON THE RIGHT HAND
SIDE THAT WE ACTUALLY SAW THESE
EPITOPES WERE STILL BEING
RECOGNIZED AND BOUND TO.
SO THERE'S A LOT OF FOLKS TO
ACKNOWLEDGE THERE.
I STILL HAVE A FEW MINUTES.
I JUST WANT TO SAY I FEEL LIKE
THIS IS SORT OF A LITTLE BIT OF
A DIFFERENT TACT THAN WHAT WE'VE
HEARD, I WILL SAY 1 OF THE
ADVANTAGES OF RUNNING THIS
TECHNIQUE, IS THAT INSTEAD OF
RUNNING A TON OF LAB
EXPERIMENTS, I THINK IT'S A
DIFFERENT VIEW WHERE YOU CAN
TAKE A DEEPER DIVE INTO
UNDERSTANDING A TARGET AND THE
MECHANISM OF ACTION, DYNAMICS
AND AND THEN IN FACT BE HIGHLY
STRATEGIC PLAN TEEJIC ABOUT
WHICH ACTIONS YOU'RE MAKING SO
THIS WAS A LARGE EFFICIENCY,
THEY CREATED, THE DOUBLE MUTANT
AND THEN EACH OF THE SINGLE
MUTANTS AND THEN WE HAD THESE
RESULTS WHICH WERE SORT OF, YOU
KNOW WHERE WE GOT TO THE
MILESTONE THAT WE WERE SORT OF
LOOKING FOR.
THAT SAID OF COURSE, THERE ARE
AND AS DAVID ALSO MENTIONED
THERE'S A LOT OF CHALLENGES IN
THIS FIELD AND FOR THE FIELD IN
GENERAL FOR DRUG AND IMMUNOGEN
DESIGN AND TALK ABOUT THESE IN
OF THE LAST SESSION AS WE'VE
BEEN CUSSING I THINK A LITTLE
BIT ALONG THE WAY BUT CERTAINLY
YOU KNOW WITH THE PDP, WHICH
ENABLED ALPHA FOLD AND ALL OF
THE GREAT SORT OF FRUITS OF THAT
LABOR WHICH TOOK MANY YEARS AND
THE DISCIPLINE OF A WHOLE FIELD
OR REAL SORT OF COMMITMENT, TO
SHARING DATA AND BEING IN THE
FORMAT OF IT AND SO FORTH, YOU
KNOW WE NOW SEE HOW THAT NEEDS
TO REVERBERATE ACROSS MANY
FIELDS INCLUDING PROTEIN
DYNAMICS, INCLUDING LIGAND SMALL
MOLECULE STRUCTURE DESIGN, AND
SO, IN MY CLOSING THING, I SEE
PAT, YOU'RE READY TO GO, I HAVE
MAYBE 2 MINUES I UOF THE WHY
WANT TO TOUCH A COUPLE OF
CHALLENGES HERE JUST TO SORT OF
SEE IT INTO THE DISCUSSION THAT
WE WILL HAVE IN A MINUTE AND I
WILL FLIP IT TO YOU BUT YOU KNOW
AS 1 OF THE THINGS THAT WE ARE
PARTICULARLY INTERESTED IN,
AGAIN WHICH I GUESS MAYBE KIND
OF A LITTLE BIT UNIQUE BUT IT'S
SORT OF ENLARGING SORT OF THE
SCALE ASK COMPLEXITY OF SYSTEMS
THAT WERE ABLE TO SIMULATE AND
THERE'S, YOU KNOW WITH THIS --
WE HAVE SUCH AMAZING DATA SETS,
FOR SO MANY DIFFERENT TARGETS
AND COMING AT THEM FROM SO MANY
DIFFERENT CORRECTIONS NOW, YOU
KNOW REALLY BEING ABLE TO, YOU
KNOW I SEE A LOT OF CHALLENGES,
RELATED TO THAT AND ALSO WITH
DATA, AND I THINK DATA'S KIND OF
LIKE IN SOME WAYS A FUNNY THING,
BUT THEN IT'S LIKE, MAYBE IT'S
NOT THE RIGHT TYPE OF DATA AND
ESPECIALLY IN OUR FIELD, WE SIGH
-- SEE A LOT OF BIOLOGY
LABORATORIES GENERATING DATA BUT
IT'S NOT NECESSARILY UNIFIED OR
BEING SHARE INDEED A WAY THAT
CAN BE USED AND I KNOW I'M NOT
THE OHM PERSON USING AI IN ML
GOING FORWARD.
OF COURSE THERE'S HARDWARE
LIMITATIONS EVEN THOUGH THERE
HAVE BEEN GREAT ADVANCES IN GPU
AND THERE ARE LIMITATIONS FOR
THESE ALGORITHMS THEY THINK WILL
NEED TO BE ADDRESSED.
AND THEN THERE'S A LOT OF SOCIAL
ASSPECS AND I'M SURE WE GET TO
TOUCH ON THIS IN THE LAST LITTLE
PANEL DISCUSSION AND YOU KNOW
THERE'S DIFFERENT SOCIAL ASPECTS
DEPENDING ON ACADEMIA ON
INDUSTRY, BUT I THINK THE FIELD
AS A WHOLE DRUG DISCOVERY AS A
WHOLE, THAT WHOLE ECOSYSTEM WILL
DO BETTER BY SORT OF TRYING TO
ADDRESS SOME OF THESE, AND OF
COURSE, PAT WILL TALK A LOT
ABOUT, MAYBE WE GET TO HEAR 1 OF
PAT'S RANTS 1 OF MY PERSONAL
FAVORITE THINGS TO DO, AND I
LOST MY CURSOR, SO I WANT TO
PASS IT TO YOU BUT YOU KNOW
WHAT, I WILL TRY TO STOP SHARING
MY SCREEN.
CAN YOU TRY TO SHARE YOUR
SCREEN?
OH THERE IT IS.
I CAN STOP SHARE.
WITH THAT I WILL PASS THE BATON
TO PAT WALTERS WHO IS AT RELAY
THERAPEUTICS AND HAS BEEN KIND
OF AN ORACLE IN THE FIELD FOR
BENCHMARKING OF DATA AND KEEPING
EVERYBODY HONEST.
SO PAT, I LOOK FORWARD TO YOUR
YOUR TALK, TO YOUR RANT.
TAKE IT AWAY.
WE SEE YOUR SCREEN.
DID YOU LOSE YOUR MOUSE, TOO?
>> I DID BUT I FOUND IT.
>> YOU'RE GOOD, WE HEAR YOU.
THANK YOU EMPLOY.
>> ALL RIGHT, THANK YOU ROMMIE,
AND THANK YOU EVERYBODY, YOU
WON'T BE DISAPPOINTED I'M GOING
ON A RANT.
THIS FIELD IS MOVING AI IS DRUG
DORPHY IS MOVING AT A HUNDRED
MILES AN HOUR AND THERE ARE TONS
OF PAPERS BEING PUBLISHED, IT'S
A REALLY EXCITING FIELD TO WORK
IN, I'M REX SIGHTED PEOPLE ARE
RELEASING SOURCE CODE WITH THEIR
PAPERS AND THINGS YOU CAN TRY
OUT BUT I THINK THERE ARE SOME
THINGS WE NEED TO CONSIDER MORE
CAREFULLY SO I WANT TO START OUT
WITH JUST A FEW FRAMING
ASSUMPTIONS.
THERE ARE A LOT OF PAPERS BEING
PUBLISHED THESE DAYS THAT
PRESENT NEW MACHINE LEARNING
APPROACHES FOR DRUG DISCOVERY.
MOST OF THESE PAPERS PRESENT
BENCHMARK COMPARISONS SO
EVERYBODY WANTS TO SHOW THAT
THEIR METHOD IS BETTER THAN THE
CURRENT STATE-OF-THE-ART,
UNFORTUNATELY, MOST OF THESE
BENCHMARK COMPARISONS ARE
PERFORMED ON DATA SETS THAT ARE
REALLY BAD.
AND IN ESSENCE A LOT OF PEOPLE
ARE PUBLISHING PAPERS, FEELING
THEY CAN LEARN RANDOM LABELS.
AND HOPEFULLY I WILL CONVINCE
YOU OF THAT.
THE SECOND THING I WANT TO
CONVINCE YOU OF IS THAT WE NEED
TO BE MORE RIGOROUS ABOUT
STATISTICS AND THE WAY IN WHICH
WE COMPARE MODELS AND THEN I'LL
WRAP UP BY TALKING ABOUT A
COUPLE OF EFFORTS THAT WE HAVE
GOING WHICH WILL HOPEFULLY FIX
SOME OF THESE SITUATIONS.
SO WHAT WE WANT TO DO IS GET TO
THE POINT WHERE WE CAN COME UP
WITH A SET OF GUIDELINES.
I THINK WE NEED GUIDELINES TO
HELP AUTHORS TO PUT TOGETHER THE
BEST PAPER.
I THINK EDITORS SOMETIMES DON'T
HAVE THE BACKGROUND IN MACHINE
LEARNING OR STATISTICS TO BE
ABLE TO ASSESS THE WORK, NOR DO
THE REVIEWERS, SO I THINK WE
NEED GUIDELINES TO HELP
EVERYBODY AALONG AND PUT
EVERYBODY ON THE SAME PAGE, SO I
WANT TO TALK ABOUT 2 THINGS
TODAY.
ONE IS DATA SETS.
AND SOME OF THE PROBLEMS WITH
THE EXISTING DATA SETS AND THEN
I WANT TO TALK JUST A LITTLE BIT
ABOUT STATISTICS AND COMPARING
METHODS AND WHAT I THINK ARE THE
MORE APPROPRIATE WAYS TO DO
THAT.
AND TO START OUT TALKING ABOUT
DATA SETS, THE DATA SET THAT'S
USED MORE OFTEN THAN NOT WHEN
PEOPLE ARE PUBLISHING PAPERS ON
MACHINE LEARNING IS A DATA SET
CALLED MOLECULE NET, SO THIS
DATA SET WAS ORIGINALLY
PUBLISHED IN 2018 BY THE
[INDISCERNIBLE] GROUP AT
STANFORD.
IT CONSISTS OF SEVERAL DIFFERENT
CATEGORIES OF DATA SETS, THERE'S
A SET OF QUANTUM MECHANICS
PARAMETERS THAT WERE CALCULATED
BASED ON SMALL MOLECULES, THERE
ARE PHYSICAL PROPERTIES,
SOLBILITY, BINDING DATA FOR A
RANGE OF PROTEINS AND THEN
THERE'S PHYSIOLOGICALLY BASED
DATA AND THESE ARE REASONABLY
SIZED DATA SETS THEY RANGE FROM
HUNDREDS OF THOUSANDS DOWN TO A
FEW HUNDRED.
SO THIS IS GOOD, THERE ARE DATA
SETS IN THERE FOR CLASSIFICATION
AS WELL AS REGRESSION
UNFORTUNATELY, AS I HOPE TO
CONVINCE YOU IN THE NEXT FEW
MINUTES, THERE ARE SO MANY
PROBLEMS WITH THIS DATA SET AND
IT REALLY SHOULD NEVER BE USED
AGAIN.
SO LET ME JUST START OUT BY
POINTING OUT SOME OF THE REALLY
SIMPLE TECHNICAL DIFFICULTIES IN
THIS DATA SET.
THERE ARE A LOT OF MOLECULES
WHERE THE CHEMICAL STRUCTURES
ARE JUST INCORRECT, SO THEY HAVE
THINGS THAT VIOLATE RULES OF
VALENCE, SO YOU'LL HAVE
TETRAVALENT NITROGENS THAT DON'T
HAVE CHARGE, THERE'S A BUNCH OF
OTHER SIMILAR PROBLEMS AND YES,
CAN YOU GO THROUGH AND DO HACKS
TO KIND OF FIX ALL THIS BUT IT
WE'RE GOING TO USE THESE DATA
SETS AS STANDARD BENCH MASHS AT
THE VERY LEAST THEY SHOULD BE
CORRECT.
IN ADDITION TO BEING CORRECT,
THEY NEED TO BE CONSISTENT, SO
YOU KNOW HERE'S JUST 2 EXAMPLES
OF ANTIBACTERIALS THAT ARE IN
THE MOLECULE NET DATA SET AND
YOU'LL SEE THAT ON THE LEFT-HAND
SIDE WE HAVE A RIGHT SIDE, AND
IT ISN'T ON THE MOLECULE, THIS
HAS NOTHING TO DO WITH PKA, THIS
HAS TO DO WITH HOW THEY DRAW THE
MOLECULE IN THE DATA SET, IF
WE'RE EVALUATING LEARNING MODELS
NOT SOME STANDARDIZATION
PROTOCOL THAT'S PUTTING THINGS
INTO A CONSISTENT PROTOMERRIC
FORM.
THERE'S A LOT OF CASES IN THE
MOLECULE DATA SET WHERE STEREO
CHEMISTRY IS MISSING.
SO HERE YOU CAN SEE, 3 TERIO
ISOMERS OF THE SAME MOLECULE THE
FIRST 2 OF THE OF THE CHEMIST
RADIOY IS FULLY SPECIFIED ON THE
FAR RIGHT, THE TERIO CHEMISTRY
IS MISSING FOR 1 OF THOSE STEREO
CENTERS.
WHAT DID THAT MEAN IN DOES THAT
MEAN THAT CENTER IS RECEIVING OR
SOMEBODY JUST FORGOT TO PUT THAT
STEREO CENTER IN.
SO AGAIN, DATAET ISS NEED TO BE
TECHNICALLY CORRECT.
IT'S ALSO IMPORTANT TO HAVE DATA
SETS WHERE THE DYNAMIC RAINCHL
IN THE DATA IS APPROPRIATE TO
WHAT'S TYPICALLY ENCOUNTERED IN
DRUG DISCOVERY.
SO IN THIS PLOT, ON THE LEFT
SIDE OF THE SLIDE, I'M SHOWING
ANOTHER DATA SET ISSUES THE
SOLUBILITY DATA SET, WITH THE
MEASURED AQUESTIONNAIRESUS
SOLUBLES AND THAT'S IN THE
MOLECULE AND THAT DATA SET, 1
THING TO NOTICE WHEN YOU LOOKOT
X-AXIS WE HAVE THE EXPERIMENTAL
SOLUBILITY, ON THE Y-AXIS WE
HAVE A PREDICTION, THE THING TO
NOTICE IS THAT THE DAT SET SPANS
14 LOGS SO WHEN YOU HAVE A DATA
SET WITH A RIDICULOUS DYNAMIC
RAINCHL LIKE THAT, IT'S VERY
EASY TO DO REGRESSION.
BUT, CORRELATION THAT YOU GET
FROM THAT DATA SET IS NOT GOING
TO REPRESENT WHAT YOU SEE IN A
TYPICAL DATA SET.
SO HERE WHAT WE HAVE ON THE PLOT
ON THE RIGHT IS THE MOLECULE NET
SOLUBILITY ON THE DATA SET IN
BLUE, IN ORANGE I HAVE A MORE
REPRESENTATIVE GATTA DETAILS
THAT WAS TAKEN FROM A
PHARMACEUTICAL COMPANY AND YOU
CAN SEE THAT THAT DATA SET ONLY
SPANS 3 LOGS, SO WE REALLY NEED
TO MAKE SURE THAT THE DYNAMIC
RANGE IN THE DATA THAT WE'RE
USING TO BENCHMARK REPRESENTS
WHAT WE'RE GOING TO SEE IN
PRACTICE.
THERE ARE ALSO JUST IN
RIDICULOUS DATA CURATION ERRORS
IN THERE, SO ANOTHER DATA SET,
IN MOLECULE NET IS A DATA SET OF
BLOOD BRAIN BARRIER PENETRATION
DATA SO THEY HAVE A SET OF
CHEMICAL STRUCTURES, THEY'RE
LABELED 1, IF THE MOLECULE HAS
BEEN REPORTED TO BE BRAIN
PENETRANT, 0 IF IT WAS REPORTED
TO BE NOT BRAIN PENETRANT.
THAT SEEMS FINE, THIS DATA SET
IS PROBABLY BEEN PUBLISHED A
THOUSAND TIMES, LITERALLY.
IF YOU LOOK AT THIS DATA SET ARE
THERE ARE 11 MOLECULES THAT ARE
IN THE DATA SET TWICE.
ONCE AS BRAIN PENETRANT AND ONCE
AS NOT BRAIN PENETRANT.
THIS IS SIMPLY BECAUSE WHEN
SOMEBODY PUT THIS DATA SET
TOGETHER, THEY DIDN'T
STANDARDIZE THE SMILE STRINGS
AND DIDN'T GO THROUGH AND
CAREFULLY CURATE THE DATA AND
LOOK FOR ERRORS LIKE THIS.
SO WHEN YOU SEE SOMEBODY
PREDICTING A GOOD PERFORMANCE ON
A DATA SET LIKE THIS, YOU REALLY
ARE TO QUESTION IT AND THEN
THERE ARE OTHER DATA SETS IN
THERE WHERE PEOPLE ARE JUST
MISUSING THEM AND PREDICTING
THINGS THAT THEY ACTUALLY
SHOULDN'T BE ABLE TO PREDICT
FROM A SMALL STRING OR A 1
DIMENSIONAL CHEMICAL STRUCTURE.
ONE OF THE PRIME EXAMPLES OF
THIS IS THESE QUANTUM MECHANICS
DATA SETS CALLED QM27, 8 AND 9.
SO THIS A SET OF CALCULATED
CHEMICAL PROPERTIES FOR
MOLECULES WITH 7, 8 AND 9 ATOMS,
THESE PROPERTIES ARE
COMFIRMATIONAL --
CONFIRMATIONALLY DEPENDENT.
ONE SHOULD NOT BE ABLE TO
CALCULATE THESE THINGS FROM A
SMILE STRING.
SO PEOPLE ARE PUBLISHING PAPERS
WHERE THEY'RE MAKING PREDICTIONS
THAT ARE COMPLETELY
INAPPROPRIATE.
PERHAPS THE WORST CASE THOUGH IS
THIS DATA SET INSIDE MOLECULE
NET WHICH IS CALLED CIDER AND
IT'S TAKEN FROM A DATABASE OF
SIDE EFFECTS.
AND AGAIN THESE ARE THE SORTS OF
THINGS THAT 1 SIMPLY SHOULD NOT
BE ABLE TO CALCULATE FROM A
CHEMICAL STRUCTURE.
SO I'VE HIGHLIGHTED SOME OF THE
EPPED POINTS THAT ARE IN THIS
DATA SET.
SOCIAL CIRCUMSTANCES, HOW AM I
SUPPOSED TO BE ABLE TO CALCULATE
SOCIAL CIRCUMSTANCES FROM A
CHEMICAL STRUCTURE.
SURGICAL AND MEDICAL PROCEDURES,
I MEAN THIS IS JUST NONSENSICAL,
IT MAKES NO SENSE, I'M NOT SURE
WHY PEOPLE ARE PUBLISHING THIS,
PEOPLE SEEM TO JUST BE PLIENDLY
MAKING PREDICTIONS WITHOUT
ACTUALLY LOOKING AT WHAT'S
INSIDE THESE DATA SETS.
YOU KNOW, FINALLY AND IT WAS
FUNNY BECAUSE IT WAS A PAPER
THAT CAME OUT IN NATURE, LAST
WEEK, WHERE THEY WERE TALKING
ABOUT THE FACT THAT WE NEED MORE
DATA FOR MACHINE LEARNING BEFORE
DRUG DISCOVERY LEARNING AND 1
EXAMPLE THEY CITED THEY WERE SO
SHOWING HOW YOU COULD BUILD A
MACHINE LEARNING MODEL FROM THIS
PARTICULAR HIV DATA SET, FROM
THE MOLECULE AGAIN.
IF YOU LOOK THROUGH THIS DATA
SET, I WOULD ESTIMATE THAT AT
LEAST 80% OF THE MOLECULES THAT
ARE SHOWN AS BEING ACTIVE ARE
THINGS LIKE THE 1S THAT ARE
SHOWN IN THIS SLIDE.
THESE ARE ALL ASSAY ARTIFACTS.
THIS IS NOT POSITIVE IN AN
ASSAY, SO WE NEED TO BE MORE
CAREFUL ABOUT CURATING THESE
DATA SETS AND MAKING SURE WE'RE
PREDICTING SOMETHING THAT'S
MEANINGFUL.
THERE ARE GOOD DATA SETS OUT
THERE.
THIS IS A DATA SET THAT WAS
PUBLISHED LAST YEAR BY A GROUP
FROM BIO BIOGEN, IT WAS ALL
REASONABLE, IT WAS ALL DONE BY
THE SAME CALCULATIONS WITH TIME
LAB, GOT CONDITIONS, AT THE END
OF THE TALK I WILL TALK ABOUT
WHAT WE'RE DOING TRY TO TRY TO
DO A BETTER JOB AND PAINTING THE
FIELD TOWARD BETTER DATA SETS.
SO WHAT DO WE WANT TO DO, WE
WANT TO FOCUS OW BENCHMARKING
EFFORTS ON ROBUST CLEARLY
DEFINED END POINTS, WE WANT TO
UNDERSTAND THE DATA AND BE ABLE
TO REMOVE BAD ACTORS FROM THE
DATA SET.
WE WANT TO MAKE SURE THE DATA IS
CORRECT.
I DON'T LIKE LEADER BOARD AND
AMERICANS, AND TURNING THIS INTO
A CONTEXT, I WOULD PREFER TO
HAVE PEOPLE IN THE FIELD WORKING
TOGETHER TO FIGURE OUT HOW TO
COME UP WITH BETTER METHODS.
SO THAT'S RANT NUMBER 1, THERE'S
MOVE NOW IN RANT NUMBER 2 AND
TALK ABOUT STATISTICS.
SO TYPICALLY WHEN WE'RE
EVALUATING A MACHINE LEARNING
MODEL, WE TAKE THE DATA, AND
SPLIT IT INTO A DATA SET, TEST
SET, WE CAN USE A RANGE OF
METHODS TO DO THAT, WE WILL THEN
DO, 5, 10, 25 DIFFERENT SPLITS
OF THE DATA SET.
WE WILL CALCULATE STATISTICS,
SO, IF WE'RE DOING A REGRESSION
MODEL WE MIGHT BE CALCULATING
SOMETHING LIKE THE MEAN AVERAGE
ERROR OR THE R-QUARED OVER
REGRESSION MODEL, WE CAN USE
DIFFERENT METRICS FOR
CLASSIFICATION MODELS BUT WHAT
TYPICALLY GETS REPORTED THEN IN
THE LITERATURE, IS SIMPLY AN
AVERAGE OVER THAT SET OF CROSS
VALIDATION FOLDS.
SO WHAT PEOPLE WILL DO IS PUT UP
A TABLE, THEY SHE ALL THE
METHODS IN ROWS AND THEN THEY
HAVE THE COLUMNS WITH THEIR
STATISTICS AND THEY COME UP WITH
WHAT I REFER TO AS THE DREADED
BOLD TABLE EMPLOY SO PEOPLE WILL
PUT A TABLE UP, THEY WILL PUT
THE METHOD THAT HAS, YOU KNOW A
HIGHER VALUE IS BET BETTER IN
THIS PARTICULAR CASE WE'RE
LOOKING ATLY DIFFERENT
CLASSIFICATION METHODS,LET
HIGHER VALUE IS BETTER, THEY
TAKE THE METHOD WITH THE HIGHEST
VALUE, PUT IT IN BOLD, IT'S
USUALLY THEIR METHOD WHOO HOO
THEY WON.
BUT LOOK AT THIS TABLE, LOOKA
THE ROCK AUC, 1 METHOD HAD .901,
THE OTHER IS .904.
IS THAT REALLY DIFFERENT?
AND THEN PEOPLE WILL EITHER PUT
UP THIS BOLD TABLE OR THEY'LL
PUT SOME BAR GRAPH LIKE THIS TO
SHOW THE METHOD COMPARISON.
YOU KNOW OR THEY WILL TRY TO
WASH AWAY THEIR SINS BUT THEY'RE
STILL PUTTING THIS BOLD TABLE
TOGETHER, YOU KNOW THE STANDARD
DEVIATION IS A MEASURE OF
VARIANCE, IT'S NOT A STATISTICAL
TEST, THIS MOT A VALID WAY TO
COMPARE METHODS.
YOU KNOW, PEOPLE WILL DO WHAT I
REFER TO AS EYE DYNAMITE PLOT
WHERE YOU THEN PLOT THE STANDARD
DEVIATION, AGAIN THIS IS NOT
APPROPRIATE.
WE HAVE TO THINK ABOUT THE FACT
THAT WE HAVE MULTIPLE FOLDS OF
CROSS VALIDATION SO WHAT YOU'RE
REALLY DOING IS YOU'RE COMPARING
DISTRIBUTIONS, SO WHAT I HAVE
HERE IS A BOX PLOT SHOWING 2
DEFINITE METHODS, I'VE DONE 25
FOLDS OF CROSS VALIDATION, YOU
KNOW THE GREEN ON THE RIGHT IS
BETTER, THE RED LINES ON THE
LEFT IS WHERE THE METHOD IS
BETTER SO WE GET A MIXTURE, IT'S
NOT ALWAYS 1 METHOD, BETTER
THANTH OTHER.
BUT WHAT ARE WE DOING HERE?
WE'RE COMPARING DISTRIBUTIONS.
THERE'S NOR THAN A HUNDRED YEARS
OF STATISTICS GOING BACK TO
GUINNESS BEERS IN THE 1910S.
THERE ARE GOOD STATISTICAL
METHODS FOR COMPARING BRIEWKSS
IT'S JUST THAT OUR FIELD ISN'T
USING THESE.
SO WHAT WE NEED TO DO WHEN
COMPARING THESE IS SHOW A
P-VALUE, SHOW THERE IS
STATISTICALLY SIGNIFICANT
DIFFERENCE BETWEEN THE METHOD.
SHOW, CALCULATE SOMETHING LIKE
COHENS D AND SHOW AN EFFECT
SIZE.
WHAT I WOULD LOVE TO SEE OR I'M
SORRY, JUST TAKE 1 MORE STEP
BACK.
THE OTHER THING THAT GETS A BIT
MORE COMPLICATED WHEN YOU ARE
COMPARING MORE THAN 2 METHODS,
YOU HAVE A HIGHER PROBABILITY OF
JUST 1 METHOD BEING BETTER THAN
THE OTHER BY RANDOM CHANCE BUT
AGAIN, YOU KNOW THERE ARE
STATISTICAL METHODS FOR
HUNDREDLING THIS, THERE ARE
METHODS LIKE ANOVA, THERE ARE
METHODS LIKE [INDISCERNIBLE]
DIFFERENCE, THERE ARE METHODS
LIKE FREED BAN'S TEST WHICH WAS
DEVELOPED IN 1937.
SO 15 WE HAVE A LONG HISTORY OF
STATISTICAL METHODS FOR DOING
THESE SORTS OF THINGS SO WHAT I
WOULD LIKE TO SEE IN PAPERS IS
PEOPLE SHOWING STATISTICS AND
SHOWING THAT THEIR METHODS ARE
ACTUALLY BETTER IN A SIGNIFICANT
FASHION.
SO, YOU KNOW I WANT TO -- I'VE
WRITTEN A LOT ABOUT THIS IN THE
PAST BUT I WANT TO CONCLUDE WITH
SOMETHING POSITIVE BECAUSE SO
FAR I'VE BEEN THIS GIVING--YOU,
OLD ANYWAY A SAYING YOU KIDS
IMET OUT OF MY YARD SO I THINK
WE HAVE COUPLE OF EFFORTS
UNDERWAY THAT I THINK WILL
HOPEFULLY ADDRESS THE PROBLEM.
SO I'VE BEEN WORKING WITH A
GROUP OF PEOPLE FROM ACROSS
BIOTECH AND LARGE PHARMA AND
WE'VE BEEN PUTTING TOGETHER A
SET OF GUIDELINES FOR MODEL
EVALUATION AND THERE HAVE BEEN
REALLY 2 DANCHIBLE OUTCOMES FROM
THIS, AND THE FIRST IS POLARIS,
SO THERE'S A WEBSITE CALLED
POLARIS HUB.IO WHERE WE ARE
HOSTING DATA SETS BUT NOT ONLY
ARE WE HOSTING DATA SETS, WE'RE
PROVIDING RECOMMENDATIONS ON
THOSE DATA SETS, AND WITHIN
POLARIS RIGHT NOW THERE ARE 2
APPROVED DATA SETS SO HOPEFULLY
THERE WILL BE A LOT MORE COMING
VERY SOON EMPLOY THE OTHER THING
THAT WE HAVE IS A MANUSCRIPT
LAYING OUTSETS OF STATISTICAL
METHODDINGS FOR COMPARING
MACHINE LEARNING MODELS IN DRUG
DISCOVERY ACCOMPANYING THIS
MACHINEUE SCRIPT IS A SET OF
JUPIT OR NOTEBOOKS OF CODE
WALKING YOU THROUGH AND SHOWING
YOU HOW TO DO ALL OF THIS.
THE MANUSCRIPT IS NOW, GOING
THROUGH THE APPROVAL PROCESS, AT
ALL THE COMPANIES OF ALL THE
AUTHORS, I AM EXTREMELY HOPEFUL
THAT BY THE END OF THIS MONTH,
THIS WILL BE AVAILABLE ON
ARCHIVE, WHAT WE WOULD LIKE TO
DO IS PUT THIS OUT THERE FOR A
FEW MONTHS, GIVE THE COMMUNITY
AN OPPORTUNITY TO COMMENT ON IT,
AND THEN, YOU KNOW, OUR HOPE IS
TO THEN PUBLISH THIS AND HAVE A
SET OF GUIDE LINES THAT CAN BE
ADOPTED BY JOURNALS.
SO, YOU KNOW ULTIMATELY WHAT CAN
WE DO BETTER?
WE NEED TO USE BETTER BENCHMARK
SETS.
I THINK POLARIS CAN BE A HUGE
HELP THERE.
WE NEED TO HAVE GUIDELINES FOR
APPROPRIATE STATISTICAL
COMPARISONS.
IF YOU'RE USING MOLECULE NET OR
THE THERAPEUTIC DATA COMMONS TO
COMPARE METHODS, I WOULD
RECOMMEND THAT YOU STOP DOING
THAT AND GO GET A BETTER DATA
SET.
AND WITH THAT, I AM GOING TO
STOP.
>> THAT WAS AWESOME PAT, NOT TOO
CRANKY.
I MEAN YOU BROUGHT IT UP AT THE
END.
GIVES AUSA LOT TO TALK BCH I
WILL POINT OUT HERE FOR THE
SPEAKERS, THERE ARE SOME
QUESTIONS YOU MIGHT WANT TO
ANSWER IN THE CHAT BY NOT JUST
LIVE BUT BY TYPING, SO WITHOUT
FURTHER ADO BECAUSE I WANT TO
MAKE SURE YOU HAVE ALL OF YOUR
MINUES, RUSS, SO THANK YOU PAT,
I WANT TO INTRODUCE PROFESSOR
RUSS ALTMAN FROM STANFORD
UNIVERSITY.
HE IS PROBABLY IN MANY DIDN'TS
BUT I KNOW HIM MOST IN THE
DEPARTMENT OF BIOENGINEERING AND
HE'S DONE TONS AND TONS OF
AMAZING THINGS AND A LOT ON
LOOKING AT DRUG ACTION ACROSS
SCALES.
AND I'LL JUST HAND IT OVER TO
YOU.
>> CAN YOU HEAR ME OKAY?
>> YEAH, YOU SOUND GREAT NTHANKS
TO THE ORGANIZERS FOR INVITING
ME, WE'RE DOING A LOT OF STUFF
ON THE TECHNICAL SIDE LOOKING AT
POCKETS AND BINDING AND I'M NOT
GOING TO TALK ABOUT THAT TODAY,
I WILL TALK ABOUT A CRAZY THING
THAT WE'RE ALWAYS DOING, WHICH
IS LARGE LANGUAGE MODEL
SOPHISTICATED TRPT REGULATORY
SUBMISSION, EVALUATION, BY
REGULATORS, LIKE AT THE FDA, SO
I THINK WE ALL KNOW, OR YOU
WON'T BE SURPRISED TO LEARN THAT
MANY OF THE SUBMISSIONS THAT ARE
NOW COMING IN TO THE FDA, ARE
PARTIALLY WRITTEN BY LARGE
LANGUAGE MODELS, RIGHT IN PEOPLE
ARE USING THEM, IT'S NOT -- IT'S
TOTALLY ALLOWED AS LONG AS SOME
HUMAN TAKES RESPONSIBILITY FOR
THE TEXT THAT'S GENERATED AND IT
CAN ACTUALLY BE A PRODUCTIVITY
IMPROVEMENT.
WELL, I'M INVOLVED IN AN FDA
SUPPORTED TRUSTEES OF
EXCELLENCE, AND WE GOT A REQUEST
FROM THE FDA ABOUT BUILDING A
LARGE LANGUAGE MODEL, YOU KNOW
NOT TO MAKE DECISIONS ABOUT YOU
TO BE KIND OF A CO PILOT FOR
REGULATORS AS THEY LOOK AT NEW
SUBMISSIONS, AND SO THIS IS
PRETTY EXCITING, IT DOES RAISE
THE POSSIBILITY THAT AI WILL BE
WRITING THE SUBMISSIONS AND THAT
AI WILL BE EVALUATING THE
SUBMISSIONS, YOU KNOW WELCOME TO
THE NEW WORLD.
I HOPE THE AI DOESN'T KILL TOO
MANY HUMANS.
SO I WANT TO TELL YOU ABOUT THAT
PROJECT WHICH IS REALLY VERY
MUCH IN PROGRESS AND I'M NOT
GOING TO BLOW YOU AWAY FOR A
SYSTEM THAT'S READY FOR YEEL
TIME USE BUT I WANT TO TELL YOU
HOW IT'S WORKING AND THIS IS
WORK OF A GREAT Ph.D. STUDENT
IN MY LAB, BETTY [INDISCERNIBLE]
WORKING WITH A COMPUTER
SCIENTISTS AT STANFORD AND
COLLABORATORS AT THE FDA.
SO THE SPECIFIC USE CASE WE'RE
BELLING FOR IS WITH THE OFFICE
OF GENERIC DRUGS WHERE WE'RE
LOOKING AT NEW GENERIC DRUG
APPLICATIONS WHERE, YOU KNOW
IT'S A TYPICAL CASE, THE DRUG
HAS -- THE ORIGINAL DRUG HAS
GONE OFF PATENT.
A NEW COMPANY WANTS TO SELL A
COPY AND THEY NEED TO PROVE THAT
IT'S EQUIVALENT.
SO THIS IS A VERY FOCUSED CASE,
BUT WE'RE VERY AWARE THAT IF WE
CAN DO THIS, WE MIGHT BE ABLE TO
GENTLEMENNIZE TO OTHER, TO
SUPPORTING OTHER REGULATORY
DECISIONS, SO OF COURSE YOU WANT
THE PK, FORMA CO DYNAMICS AND
ABSORPTION DISTRIBUTION
METABOLISM AND EXKRIEWGZ TO BE
EQUIVALENT.
SO SO YOU CAN MAKE A DECISION ON
WHETHER THIS NEW DRUG WILL BE
APPROVED.
AND THEY SEE AN OPPORTUNITY AND
WE SEE AN OPPORTUNITY TO BUILD A
CHAT BOT AS A CO PILOT AS I SAID
BEFORE FOR REVIEWERS AT THE
OFFICE OF GENERIC DRUGS.
JUST ANSWERING QUESTIONS, THESE
APPLICATIONS CAN BE HUNDREDS OF
PAGES AND YOU MIGHT WANT TO SAY
DID THEY STUDY IMPACT ON
PREGNANCY, HOPEFULLY THEY DIDN'T
IN THIS CASE UNLESS THERE WAS AN
ISSUE IN THE ORIGINAL DRUG.
THERE'S A LOT OF QUESTIONS CAN
YOU ASK AND REQUIREMENTS.
WE CANNOT TRAIN THIS ON
PROPRIETARY DATA BECAUSE THEY'RE
NOT ALLOWED TO GIVE US
PROPRIETARY DATAY SOPHISTICATED
WE HAVE TO FIND PROXIES FOR DRUG
DISCUSSIONS, FOR EXAMPLE,
JOURNAL ARTICLES ABOUT THIS KIND
OF EVALUATION AND EXISTING DRUG
LABELS WHICH IS A BIG 1 I WILL
GET TO.
EEIVETUALLY THEY WILL BE OPEN,
CHATGPT WILL NOT WORK BECAUSE
THEY CAN'T SEND REGULATORY
SUBMISSIONS FROM A COMPANY OVER
THE INTERNET TO A THIRD PARTY.
THAT'S NOT ALLOWED.
AND THE FDA DOESN'T HAVE 10 OR
20,000 GPUs SO WE HAVE TO
BUILD A SMALL MODEL THEY WOULD
BE ABLE TO USE, THIS IS VERY
MUCH A PROTOTYPE PROJECT TO SEE
HOW CAN YOU DEAL WITH THESE
CONSTRAINS AND YOU CAN BUILD
SOMETHING USEFUL.
SO THE MAIN IDEA OF THIS PRONG
ECTOMYOSIN IS DO SOMETHING
OTHERS HAVE DONE THIS, IS NOT
OUR IDEA, AND IT'S CALLED A
DISTILLED MODEL AND THE IDEA IS
THAT YOU USE A LARGE AND CAPABLE
TEACHER MODEL SUCH AS GPT 4.0
WHICH IS FROM OPEN AI, TO
DISTILL KNOWLEDGE ABOUT HOW TO
READ A DRUG APPLICATION INTO A
SMALLER, MUCH SMALLER STUDENT
LANGUAGE MODEL THAT ONLY CAN
TALK ABOUT DRUGS, DRUG LABELS,
AND DRUG FEATURES.
IT CAN'T ANSWER QUESTIONS ABOUT
THE HISTORY OF THE WORLD, IT
DOESN'T KNOW WHO WON THE
SUPER BOWL, IT HAS NO IDEA AT
MANY THINGS BUT IT'S PRETTY GOOD
WITH DRUGS BECAUSE YOU HAVE
QUERIED GPT4.0000S AND THOUSANDS
OF TRIEMS AND USE THOSE QUESTION
AND ANSWER PAIRS TO FINE TUNE A
PUBLIC DOMAIN MUCH SMALLER
MODEL.
SO LLAMA 3.1 WITH 8 BILLION
PARAMETERS, BELIEVE IT OR NOT 8
BILLION IS A VERY SMALL MODEL.
SO THAT WILL BE OUR STARTING
MODEL.
A BASE LINE AND WE WILL TRY TO
SEE HOW GOOD CAN WE GET IT AT
UNDERSTANDING THE CONTENTS OF
DRUG LABELS.
SO IT TURNS OUT THAT THE REVIEW
PROCESS IS WELL REPRESENTED, THE
KIND OF LANGUAGE THAT YOU SEE IN
APPLICATIONS IS VERY WELL
REPRESENTED IN THE KIND OF
LANGUAGE THAT WINDS UP IN DRUG
LABEL ANDS THAT SHOULDN'T BE A
SURPRISE BECAUSE THOSE DRUG
LABELS ARE LITERALLY TIMES
WRITTEN BY THE PHOBE WHO IS
SUBMITTED THE DATA IN THE FIRST
PLACE.
THESE ARE ALL PUBLICLY
AVAILABLE.
2000 OR SO OR MORE DRUG LABELS
AND YOU CAN SEE THE PICTURE AT
THE BOTTOM RIGHT WHERE WE HAVE A
BIG MODEL LIKE CHAT GPT, AND I
WILL SHOW YOU CHAT GPT IS PRETTY
GOOD AT READING A DRUG LABEL AND
ANSWERING QUESTIONS ABOUT IT AND
THEN WE DO THIS KNOWLEDGE
TRANSFER MODEL INTO A SMALLER
MODEL AND THAT'S WHAT WE'RE
TRYING TO DO.
AND I WANT TO TELL YOU ABOUT
SOME STUFF AND I WANT TO TALK
ABOUT WHAT WE'VE DONE.
FIRST THING IS THESE LABELS ARE
PDFs, I DON'T WANT TO BORE YOU
BUT WE HAVE TO TAKE THE PDF AND
TURN IT INTO TEXT.
WE DO THAT.
IT'S CALLED A MARK DOWN AND THEN
WE NEED TO FIGURE OUT WHERE OR
HOW ARE WE GOING TO ASK
QUESTIONS OF THESE LABELS SO
THAT'S JUST A PICTURE, BY THE
WAY, I LOVE THIS PICTURE BECAUSE
1 OF THE THINGS IN IN LABEL IS
THE BLACK BOX WARNINGS, WELL
GUESS WHAT, A LOT OF TIMES WHEN
YOU MARKET DOWN TO ASCII TEXT,
YOU LOSE THE BOX, SO YOU HAVE TO
FIGURE OUT HOW TO RECOVER BIG
IMPORTANT WARNINGS THAT WOULD
HAVE BEEN IN A BLACK BOX WITH
PDF, IN A BOX ABOUT TEXT BECAUSE
IT'S NOT ALWAYS LABELED AS SUCH.
SO WE'RE DEALING WITH UP THING
SAYS LIKE THAT.
NOT EXCITING BUT CRITICAL TO GET
IT RIGHT.
WE ALSO KNOW THERE ARE ABOUT 9
OR 10 THINGS THAT ARE VERY
IMPORTANT TO EXTRACT ACCURATELY
FROM A LABEL AND ALSO FROM A
TRUG APPLICATION.
AND THOSE WOULD BE THE
INDICATION, IF THERE IS A BOXED
WARNING, THAT'S WHYY WOO CARE
ABOUT THE LAST SLIDE, TEAM
CENTER FOR EXCELLENCE ON AGINGS
AND METABOLISM, HALF LIFE,
MAXIMUM DAILY DOSE.
THERE ARE A LOT OF OTHER THINGS
BUT THESE ARE VERY TYPICAL
QUESTIONS THAT YOU WILL ASK
ESPECIALLY FOR GENERIC DRUG.
SO WE CALL THESE ATOMIC QUERIES.
I WILL SHOW YOU HOW WELL CHATGPT
DOES AT ANSWERING THESE
QUESTIONS, BUT EVEN IF WE COULD
BUILD A MODEL OF THAT, THE
REVIEWERS WANT TO ASK OF A PILE
OF DATA AND TEXT AND A LOT OF
QUESTIONS YOU COULD ASK OF A
DRUG LABEL SO WE'VE COME UP WITH
CLEVER WAYS TO DO THAT WHICH I
WILL SHOW YOU RIGHT NOW.
WHICH IS WE ARE USING THE
ABILITY OF CHATGPT, TO LOOK AT A
DOCUMENT, WE GIVE IT SAMPLE
QUESTIONS, THE DOCUMENT IS A
DRUG LABEL, WE GIVE IT 9 OR 10
QUESTIONINGS ABOUT INDICATION,
BOXED WARNING, BIOAVAILABILITY
AND THEN WE TELL IT GENERATE A
BUNCH OF QUERIES OF THIS LABEL
SIMILAR TO WHAT WE JUST SHOWED
YOU BASED ON CONTENT AND THIS IS
CALLED SELF-INSTRUCTION BECAUSE
IT'S GOING TO GENERATE THESE
QUERIES AND THEN WE'RE GOING TO
ASK IT TO ANSWER THE QUERY AND
JUST TO GIVE YOU THE BOTTOM LINE
AND THEN WE WILL ASK IT TO GRADE
ITS ANSWER.
AND BELIEVE IT OR NOT IT'S QUITE
GOOD AT ALL 3 OF THOSE.
SO FOR THE FIRST 1, HERE IS SOME
-- THESE ARE ALL QUESTIONS
CREATED BY CHATGPT BASED ON THE
LABELS.
THE THING OF THE LEFT THE COLON
IS OUR LABEL OF WHAT KIND OF
QUESTION IT IS SO THAT FIRST 1
WE SAID, THIS IS A QUESTION
ABOUT ADVERSE REACTIONS BUT
AFTER THE COLON THAT'S ALL FROM
CHATGPT, WHAT ARE THE ADVERSE
INTERACTIONS OF THE DRUG?
WHAT OTHER DRUGS IRPT ACT WITH
THE DRUG?
CONTRA INDICATIONS WHAT ARE THE
CONDRAINDICATIONS OF THE DRUG?
SO WE CREATED FROM MY STUDENT
BETTY, AND SHE CREATED THOSE
QUESTIONS AND SHE COMPARED THE
CHATGPT TO HER GOLD STANDARD
ANSWERS.
THEN SHE ASKED CHATGPT TO USE A
SIMILAR RUBRIC TO COMPARE HER
ANSWERS WITH ITS OWN ANSWERS AND
THIS IS WHERE I HAVE TO REMINE
THAT YOU CHATGPT IS NOT A HUMAN
AND HAS NO PRIDE IN ITS ANSWERS
AND MANY OF YOU MAY HAVE
EXPERIENCED THIS, BUT IT WILL
GIVE YOU AN ANSWER AND YOU CAN
SAY, DO YOU THINK THAT'S A GOOD
ANSWER AND IT'LL SAY NO, THAT'S
NOT A GOOD ANSWER.
SO WE TOOK ADVANTAGE OF THAT TO
SAY, HERE'S THE QUESTION YOU
CAME UP WITH, HERE'S THE ANSWER
YOU CAME UP WITH, IS THIS A GOOD
QUESTION, THAT'S THE RUBRIC.
CORRECT.
THE OUTPUT MATCHES WHAT BETTY
DID EXACTLY AND IT LEARNED WHAT
CORRECT IS AND NOW IT CAN DO
CORRECT FOR THINGS THAT BETTY
DIDN'T DO, EXTRA COME WHICH
MEANS IT CONTAINED THE ANSWER
BUT IT WAS TOO WORDY AND
CONTAINED A LOT OF EXTRA THINGS
WE DON'T NEED.
INCOMPLETE, WHICH IS OPPOSITE,
IT WAS PARTIALLY CORRECT, OR
INCORRECT, THE OUTPUT DOES NOT
MATCH THE GOLD STANDARD AT ALL.
SOW WHAT WE HAVE NOW IS HOW IT
PERFORMS AT KUFULT ORDER OF
MICRONS ANSWER AND EVALUATION.
SO THE GREEN ARE CORRECT, BETTY
THINKS THEY'RE CORRECT, CHAT GPT
THINKS THEY'RE CORRECT AND THEY
ARE CORRECT AND YOU CAN SEE FOR
EXCRETION, RENAL, LIVER, GI,
FOOD EVENS PRETTY GOOD.
HALF LIFE PRETTY GOOD AND THAT'S
BECAUSE IT TIP KACCT WILY SAYS
THE HALF LIFE OF THIS DRUG IN A
HUMAN WITH HEALTHY NORMAL LIVERS
AND BLOOD AND IT'S GOOD AT THAT.
IT'S PARTIAL CREDIT ON THE REDS
AND ORANGES BUT I'M FOCUSING ON
THE GREENS, SO CAN YOU SEE ON
THE RIGHT SIDE, CHATGPT IS
PRETTY GOOD AND IF WE GENERATE
10S OF THOUSANDS OF EXAMPLES OF
THESE, WE HAVE SOME EXPECTATION
THAT OUR DISTILLED MODEL WILL BE
GOOD.
THE PROBLEM IS ON THE LEFT.
CHATGPT IS FIGURE THE OUT WHAT
TO DO WITH THE LEFT.
WE LOSE THE BOX, WE GET IT WRONG
AND WE'RE WORKING ON THAT AND
WE'RE WORKING ON, WHEN I SAY
WORKING, IT MEANS WE'RE WRITING
THE SPOKE OF PROMPTS TO THE LLM
TO HELP IT GET A BETTER ANSWER.
AND THEN BIOAVAILABILITY WAS
ACTUALLY SURPRISINGLY NOT VERY
GOOD.
SO THOSE ARE THE 1S WE LED IT
WORK ON BEFORE WE LET IT LOOSE.
I WANT TO SHOW YOU THIS BECAUSE
CHATGPT OUT OF THE BOX IS PRETTY
GOOD BUT IT NEEDS HELP WITH THE
NOW ONCE WE GET THAT IN SHAPE, I
CAN TELL YOU THE NEXT STEPS AND
I CAN BE QUIET, AND WE WILL
REFORM OUR AND WE WILL SCALE TO
THOUSANDS OF DRUG LABELS TO GET
TONS OF THOUSANDS OF QUERIES,
ANSWERS, AND EVALUATIONS EMPLOY
IT TURNS OUT WHEN IT DOES
POORLY, IT CAN BE USED IN
TRAINING.
IT'S RELATED TO PAT'S TALK,
NEGATIVE EXAMPLES BY THE WAI
THAT'S A BAD SORRY, THAT'S
USEFUL FOR TRAINING THE LLM.
SO WE WILL GIVE THAT TO THE
MODEL.
WE WILL AS I SAID FINE TUNE
LLAMA 3.1.
WHEN WILL MODIFY THE WEIGHTS, WE
WILL GIVE IT THE DOCUMS THAT THE
LABELS, THE QUESTIONS, THE
ANSWERS AND THE KIND OF
CONFIDENCE IN THE ANSWERS, AND
THEN WE WILL DO THE REAL, THIS
IS WHERE THE RUBBER WILL HIT THE
ROAD FOR A SUBSET OF A COUPLE
HUNDRED OF THESE, WE WILL GET
THROUGH OUR COLLABORATION WITH
THE FDA, FDA REVIEWERS TO
ACTUALLY GRID, THE RESULTS ON
NEW LABELS THAT HAVEN'T BEEN
SEEN BEFORE TO SEE HOW GOOD IT
IS.
AT THAT POINT, WE WILL PROBABLY
DELIVER IT TO FDA INTERNALLY FOR
THEM TO NOT EXPOSE IT TO DRUG
LABELS, BUT TO EXPOSE IT TO
REGULATORY SUBMISSIONS, AND THEY
WILL TELL US HOW WE DID, AND
THEN THEY'LL TELL US WHAT WE
HAVE TO FIX IN AN ITERATED
VERSION, SO, NO TIME SOON WILL
YOUR DRUGS BE APPROVED OR NOT
APPROVED BY AN LLM BUT VERY
EXCITING TO BE WORKING WITH OUR
COLLEAGUES IN REGULATORY SCIENCE
TO SEE HOW WE CAN -- MIGHT BE
ABLE TO HELP THAT PROCESS BE A
BIT MORE EFFICIENT AND PERHAPSIC
TAKEN--THEYING THE BEST OF THE
REVIEWERS HUMAN CAPABILITIES AND
THE BEST OF THE LLM'S
CAPABILITIES.
SO LET ME STOP THERE AND THE
OFFICE OF COLLABORATION AND
EVERYBODY WHO COLLABORATED AND I
THINK WE WILL HAVE QUESTIONS
LATER.
THANKS.
NTHAT WAS GREAT.
THAT WAS QUICK AND I THINK
THAT'S OKAY, SHOULD WE JUST MOVE
ON TO JOEL NOW I THINK SO
BECAUSE THEN WE CAN HAVE MORE
TIME FOR DISCUSSION.
SO JOEL, IF YOU'RE READY, I SEE
YOU MOVING YOUR CAMERA, THANK
YOU RUSS THAT WAS TERRIFIC.
I LOVE SORT OF THE BREDTH OF
WHAT WE'RE TALKING ABOUT HERE.
OKAY,IOLE I DON'T SEE YOUR FACE
BUT IS IT JUST ME, OH THERE YOU
ARE, YOU ARE SHARING YOUR
SCREEN, OKAY.
LET ME GO AHEAD AND INTRODUCE
YOU QUICKLY, SO JOEL KARPIAK, IS
THE DESIGN HEAD AT GSK, FOCUSING
ON COMPUTATIONAL CHEMIST RADIOY
AND PROTEIN DESIGN, WORKED ON
DRUG DISCOVERY, DONE A LOT WITH
GPC Rs WHICH IS REALLY COOL
AND I THINK IS NOW WORKING
PROBABLY PROBABLY WITH
INTEGRATING ARTIFICIAL
INTELLIGENCE FOR DRUG DORPHY AND
INDUSTRIAL APPLICATIONS, MAYBE,
BUT ANYWAY, TAKE IT AWAY.
>> YEAH THANKS.
A WIDE VARIETY OF THINGS LEADING
THE DATA AND PREDICTIVE SCIENCES
ORGANIZATION HAS BEEN
INTERESTING RECENTLY.
I WANT TO TALK ABOUT THE COST OF
A VARIETY OF THINGS AND HOW WE
CAN ALL WORK TOGETHER AS A TEAM
FOR TRANSLATION AND REFLECTING
ON WHAT TRANSLATION MEANS IN A
VARIETY OF DIFFERENT CONTEXT,
IT'S KIND OF IMPORTANT I THINK
AS WE'RE ACTUALLY IMPACTING HOW
WE ACCELERATE OR MAKE DRUG
DORPHY A BIT MOREI FICIENT.
SO JUST BEING ABLE TO TALK TO
EACH OTHER AND SHARE THE SAME
KIND OF VOCABULARY AROUND WHAT A
DATA SCIENTIST DOES, THE
ENGINEERING NEEDED FOR AN ACTUAL
COMPUTE CLUSTER, WHAT A
COMPUTATIONAL HAS FOR DESIGN OR
WET LAB ENABLED TO TEST IT, HOW
DO WE CONTRIBUTE TO EACH OTHER'S
DISCIPLINES, HOW DO WE IMPACT
AND COMMUNICATE WHAT WE'RE
ACTUALLY TRYING TO PREDICT, GET
THOSE SPRPTS DONE AND INFLUENCE
EACH OTHER TO WORK MORE
EFFICIENTLY.
EVEN SOMETHING VERY SIMPLE IN
TERMS OF HOW DID DNA CORRELATE
TO RNA WHICH TRANSLATES INTO
PROTEINS.
YOU KNOW PEOPLE CAN TALK ABOUT
YEENS AS TARGETS, VERY DIFFERENT
THAN AND THEREFORE A COMMON GOAL
AND ALSO TRANSLATION TERMS OF
PROTEIN TARGET AND THEN FINDING
A BINDER TO THAT IS DIFFERENT
THAN BEING EFFICACIOUS IN A
MODEL SYSTEM AND CERTAINLY
DIFFERENT THAN EFFICACIOUS IN
THE HUMAN SYSTEM, SO HOW DO WE
LINE UP MODELING TO IMPACT THESE
IN ANENTIOUS FICIENT WAY.
AND I SHOULD SAY THIS SLIDE IS
REALLY MESSY ON PURPOSE NOT JUST
BECAUSE I WEAR POWER POINT
SKILLS BUT BECAUSE THIS IS A
REALLY MISSING AREA THAT NOT
ANYBODY HAS RIGHT.
ALTHOUGH COMPUTERS THROUGHOUT
THIS ENTIRE SESSION CAN SPEAK
ALL OF THESE DIFFERENT
LANGUAGES, WE DON'T YET HAVE
COMPLETELY AUTONOMOUS AGENTS
DOING EVERYTHING POSSIBLE,
HUMANS ARE STILL IN THE LOOP,
STILL NEED TO MAKE DECISIONS AND
PRIORITIZATIONS AND HOW DO WE CO
THAT.
SOW WHEN -- SO WHEN WE START OE
HAVE INFORMAT 8 -- INFORMATICI,
AND WE DON'T HAVE ANY KNOWLEDGE
GAPS OR WHITE SPACE BECAUSE
WE'RE DOING THINGS IN A MODULAR
WAY BUT TRYING TO HAVE THE
SEAMLESS DATA FLOW SO THAT WE'RE
GENERATING DATA AND MAKING
PREDICTIONS THAT IMPACT AND
LOOKING AT IT FROM A GLOBAL MIND
SET WHERE FOUNDATIONAL MIND SET
BUILDING UP THOSE DATA SETS AS
WE'VE TALKED ABOUT PREVIOUSLY
THROUGHOUT THIS WORKSHOP.
IMH REALLY THIS IS SEAMLESS
INTEGRATION BETWEEN BESPOKE,
DATA SCIENTISTS, AUTOMATION,
DOING IT AT SCALE AND THE
QUALITY TO MAKE PREDICTIONS AND
ALL OF THIS HAS UNDERPINNINGS OF
TRANSPARENCY AND TRUST WITHIN
THE TEAMS SO WE KNOW IT'S GOING
ON, PEOPLE UNDERSTAND WHERE
PREDICS ARE COMING FROM AND
INTERPRET THEM IN THE BEST WAY
TO MAKE THE BEST KINDS OF
DECISIONS.
SO YOU'VE HEARD A LOT ABOUT
DIFFERENT KINDS OF MACHINE
LEARNING AND DRUG DISCOVERY AND
THINGS LIKE THIS, THERE'S A WIDE
VARIETY OF COMPUTATIONAL IMPACT.
SO I WANT TO FOCUS ON WHAT
HAPPENS IN SMALL MOLECULE
DESIGN, TO PUT ALL THESE THINGS
TOGETHER.
SO THAT'S A ROUND GENERATING
DATA, HOW DO WE DO IT
INTENTIONALLY, HOW DO WE
INKERPERATE IT INTO MODELS AND
DEPLOY THOSE IN RESPONSIBLE WAY
FOR PEOPLE TO BE MABEL TO MAKE
GOOD DECISIONS AND A BIT OF A
SAND BOX ENVIRONMENT AND
INTERPRET WHAT WE'RE TRYING TO
SAY INTO THE EXPERIMENTAL
PRABLGHTIS EMPLOY SO A LOT OF
THIS GOES INTO A BIT OF A
FRAMEWORK AT GSK,ERB HAS THEIR
SHOP FOR.
SO STARTING WITH THIS AND FOR
THE REST OF THE TALK, IN
PRINCIPLE THERE'S REALLY NOTHING
DIFFERENT BETWEEN SMALL
MOLECULES AND LARGE MOLECULES OR
OLIGOS OR ANYTHING ELSE WE MIGHT
BE INTERESTED INY DID SIGNING,
JUST HAPPENED TO SELECT THIS TO
GET INTO THE BIT OF THE
NITTY-GRITTY ABOUT HOW TEAMS
WORK.
SO HERE WE'VE GOT SORT OF ALL
THE COMPONENTS WE HEARD ABOUT
ALREADY THROUGHOUT THESE
SESSIONS IN EMERGING ITS OF
MOLECULE GENERATORS, MODEL
CLICKS, THAT HAVE A VARIETY OF
DIFFERENT AMOUNT OF DATA BEHIND
THEM TO ENABLE THE QUALITY MODEL
BUILDING AND ALSO SELECTION OR
ULTIMATELY PRIORITIZATION OF
MOLECULES TO MAKE AND HOW DO WE
DO THAT ON AUTOMATED CHEMICAL
PLATFORMS AND THE EMPHASIS IN A
WAY THAT REALLY DRIVES THE
PROGRAM FORWARD AND WITH THE
MAXIMAL AMOUNT OF IMPACT AND
GENERATEDDA THE EACH STAGE.
SO I WANT TO KIND OF GIVE A
COUPLE OF EXAMPLES AROUND WHAT
THIS MIGHT LOOK LIKE.
SO THE VARIETY OF DIFFERENT
TYPES OF DATA ARE GOING TO BE
IMPORTANT FOR THE KINDS OF
MODELSY WANT TO BUILD BUT
ON-TARGET, OFF-TARGET AND HOW DO
WE DEPLOY THOSE FOR BETTER
DECISIONINGS.
IT COULD BE DATA IMAGES, OR IT
COULD BE ANYTHING, SO HAVING A
FLEXIBLE SYSTEM FOR MODEL AND
DEPLOYMENT IS IMPORTANT BUT
STANDARDIZATION ABOUT HOW WE
BUILD THOSE MODELS AND BEING
ABLE TO SELECT THE VAST 1S THAT
ARE GBLIZABLE GOING FORWARD.
AND SO, I JUST WANT TO MAKE
ANOTHER COMPARISON TO A BIT, --
FROM A VARIETY OF SCREENING
CASCADES WHICH IS SIMILAR ACROSS
THE INDUSTRY, WHAT'S PUBLISH IS
IT'S TYPICALLY POSITIVE SO BEING
ABLE TO ESTABLISH CAREFUL 3
CURATION ESTABLISH BALANCE DATA
SETS FOR MODEL BUILDING IS
PRETTY IMPORTANT AND AGAIN I
WANT TO EMPHASIZE THE IMPORTANCE
OF BEING ABLE TO INCORPORATE
THAT OPEN SOURCE DATA GOING
FORWARD.
SO, JUST SOME THINGS TO TAKE
INTO CONSIDERATION AGAIN BECAUSE
THIS IS A THEME.
IN TERMS OF EVALUATING
EXPERIMENTAL DATA FOR MODEL
BUILDING, THIS REQUIRES REALLY
CLOSE COLLABORATION IN
COMMUNICATION WITH EXPERIMENTAL
SCIENCES TO MAKE SURE YOU'RE
INTERPRETING EXPERIMENTAL DATA
TO BUILD THE BEST KINDS OF
MODELS, SO WITHIN THE
EXPERIMENTAL ASSAYS, IT COULD BE
[INDISCERNIBLE] MEASURE WANTS
WHICH IMPACT THINGS, ERROR
PREDICTION, IMBALANCED DAILY
BASIS AT SETY DID PENDING ON
THE RESULTS, AND THE FACT THAT
YOU'RE SIMPLIFYING THE
PRESENTATIONS AND HOW YOU BUILD
THE MODEL AND THE QUALITY OF IT.
THE ABILITY OF THE MODEL TO
EXTRAPOLATE BASED ON DATA AND
TRAINING SET.
ULTIMATE UNCERTAINTY IN THE
MODEL AND HOW WE QUANTIFY THAT
AND THEN ALSO BUILD AN
INTERPRETIVE METHOD IF WE CAN
FOR EXPERT ANDS NONEXPERTS TO
MAKE DECISIONS OFF OF AND
HAPPENED WHAT'S GOING ON UNDER
THE HOOD.
AND OVER TIME SURPRISINGLY AS
CHEMIST RADIOY CHANGES THE
PROGRAM EVOLVES THE ABILITY FOR
ANY KIND OF SCAFFOLD OR GROUPS
WILL SCRIEWBT KNCHTLY SO THERE'S
A DEPENDENCY IN ORDER TO
GENERALIZE, MISSING DATA SETS,
SPARKS MATRIXES, TONS OF
PROBLEMS YOU HAVE TO TAKE INTO
CONSIDERATION WHEN WE LOOK AT IT
TO DO THIS IN A RIGOROUS WAY.
SO BEING ABLE TO THEN BOTH AT A
GLOBAL LEVEL, BUILD DATA SETS
INTENTIONALLY TO BE ABLE TO
BUILD THE BEST KIND OF MODELS
THAT EXTRAPOLATE ACROSS CHEMICAL
SPACE WHILE AT THE SAME TIME
HAPPENING THE LIMITATIONS ON A
POTENTIAL SERIES THAT YOU'RE
WORKING ON AND THEN NEED TO
BUILD A MODEL TO FILL THE 1
SPACE AND ALSO COMPUTE IT TO
NATURAL PROJECT THAT YOU'RE
WORKING ON IN ORDER TO JUST
DRIVE IT.
SO BEDDING WHETHER OR NOT
THERE'S GLOBAL MODELS ARE
SUITABLE, IF YOU NEED TO BUILD A
LOCAL MODEL AND PUSH IT MORE, OR
IF YOU CAN GENERATE DATA IN
ORDER TO MAKE BOTH BETTER AT THE
SAME TIME AND WHAT ARE THE
STRATEGIES IN ORDER TO DO THAT
EMPLOY I SHOULD SAY, JUST TO
GIVE A SCALE ON THE KIND OF DATA
AND MODELS NECESSARY FOR DRIVING
THESE KINDS OF PROJECTS FORWARD
TO GIVE AN EXAMPLE HERE FOR
OFFTARGET ASSAY PANEL, LOTS OF
NASTY THINGS YOU WANT TO AVOID
FOR VARIOUS VENE REASONS AND
COUNSEL STREAM CENTER FOR
EXCELLENCE ON AGINGS, HERE WE'RE
SHOWING A SAMPLING OF 98 MODELS
BASED ON A PANEL OF TARGETS,
THAT'S PRETTY STANDARD.
AND IN SHOWS DIFFERENT LEVELS OF
MODEL CALLING BASED ON THE DATA
THAT EXISTS AND WHETHER OR NOT
WE CAN IMPROVE THAT WITH THE
INGTENSIONAL DATA GENERATION
GOING FORWARD.
SO AGAIN JUST TO GIVE A SENSE OF
SCALE OF WHAT NEEDS TO BE DONE
IN ORDER TO DESIGN A GOOD
MOLECULE AND THESE SORT OF DATA
GAPS WE HAVE IN AND AROUND THAT.
AND GETTING BACK TO TEAM WORK
AND COLLABORATION, TALK ABOUT
WORKING WITH THE
EXPERIMENTALISTS IN ORDER TO
INTERPRET THE DATA CORRECTLY FOR
MODEL BUILDING NOW IT'S ABOUT
INTERPRETING MODEL BUILDING
CORRECTLY AND DEPLOYING THOSE
MODELS FOR EXPERIMENTALISTS OR
CHEMISTS WHOMEVER ON THE OTHER
SIDE TO BE ABLE TO USE
APPROPRIATELY.
UNDERSUPER VISION AND SAND BOX,
DESIGN AND HOW DO WE POOL ALL OF
THOSE IDEAS TOGETHER TO MAKE THE
BEST DECISIONS.
SO 1 POTENTIAL WAY TO DO THIS IS
THROUGH A SYSTEM FOR INSTANCE
THIS IS THE COMMERCIAL 1 WITH
THE BY DESIGN, WHERE IT'S A
CLOUD BASED SYSTEM, CLOUD BASED
PLATFORM DESIGN THAT'S
ACCESSIBLE, YOU CAN DEPLOY
MODELS THROUGH IT, GET IMMEDIATE
FEEDBACK ON DIFFERENT DESIGN
IDEAS AND INCORPORATE ANYTHING
ELSE THAT YOU WANT TO IN ON
ORDER TO ACTUALLY PUSH THESE
KINDS OF PROJECTS ALONG.
SO FOR INSTANCE YOU CAN BUILD
CUSTOM TEMPLATES FOR USERS TO BE
ABLE TO LOOK AT DATA IN A
CERTAIN WAY AND MAKE SURE IT'S
INTERPRETED IN A CERTAIN WAY AND
INCORPORATE ALL OF THOSE OTHER
KIND OF END POINTS IN HERE THAT
MIGHT BE IMPORTANT.
SO NOT JUST A PREDICTION BUT THE
UNCERTAINTY RELATED TO IT, HOW
IT SHOULD BE INTERPRETED AND THE
APPROPRIATE VIECIALIZATIONS IF
NECESSARY IN ORDER TO ACTUALLY
PUSH THINGS ALONG.
AND DEPENDING ON THE THINGS YOU
MIGHT BE INTERESTED IN, THERE
WOULD BE A PARTICULAR VIEW FOR
SAFETY OR OFFTARGET EFFECTS,
ONTARGET POTENCY ANYTHING YOU
MIGHT BE INTERESTED IN CAN BE
DEPLOYED HERE.
SO THIS IS REALLY A WAY TO HAVE
TRANSPARENCY ESPECIALLY WITH
COMPUTATIONALLISTS AND HOW THEY
MAKE DECISIONS TO DESIGN THE
TEST CYCLE AND AGAIN JUST TO
GIVE A SENSE OF SCALE, SO, ON
EACH PROJECT WE HAVE OVER 150
GLOBAL MODELS THAT ARE
CONTINUOUSLY REBUILT THAT ARE OF
VARIOUS DIFFERENT PROPERS THAT
WOULD BE INTERESTING OR
IMPORTANT FOR ACTUALLY DRIVING
THE PROGECS FORWARD IN THAT
PARTICULAR SERIES AND THEN
UNSURPRISINGLY, MANY CUSTOM
MODELS DEPENDING ON THE ACTUAL
SERIES OR THE TARGET SPECIFIC
THAT CAN BE DEPLOYED THROUGH
HERE AND THIS IS ALWAYS A
COLLABORATIVE EFFORT FOR
INITANCE WITH DMPK WHERE OTHER
ASSAY SCIENTISTS ARE ABLE TO DO
THIS.
SO AGAIN, MANY COOKS IN THE
KITCHEN IN ORDER TO TRY TO DO
THIS, BUT THE CLOSE
COLLABORATION BETWEEN THE
DIFFERENT FUNCTIONS AND THE
TRANSPARENCY AND THE TRUST THERE
IS ABSOLUTELY ESSENTIAL FROM THE
BEGINNING SO WE TRY TO DO THAT
BOTHOT EXPERIMENTAL AND ON THE
COMPUTATIONAL SIDE.
AGAIN, ANOTHER ASPECT OF
TRANSPARENCY IS AUTOMATED MODEL
TRIP FOR AT LEAST THESE TYPES OF
GLOBAL ASSAYS OR GLOBAL
PROPERTIES THAT WE MIGHT BE
INTERESTED IN, ANYBODY AT ANY
TIME CAN LOOK AT HOW THAT MODEL
IS PERFORMING WITH AN
AUTOMATED SYSTEM FLAGGING IF IT
STARTS TO GET MORE COMPOUNDS
MADE AND SUGGIEST A THAT THE
MODEL NEEDS TO BE REMADE OR WE
CAN CONSIDER AN ACTIVE LEARNING
STRATEGY TO MAKE IT BETTER.
BUT AGAIN, IN THE INTEREST OF
TRANSPARENCY, THESE ARE THINGS
THAT ANYBODY CAN LOOK @ ANY 1
TIME EVEN THOUGH THERE'S AN
AUTOMATED SYSTEM WITH FLAG
ISSUES.
JUST TO GROUND IT IN REALITY A
BIT, FOR INSTANCE 1 PROJECT JUST
TO GIVE AN EXAMPLE OF HOW THIS
WORKS ON INDIVIDUAL PROPERTIES
AND THEN ALSO HOW IT COULD WORK
IN COMBINATION, SO HERE THERE IS
AN OFFTARGET LIABILITY HERE FOR
A PARTICULAR SERIES ON A PROJECT
AND IN ORDER TO GET AROUND IT WE
HAVE FOCUSED DATA GENERATION,
BASED ON THE MODEL THAT THE
FLOABAL 1 IS NOT TRANSLATING AND
SO BEING ABLE TO DECREASE THAT
OVER TIME THROUGH THESE
DIFFERENT KINDS OF ACTIVE
LEARNING ITERATIONS WAS PRETTY
KEY TO THE PROJECT AND AT THE
SAME TIME PROVIDING THE
VISUALIZATIONS AND
JUSTIFICATIONS FOR WHY WE NEED
TO DO THIS.
SO FOR INITANCEOT RIGHT SHOWING
THE GLOBAL MONO PREDICTIONS WERE
CLUSTER INDEED A SPACE THAT WERE
NOT APPLICABLE, TO THE LOCAL
THINGS THAT WERE NOT INTERESTED
IN YOU CAN SEE IN THE FIRST 2
COLUMNS.
CHEMISTRY'S VERY DIFFERENT.
SO THINGS LIKE THIS ARE PRETTY
ESSENTIAL FOR COMMUNICATION
ABOUT PRIORITIZATION,
EXPERIMENTS THAT NEED TO BE DONE
AS OPPOSE TO JUST SAYING IT OR
TAKING SOMEBODY'S WORD FOR IT.
YOU KNOW IT'S A CENTRAL PART OF
THE PROJECT TEAM MOVING FORWARD.
AND SO LOOKING AT THIS, IS MORE
THAN AN INDIVIDUAL PROPERTY, SO
LOOKING BACK AT THAT OFF TARGET
I MENTIONED BEFORE VERSUS
ON-TARGET POTENCY, BEING ABLE TO
DECREASE OR TUNE OUT THE
ACTIVITY THAT WE DON'T WANT,
WHILE KEEPING THE POTENCYOT
ACTUAL TARGET AT THE SAME TIME.
SO USING THAT EXAMPLE IS JUST AN
OFFTARGETS, IT'S A PRETTY GOOD
PROOF OF CONCEPT AND GETTING
BACK TO HOW WE ACTUALLY DO THIS
IN A PRACTICAL WAY, WOWRKING ON
MULTIPLE SEARS AT A TIME FOR
INSTANCE, WE TRY TO MAP OUT
GETTING BACK TO THAT DATA
STRATEGY AND BEING INTENTIONAL
ABOUT WHAT WE'RE DOING, MAP OUT
HOW LONG THESE CYCLES ACTUALLY
TAKE BASED ON THE CHEMISTRY
WE'RE INTERESTED IN AND STAGGER
THE CHEMISTRY RESOURCE WITH THE
SCREENING RESOURCE, WITH THE
COMPUTATIONAL RESOURCE ALL AT
THE SAME TIME SO THIS PROCESS
CAN BE ASENTIOUS FICIENT AS
POSSIBLE WHILE GENERATING THE
MAXIMUM AMOUNT OF IMPACTFUL DATA
WE CAN DO AT THE SAME TIME SO
THIS IS PRETTY MUCH ESSENTIAL
WHEN MAINTAINING A LARNG
PORTFOLIO OF PROGECS AS OPPOSE
TO A START UPPER SOMETHING.
WE REALLY FOCUSED ON 1 TARGET
AND PUTTING ALL THE RESOURCES
BEHIND IT.
SO I SHOULD SAY TOO, THIS IS
DONE ON A LARGER SCALE.
SO NOT SURPRISINGLY, WHEN
STARTING A PROJECT YOU WANT
THINGS THAT ARE SOLUBLE AND SO
FOR AN ORAL DRUG FOR INSTANCE
AND AN EASY WAY TO DO THAT IS
TAKE GENERAL GUIDE LIVENS ABOUT
WHAT'S GOD THERE, TRY TO DESIGN
THEM FOR THE FULTERS OR
MULTIPROPERTY OPTIMIZATION
TECHNIQUES, AND DO THOSE OVER
TIME, THAT'S GREAT.
RIGHT?
WE CAN DO THAT AND THIS SHOWS AN
EXAMPLE ABOUT DIFFERENT KINDINGS
OF PROPERTIES THAT HAVE BEEN
IMPORTANT BASED ON THE SERIES
AND FLAGS OVER A VARIETY OF
ITERATIONS AND OVERTIME EMILY
THE ON A PARTICULAR PROJECT.
BUT ON UNSURPRISINGLY, WHAT THIS
DOES IS IT POTENTIA WILY GIVES
YOU G GOODISH COMPOUNDS.
IF GENERAL BUT THERE MIGHT BE
SOMETHING SPECIFIC FOR YOUR
PARTICULAR TARGET, FOR YOUR
PARTICULAR DISEASE OR FOR YOUR
PARTICULAR MECHANISM OF ACTION,
THAT IS IMPORTANT SO
UNDERSTANDING WHAT A GOOD
MOLECULE OR CANDIDATE MOLECULE
IN THAT PARTICULAR THING IS
POSSIBLE AND IMPORTANT TO SAVE
YOU FROM MAKING MODELS YOU DON'T
HAVE AND MAKING IT BETTER FOR TO
YOU HAVE THIS.
IT ALSO HELPS SET UP
MULTIOBLIGATIONS YECTIVE OPTICAL
IMAGES MIERSATION THAT AT THE
SAME TIME THAT ARE SPECIFIC TO
YOUR DISEASE OR EVEN YOUR
SERIES, OR MODALITY AT THE SAME
TIME.
AND SO 1 POTENTIAL WAY TO DO
THAT, IS ACTUALLY GOING AND
TAKING THIS FROM A CLINICAL
MODELING AND GOING BACK TO
TRANSLATION WHICH IS THESE
COURSE MECHANISTIC MODELS, AND
QUANTITATIVE SYSTEMS
PHARMACOLOGY, AND WHERE EACH OF
THE DISEASES WHERE YOU KNOW FOR
INSTANCE BY THE TIME YOU HAVE A
CANDIDATE AND HOW THESE ARE
FORMED HOW THESE ARE PERFORMED
IN IN VIVO, OR MAYBE EVEN HAVE
CLINICAL DISEASE THAT YOU'VE
BEEN INVESTING IN OVER TIME OR
BOUGHT THAT DATA FOR INSTANCE
AND THEN ALSO UNDERSTAND THE PBK
PROPERTIES FOR THE SERIES AND
AND SO WHAT WORKING BACKWARDS
FROM THOSE, GOING BACKWARDS FROM
THE CLINIC, SO A REVERSE
TRANSLATION APPROACH, CAN YOU
PREDICT THESE ADME PROPERTIES
WITH REALLY GOOD MODELS, SO
UNDERSTANDING HOW THE INDIVIDUAL
COMPONENTS OF PERMIABILITY,
SOLVABILITY, CLEARANCE,
BIOAVAILABILITY, ALM THESE
THINGS THAT ARE IMPORTANT
INDIVIDUALLY HOW THEY MAY
COMBINE INTO THE PROPERTIES AND
THEY MAY TRANSLATE INTO THESE TO
BE EFFICACIOUS IN HUMANS.
SO BEING ABLE TO TAKE THEY--THE
KRETICALLY BEING ABLE TO TAKE
THESE MODELS OF THESE MOLECULES
THAT WERE INTERESTED IN, AND
THEN AGAIN WORK BACKWARDS SO,
BEING ABLE TO VIRTUALLY PROFILE
HUNDREDS OF THOUSANDS OF
POTENTIAL MOLECULES, BED THE
DRIVERS OF THE EFFICACY THAT ARE
CONTRIBUTING TO THE PK, SO THAT
COULD BE SOLIEWBILITYD, COULD BE
PERMIABILITY OR COULD BE A
REALLY COMPLICATED KRIEWK OF ALL
OF THEM INTO ONCE BUT USING
DIFFERENT KINDS OF MACHINE
LEARNING, AND DESIGN STRATEGY
SHOULD BE GOING FORWARD, AND FOR
THE NEXT ENUMERATION, YOU CAN
BUILD BETTER MODELS AND KEEP
ITERATING THROUGH THE SERIES SO
IT'S NOT JUST MAKING OR PLAYING
WHACK-A-MOLE, AND SAYING I WANT
TO OPTIMIZE PERMIABILITY BUT
LOOKING LARGELY IN A TRANSLATION
POINT OF VIEW, HOW DO WE DO
HUMAN DOZE THAT WE'RE TRYING TO
OPTIMIZE AS EARLY AS POSSIBLE.
YOU CAN HAVE 2 DIFFERENT
COMPOUNDS LABELED A AND B HERE,
BUT DIFFERENT BALANCE OF AND
THIS IS THE PROBABILITY OF WHAT
THE HUMAN DOSE MIGHT END UP
LOOKING LIKE, KIND OF HELPS MAKE
IT CLEAR ABOUT WHAT THE DESIGN
STRATEGY SHOULD BE AND WHAT
SERIES WE MAYBE WANT TO
PRIORITIZE WITH MORE RESOURCE
GOING FORWARD, SO DO WE THINK
IT'S EASIER TO OPTIMIZE POTENCY
BUT KEEP ADME THE SAME, IF WE
REDUCE IT BY 5 X, WE CAN
INCREASE THE PROBABILITY OF A
LOWER DOSE MUCH MORE OR IS IT
GOING TO BE HARDER TO OPTIMIZE
THE ADME PROPERTIES FOR INSTANCE
AND THEN WE USE AND BREAK DOWN
WHICH PHYSICAL PROPERTIES
CONTRIBUTE TO THE OVERALL ADME
SCORE TO OPTIMIZE FOR FURTHER
DESIGN ROUNDS.
AND I SHOULD SAY TOO, THERE ARE
OTHER KINDS OF PROPERTIES THAN
THAT IMPACT THAT KIND OF ARK
SUSMGZ WHERE THERE MIGHT BE
BIOLOGICAL VARIABLES WE DON'T
UNDERSTAND, WHETHER IT'S A NEW
DISEASE OR SOMETHING LIKE THIS
SO WE CAN MAKE ASSUMPTIONS AND
SEE HOW THEY LOOK OVER TIME, SO
THE PLOTS ON THE RIGHT ARE
DIFFERENT LEVELS OF EFFLUX RATIO
IN THE SAME PLOT AND SHOW HOW
THAT CHANGES TIME AND THE
PROBLEMMABILITY OF SUCCESS MIGHT
CALL OUT AN IMPORTANT PARAMETER,
FOR INSTANCE, THAT WE NEED THAT
WE NEED TO PRIORITIZE WITH
EXPERIMENTAL TESTING TO
UNDERSTAND WHAT OUR COMPOUNDS
ARE DOING WHETHER OR NOT A
SERIES IS WORTH PURSUING.
SO IT HIGHLIGHTS BASICALLY THE
THINGS WE DON'T KNOW AND SHOULD
PRIORITIZE THE RESOURCE AROUND
CERTAIN EXPERIMENTS THAT ARE
CLEAR NO GOES FOR THE PROJECT OR
FOR THE PARTICULAR SERIES MUCH
SAME SORT OF THING JUST AROUND
TURNOVER RATE OF THE ACTUAL
TARGET IN A PD SENSE AND SO YOU
CAN HAVE THE SAME MOLECULES IN
TERMS OF POTENCY, ADME PROPERTY
ANDS THINGS LIKE THIS BUT
DEPENDING ON WHAT THE ACTUAL
PROPERTIES OF THE MOLECULE AND
THE TARGET ARE?
IT COULD HAVE 0 PERCENT CHANCE
OF SUCCESS OR CLINICAL
EFFICACIOUS RESPONSE RATE, OR IT
COULD HAVE A 50% CHANCE OF
SUCCESS SO BEING ABLE TO
HIGHLIGHT THE PROPER EXPERIMENTS
TO DO TO INTERPRET TO TELL YOU
WHAT TO DO NEXT, IS IMPORTANT
FOR PRIORITIZING RESOURCE AND SO
GOING BACK TO COMPOUND DESIGN,
WHERE THIS COULD COME INTO PLAY
ESPECIALLY AS EARLY AS POSSIBLE,
WHERE YOU WANT TO IT TOO, IS
TAKING INITIAL [INDISCERNIBLE]
SO HERE IN THE MIDDLE IS THAT
RED, IS THAT BLACK BOX, AND
DOING DIFFERENT KINDS OF
MOLECULE GENERATORS, AROUND
THOSE SCAFFOLDS, DIFFERENT
SCREENS, THINGS LIKE THIS TO
UNDERSTAND THE OPTIMIZE ABILITY
OR POTENTIAL OF THAT SERIES TO
REACH ANY PROBABILITY OF SUCCESS
OF BEING EFFICACIOUS OR WITH A
LOW DOSE.
AND SO BEING ABLE TO THEN TAKE
THOSE SERIES LOOK AT THE COMMON
PROPERTIES OF ANY OF THOSE
MOLECULES THAT ARE PREDICTED TO
HAVE A LORER DOSE, UNDERSTAND
WHERE THOSE KEY PHARMA 4S ARE
AND UTILIZING THOSE OR IF WHAT
WE DO, HOW MUCH DESIGN EFFORTS
WE PUT INTO IT IS NEVER GOING TO
BE PREDICTED LOWER THAT KNOW
THAT THAN EITHER WE CAN KILL A
SERIES, WE CAN REPRIORITIZE,
TEST 1 OR 2 THINGS THAT ARE THE
VIRTUAL NAIL IN THE COFFIN TO
MAKE SURE WE HAVE CONFIDENCE IN
THE MODEL PREDICS, IT INFORMS
THE NEXT DESIGN FOR THE DESIGN
STRATEGY.
SO JUST TRYING TO THINK AGAIN
GLOBALLY HOW MODELING AND
TRANSLATE ALL THE WAY FROM THE
VERY BEGINNING TO THE CLINIC AND
FEEDBACK PRACTICES THAT TO
ANSWER QUESTIONS ABOUT HOW DO WE
MODULATE THIS TARGET IN AN
EFFICACIOUS WAY, WHO MODALITY
YOU WANT TO CHOOSE, SO VALENCEY
VERSUS SYNTAX, VERSUS FUNCTIONAL
INHIBITION AND THEN PRIORITIZING
DIFFERENT KINDS OF COMPOUNDS, 1
OF THE GAPS TO BUILD A ABOUT
THER MODEL WE MIGHT NEED OR MAKE
MORE ACCURATE PREDICTIONS AND
PRYERITIZE THOSE EXPERIMENTS AND
THEN AGAIN WHARKS IS ACTUALLY
TRIEFING THE BIOLOGICAL EFFICACY
AND WHAT ARE THE PHYSICAL
PROPHECIES THAT ARE CLEAR FOR US
TO DESIGN TO.
SO WITH THAT I JUST WANT TO SAY
THANK YOU TO EVERYBODY AND WAY
TOO MANY PEOPLE AT GSK AND
ELSEWHERE TO THINK.
SO THIS IS JUST A GENERAL 1.
APPRECIATE IT.
HOPEFULLY I CAUGHT US ON TIME.
THAT.
>> THAT WAS PERFECT.
THAT WAS GREAT.
>> GREAT.
>> AT THIS POINT WHAT WE'RE
GOING TO DO IS IF THE PANELISTS,
THERE'S MARTY AND RUSS IF YOU
COULD COME BACK TO US THAT WOULD
BE GREAT.
HOW ARE YOU DOING, WE WILL JUST
DISCUSS THINGS I HAVE QUESTIONS
THAT I HAVE SORT OF COME UP
WITH, I'M SURE -- AND I -- YOU
KNOW I WANT TO KEEP IT OPEN, WE
DO HAVE SOME SORT OF MORE
TECHNICAL QUESTIONS IN THE Q&A
I'M GOING TO START WITH AND I
WILL INVITE THE UDIENCE, IF YOU
WANT TO PUT QUESTIONS IN, MAYBE
I CAN JUST START IT OFF THOUGH,
I THINK SO WE HEARD, WE REALLY
HEARD, I THINK LIKE A REALLY
GREAT RANGE OF TALKS, YOU KNOW
TAWRCHING ON MANY DEFINITE
EXCITING THICKS, HAPPENING
INSILICO DRUG DISCOVERY AND
DESIGN.
I'M CURIOUS SORT OF A BASIC
QUESTION OF WHAT ARE YOU MOST
EXCITED ABOUT IN THIS LAND SCAPE
AND I KNOW IT WILL BE DIFFERENT
FOR EVERYBODY BECAUSE WE ALL
HAVE DIFFERENT THINGS WE ALL
LIKE.
PAT, CAN I START WITH YOU SINCE
YOU'RE UNMUTED.
>> SURE.
THE MOST EXCITING PART FOR ME IS
THE PART WE'RE ENCOUNTERING
BETWEEN PHYSICS AND MACHINE
LEARNING.
AND I THINKA BECAUSE OF THAT, WE
CAN DO THINGS AT A MUCH HIGHER
RESOLUTION THAN WE COULD DO
BEFORE, BUT THE THING THAT
EXCITING ME THE MOST IS WE HAVE
THIS PROTEIN STRUCKURE AT 1 END,
YOU HAVE THE ALPHA FOLD
TECHNIQUES AND AT THE OTHER END,
WE HAVE MOLECULAR DYNAMICS AND
IT'S ALL KIND OF COMING TOGETHER
AND I THINK, YOU KNOW IN A
COUPLE OF YEARS, WE'RE ALL GOING
TO BE DOING THESE MULTISCALE
SIMMULESS THAT ARE GOING TO
ENABLE US TO DO THINGS THAT WE
COULD NEVER EVEN THINK ABOUT
DOING.
>> I'M SMILING TO HEAR YOU SAY
THAT, I THINK THE IDEA OF
RUNNING MULTIPLE SIMULATIONS IS
PRETTY COOL.
I HAVE A QUESTION BUT I'M SAYING
IT FOR THE END.
MARTY YOU ARE UNMUTED AND
ANYTHING YOU'RE PARTICULARLY
EXCITED ABOUT?
>> THOSE WHO ATTENDED HEARD SOME
OF WHAT I HAD TO SAY IN THE
KICKOFF SO YOU KNOW A BIT ABOUT
THAT BUT IT ECHOES SOME OF WHAT
PAT'S SAYING, IT ECHOES SOME OF
THE THEMES THAT JOEL WAS PULLING
OUT AT THE END WHICH IS IT'S NOT
JUST 1 HAMMER, I'M ANYTHING GO
OUT AND FIND NAILS TO HAMMER,
IT'S HOW DO I BRING ALL OF THESE
THINGS TOGETHER BOIGHTSD
COMPUTATIONALLY AND IN THE WET
LAB.
SO ON THE MULTIOMICS SIDE, WHAT
ARE THE DATA SETS THAT INFORM US
ACROSS ALL OF THESE THINGS.
AND THEN WHAT ARE THE
COMPUTATIONAL METHODS THAT
INTEGRATE THOSE DATA TOGETHER TO
UNDERSTAND DISEASE BIOLOGY
BETTER AND TO UNDERSTAND WHETHER
OUR MOLECULES ARE IMPACTING THE
SYSTEM IN THE RIGHT WAY AND YOU
WOULD FALL OFF YOUR CHAIR IF I
SAID WE'RE GOING TO DO MORE MD
SIMULATIONS SO I'M NOT GOING TO
SAY THAT.
>> THAT'S GREAT.
YOU CAN TELL I'M EXCITED.
>> SO RUSS OR PAT?
>> THE THEY SAID THE MOST
EFFICIENT PRPLT IS NO EXPERIMENT
AT ALL AND WHAT I'M EXCITED
ABOUT IS THAT WE WILL BE ABLE ON
DO A TON OF PRETTY CHEAP
COMPUTATION, IT'S NOT FREE BUT
COMPARED TO A LOT OF EXPERIMENTS
SO THAT WHEN WE'RE DOING OUR
EXPERIMENTS THEY WILL BE
CONFIRMATORY OF THINGS THAT WERE
PRETTY DARN SURE OF AND I'VE
SEEN IT ALREADY HAPPEN IN SOME
OF OUR WORK WHERE REALLY, WE
WENT TO A MOUSE AND WE IMU WE
HAD TO DO THE MOUSE BUT WE KNEW
IT WAS GOING TO WORK AND WHEN
THAT HAPPENS YOU ARE IN A VERY
GOOD MOOD AND YOU DON'T SPEND A
LOT OF MONEY, SO MAYBE IT WAS
SHERYL WHO SAID IT, I DON'T KNOW
WHO SAID IT, BUT I THINK THAT
THAT'S WHERE WE'RE HEADED AND
THAT'S GOING TO REALLY OPEN UP A
LOT OF OPPORTUNITIES, BECAUSE
YOU REALLY SHOULD DO EVERYTHING
COMPUTATIONAL EVEN WITH THE
AWARDS OF THE COMPUTATION AND WE
KNOW ABOUT THE AWARDS, IN MANY
WAYS IT'S BEEN A 2 DAY MEETING
ABOUT AWARDS BUT YOU SHOULD DO
ALL THAT BEFORE EXPERIMENTS
BECAUSE THEN HAVE YOU SOME
EXPECTATION THAT YOU'RE NOT
DOING REALLY SILLY EXPERIMENTS
THAT ARE NOT WORTH WHILE.
SO I'M EXCITED ABOUT THAT
BECAUSE IN COMPUTER FOLKS ARE
ACTUALLY CONTRIBUTING, GOD FOR
BID.
>> JOEL, HOW ABOUT ANY THOUGHTS
ON WHAT YOU FIND PARTICULARLY
EXCITING.
I LOVED YOUR TALK BY THE WAY
NTHANKS.
YEAH, I THINK THAT THE
ENGINEERING AND TECHNICAL
PLATFORMS ARE CATCHING UP TO THE
COMPUTATION AND WE'RE CO
CREATING THOSE FOR THE SAME TIME
FOR THE TIME, SCALE AND QUALITY
OF DATA AND REPRODUCIBILITY THAT
WE ACTUALLY NEED AND MARTY WAS
TALKING ABOUT JUST THE FACT THAT
WE'RE IN THE SPACE TO INTEGRATE
THESE KINDS OF MULTIMODE AT
TYPES, TO MAKE STUFF THAT REALLY
POWERFUL IN TERMS OF MULTIOMICS,
GENETICS, IME NOAMICS ALL AT THE
SAME TIME TAKEN--THEY MAKE MORE
BRNT DECISIONS THERE, 'S A LOT
TO DO, IT'S EXCITING.
>> YEAH, VERY COOL.
SO A FLIP OF THAT QUESTION IS
THEN, -- OH MY GOD I HAVE SO
MANY THINGS TO TALK ABOUT -- BUT
A FLIP OF THAT QUESTION, MAYBE
YOU EACH TOUCHED ON THIS BUT
JUST SORT OF STEPPING BACK, WHAT
DO YOU VIEW AS THE BIGGEST
CHALLENGE TO YOU KNOW MAKING
PROGRESS, I EMBASSY IN THE
PARTICULAR SPACE OF WHAT YOU'RE
EXCITED ABOUT IN IS IT THE DATA?
ALGORITHMS?
IS IT THE TEAM?
WHAT DO YOU THINK?
>> IF I COULD START FROM
ACADEMIC PERSPECT.
COMPUTE ISICLING US, MY
COLLEAGUES MIGHT HAVE A
DIFFERENT REACTION, BUT I KNOW
THEY HAVE SOME PAIN AS WELL, FOR
THE FIRST TIME IN MY CAREER IS
HOW ARE STUDENTING GOING TO PAY
FOR THE COMPUTE, WE HAVE
CLUSTERS AND WE HAVE SUPER
COMPUTERS BUT WE FIGURED OUT A
WAY BUT I AM CONCERNED AS AN
ACADEMIC ABOUT OUR ABILITY TO
MAKE CONTRIBUTIONS AT SCALE AND
SO THAT'S FINE, WE CAN TRY TO
MAKE OUR CONTRIBUTIONS AND BE
STRATEGIC PLAN TEEJIC BUT I DO
WORRY THAT IT'S FUNNY BECAUSE
YOU'RE TALKING ABOUT THE
EMERGING OF MOLECULAR DYNAMICS
AND AI ML, THE 2 MOST EXPENSIVE
COMPUTE -- LITERALLY THE THINGS
THAT USE --
>> WHY NOT BRING THEM TOGETHER,
RIGHT?
>> AT THE NATIONAL LABS THE MOST
AND WE WANT MORE OF THAT.
AND WE WANT TO DO A CROSS
PRODUCT OF AI AND BY THE WAY, I
TOTALLY AGREE WITH THAT, BUT WE
REAMLY DO HAVE COMPUTE
CHALLENGES, I THINK AND I LOLLED
TO HEAR FROM COLLEAGUES AND
INDUSTRY, THIS THAT'S A PECULIAR
PROBLEM FOR ME.
>> I WOULD SAY RUSS IT'S NOT A
UNIQUE AND PECULIAR PROBLEM FOR
YOU.
I'M GOING TO THROW IN A
NONSEQUITER BECAUSE IT'S ON MY
MIND WHEN WE TALK ABOUT COMPUTE
BUT I'VE HEARD SO MANY PEOPLE IN
THE AI WORLD IN THE PUBLIC
DOMAIN TALKING ABOUT HOW THE
SINGULARITY IS COMING, AND THE
COMPUTERS ARE GOING TO BE ABLE
TO THINK.
YOU CANNOT GENERATE ENOUGH POWER
TO RUN A BIG ENOUGH COMPUTER TO
HAVE THE SINGULARITY HAPPEN.
SO THAT'S CRAZY TALK EMPLOY AND
ROLLING IT BACK TO THE SIMPLER
QUESTIONS HERE, SOME OF OUR
ASPIRATIONS ARE PROBABLY BIGGER
THAN THE COMPUTE THAT WE HAVE IN
HAND RIGHT NOW AND SO, IT IS THE
NEXT BIG INTUITIVE LEAP PROBABLY
DOES NEED OTHER COMPUTE
INFRASTRUCTURES, PROBABLY NEEDS
COMPUTE INFRASTRUCTURES THAT ARE
NOT QUANTUM COMPUTING BECAUSE
THOSE ARE EVEN MORE ENERGY HOGS
THAN THE TOUGH WE'VE RUN RIGHT
NOW.
SO THAT'S PERHAPS A TANGENT, SO
JUST SAYING RUSS I KIND OF AGREE
WITH YOU AND YES, I AM THE
PERSON WHO HAS TO FIGURE OUT THE
BUDGET FOR THE COMPUTE WE WANT
TO DO ACROSS ALL OF THE
DIFFERENT RESOURCES ACROSS ALL
OF THE PROJECTS GOING ON AT
AMGEN SO IT IS A PERENNIAL
PROBLEM AND CONTINUES TO BE 1
EMPLOY I DON'T KNOW THAT IT'S A
SCIENTIFIC PROBLEM BUT IT IS A
LOGISTICAL AND OPERATIONAL
CHALLENGE.
>> I DON'T SEE IT THAT WAY.
>> IT'S INTERESTING.
>> THE VAST MAJORITY OF THE
ESPECIALLY THE MACHINE LEARNING
MODELS I'M BUILDING FOR PROPERTY
PREDICTION, I DON'T NEED THIS ON
MY LAB TOP TO DO A LOT OF THIS,
I KNOW THE BIGGEST THING WE'RE
LACKING IS DATA, YOU KNOW.
IT HURTS ME TO SEE ALL OF THESE
SMART KIDS AT MIT, OUT THERE
BUILDING MACHINE LEARNING MODELS
ON TERRIBLE DATA SETS.
I THINK IF ANYTHING, ESPECIALLY
IN TERPS OF JUST PROPERTY
PREDICTION FOR DRUG DISCOVERY,
THE MOST SIGNIFICANT THING WE
BEING DO IS HAVE A PUBLICLY
FUNDED EFFORT TO GENERATE HIGH
QUALITY DATA, IN A CONSISTENT
FASHION AND MAKE THIS PUBLICLY
AVAILABLE.
>> DON'T DISAGREE ABOUT THE DATA
QUESTION, I WOULD SAY THERE'S A
RANGE OF MODELS THAT ARE
HAPPENING ON MY TEAM AND IF
SOMEONE IS TRYING TO BUILD A
MODEL PROCESSING I BUNCH OF
IMAGES COMING OUT OF SINGLE CELL
EXPERIMENTS AND LAYER ON TOP OF
THAT THE TRANSCRIPTOMICS AND THE
PROTEOMICS THAT BECOMES A BIG
COMPUTE PROBLEM FOR ME.
WHEREAS SOME OF THE OTHER MODELS
ABSOLUTELY, JUST, YOU KNOW LET
ME GIVE YOU MORE MEMORY FOR YOUR
LAPTOP SO THAT IT'S RUNNING
EFFICIENTLY ENOUGH FOR YOU SO
IT'S THAT SPAN, THAT BEING SAID,
YOU ARE EXACTLY RIGHT.
I CAN'T EVEN TRAIN THOSE MODELS
UNLESS I HAVE THE DATA FOR THEM
-- UNLESS I GENERATE THOSE
IMAGES AND I CAN'T IMENERATE
THOSE IMAGES IN THE RIGHT WAY IF
I HAVEN'T PROACTIVELY THOUGHT
ABOUT WHAT'S THE QUESTION I'M
TRYING TO ANSWER, WHAT ARE THE
DATA THAT WILL HELP ME TO ANSWER
IT, AND HOW DO WE CONSTRUCT AN
EXPERIMENT GOING BACK TO SOME OF
MY COMMENTS THE OTHER DAY, THAT
IS AN APPROPRIATE MODEL AT THE
RIGHT LEVEL OF SPECIFICITY FOR
THE QUESTION WE'RE TRYING TO
ASK, SO I AGREE WITH YOU PAT.
I JUST THINK THAT THERE'S SOME
SPAN ACROSS THE KINDS OF COMPUTE
WE DO AND REALLY IF ROMMIE WANTS
TO DO ALL THESE ATOM SIMULATIONS
AND TRAIN A BUNCH OF TOUGH ON
TOP OF IT SHE WILL NEED A BUNCH
OF [INDISCERNIBLE]
>> YEAH IT TAKES A LOT OF BIG
COMPUTE.
JOEL ANY COMMENTS SOME.
>> NO, I WOULD AGREE WITH MARTI,
JUST FROM THE VAST VARIETY OF
STUFF YOU WOULD DO IN TARGET ID,
PRECISION MEDICINE, COMPARED TO
ACTUAL MOLECULE DESIGN, YOU
KNOW, REALLY FAR OUTWEIGHS THE
RESULT AND AS CUTCHES TRANSITION
TO THE CLOUD AND BACK TO A
HYBRID MODEL I DO NOT ENVY HER
FOR TRYING TO FIGURE OUT THE
PUDGEET FOR EVERYTHING NTHERE
WAS TRANSITION OUT OF THAT, YOU
MEAN IN I DIDN'T REALIZE THAT
NYEAH.
>> YEAH, IT'S SOMETIMES CHEAPER
TO DO I THINK THISS BACK ON
PRIMARY REVIEWER AND --
>> SO, SURE, YEAH, SO IT'S LIKE
SIMILAR TO US.
>> OKAY.
>> YEAH, THERE'S ALWAYS GOING TO
BE SECTIONS OF THIS, AND CLOUD
COMPUTING IS COMPUTING IT'S JUST
ON SOMEONE ELSE'S PREMISES.
>> SO IT'S NOT MY PAPER WEIGHT
AT THE END OF IT.
>> RIGHT.
>> SOPHISTICATED A QUESTION,
TOO, SO PAT YOU MENTIONED THAT
YOU KNOW AND IT'S TRUE, THINGS
ARE LIKE, THEY FEEL LIKE THEY'RE
MOVING, YOU SAID A HUNDRED MILES
AN HOUR AND I FEEL LIKE IT'S
FASTER, AND I WAS LISTENING TO A
TALK AND I WAS THINKING HOW IS
THIS HAPPENING ON PLANET EARTH?
HOW DO YOU ALL KEEP UP WITH
THINGS MOVING FAST?
HOW ARE YOU HANDLING IT IN
KEEPING PACE WITH THE ADVANCES
BECAUSE IT'S CRAZY.
>> YEAH, I THINK IT'S -- IT'S
HAVING A GROUP OF PEOPLE WHO ARE
REALLY EXCITED ABOUT THE FIELD
AND WHO ARE CONSTANTLY READING,
GOING TO MEETINGS, TALKING TO
PEOPLE AND THEN HAVING
CONVERSATIONS --
>> FILTERING OUT --
>> YEAH, AMONG THAT GROUP.
YEAH.
BUT IT'S TOUGH.
YOU KNOW BECAUSE THERE'S
PROBABLY MACHINE LEARNING AND
DRUG DISCOVERY PAPERS THAT SHOW
UP ON ARCHIVE EVERY WEEK SO
YEAH, ASTERISKS JUST GETTING
RIDICULOUS TO TRY TO KEEP UP.
>> YEAH, YEAH.
>> ON OUR LAB SLACK WE HAVE AN
INTERESTING PAPERS CHANNEL AND
USED TO TRY TO GIVE A 1 LINER
LIKE AN IMPRESSION OF THE PAPER
TO HELP THE STUDENTS LIKE
PRACTICE THEIR OWN CRITICAL AND
FORGET ABOUT IT, LIKE RIGHT NOW
IT'S A SCROLLING, IT'S A FEED
AND SO FOCUS AND KNOWING WHAT
WERE YOU AWARE TRYING TO DOA
BECOMES MORE IMPORTANT BECAUSE
YOU CAN'T BE JUST OPPORTUNISTIC
BECAUSE YOU WILL BE CONTEXT
SWITCHING EVERY TIN MINUTES SO
YOU HAVE TO HAVE A PLAN, YOU
HAVE TO REALIZE THAT THE PLAN
MAY HAVE TO CHANGE BUT THE FOCUS
HAS TO BE THERE AND THIS IS LIKE
FOR Ph.D. STUDENTS BUT ALSO
FOR START UPS AND FOR BIG PHARMA
THE FOCUS AND THE PLAN, YOU HAVE
TO BELIEVE IN IT AND STICK TO
IT, EVERY NOW AND THEN THERE
WILL BE A VERY DISRUPTIVE
TECHNOLOGY BUT MOST TECHNOLOGIES
ARE ONLY A LITTLE DISRUPTIVE TO
THE PLAN.
BUT SOMETIMES THEY BLOW YOUR
PLAN OUT OF THE WATER AND THAT'S
THE 1 YOU WORRY ABOUT, RIGHT IN.
>> YEAH, THERE WAS A QUESTION
FROM THE AUDIENCE THAT I DID --
THAT I DID EXTRACT, AND I'M
SORRY, I'M NOT -- I'M NOT SURE
WHAT I ASKED IT MIGHT HAVE BEEN
AN ANONYMOUS ATTENDEE WHAT THE
QUESTION WAS WHAT ARE THE
CHALLENGES YOU CAN SEE ON THIS
FIELD AS A YOUNG SCIENTIST?
I CAN PUT ON MY PROFESSOR HAT
AND THE STUDENTS AND DIFFERENT
DISCIPLINES AND THINGS THAT YOU
FEEL LIKE ARE LACKING OR NEED TO
BE ADDRESSED THAT ARE MAYBE A
LITTLE BIT DIFFERENT NOW OR ARE
THERE -- WHAT ARE THE THOUGHTS
ON THAT?
STHRKS 1 THING THAT WORRIES ME
ARE DATA SCIENTISTS THAT ARE NOT
CONNECKED TO A DOMAIN OF
EXPERTISE.
IF YOU WANT TO BE IN THE FIELD,
IT'S GO TO KNOW A LOT OF CHEMIST
RADIOY OR BIOLOGY, AND SOME
PEOPLE ARE BEING SOLD THIS
GENERAL PURPOSE -- I CAN'T
IMAGINE THERE'S ANY DOMAIN WHERE
YOU WILL BE HAPPY HIRING A KIND
OF HIRED GUN DATA SCIENTIST WHO
DOESN'T KNOW ANYTHING ABOUT YOUR
DOMAIN BUT IT'S FOR SURE, I
WON'T SPEAK FOR OTHER DOMAINS IN
BIOLOGY, CHEMIST RADIOY AND
MEDICINE, FOR SURE, YOU'RE NOT
THAT USEFUL IF YOU'RE JUST GOING
TO TURN THE CRANK -- ABOUT
SOMETHING THAT IS IN THE WORMED.
>> RIGHT.
MAYBE COMPLETELY, I THINK, I
ALWAYS KIND OF URGE PEOPLE TO
SIT AT THE CENTER OF A THEN
DIAGRAM CONSISTISTING OF YES,
YOU NEED THE HACKING SKILLS, YOU
NEED TO UNDERSTAND MACHINE
LEARNING, YOU DEFINITELY NEED
THAT DOMAIN EXPERTISE AND THEN
YOU NEED TO KNOW ENOUGH MATH
STATISTICS TO KNOW WHETHER
YOU'RE FOOLING YOURSELF EMPLOY.
>> I KNEW YOU WERE GOING TO SAY
THAT.
>> I'M GOING TO BE HONEST WITH
YOU THAT EVEN AS THE PACE OF
LIFE CHANGES, EVEN AS NEW, AS 6
BILLION PAPERS COME OUT EVERY
WEEK IN ALL OF THAT, THE CORE
SKILLS THAT OUR NEW HIREEES NEED
TO HAVE, HAVEN'T CHANGED ANY,
RIGHT?
I STILL WANT THAT PERSON WHO IS
AN EXPERT IN SOMETHING BUT HAS
THE ABILITY TO LEARN NEW THINGS
AND TO TEACH TO OTHERS.
I STILL WANT THAT PERSON WHO IS
RIGOROUS ENOUGH IN THEIR
THINKING AND NOT FOOLING
THEMSELVES ENOUGH IN
INTERPRETING THEIR RESULTS THAT
THEY WILL EVEN MAKE PAT WALTERS
HAPPY, I STILL WANT SOMEONE WHO
CAN COME IN AND COMMUNICATE
THEIR CONCEPTS AND THEIR DEPTH
OF KNOWLEDGE TO A NONEXPERT
AUDIENCE IN A USEFUL WAY.
SO, AT THE ROOT OF IT, I DON'T
THINK THE CORE BEHAVIORS HAVE
CHANGED AT ALL IN WHAT WE NEED.
THERE'S MAYBE SOME ADDITIONS
AROUND FOR ALL OF US, INCLUDING
-- HERE AROUND HOW DO WE RIDE
THE WAVE OF CHANGE AND MAINTAIN
RESILIENCE?
BUT IT'S THE SAME SET OF SKILLS
THAT I'M LOOKING FOR AND I
COULDN'T AGREE MORE WITH RUSS
AND PAT THAT YOU DO WANT -- YOU
DO WANT TO BE ABLE TO KEEP
CHENLIST RADIOY TO A MEDICINAL
CHEMIST IF YOU WANT TO DO THAT.
>> JOEL, YOU UNMUTED YOURSELF
WHAT DO YOU THINK?
>> I MEAN I WOULD AGREE WITH
EVERYBODY ESPECIALLY RESONATE
WITH WHAT MARTI WAS SAYING
AROUND THE CORE SKILLS WE STILL
SEE THE COMPUTATIONAL SCIENTISTS
BEING THAT TRANSLATOR BETWEEN
THE DEEP, DEEP, DATA ENGINEERS
AND TECHNICAL EXPERTISES TO BE
ABLE TO COMMUNICATE REQUIREMENTS
AND THEN ALSO WORKING WITH THE
AUTOMATION TEAMS, WORKING WITH
THE ACTUAL EXPERIMENTAL
SCIENTISTS AND REALLY BEING THAT
KIND OF HUB, SO IF YOU DON'T
KNOW SOMETHING THAT YOU'RE
TALKING ABOUT, THAT WILL BECOME
OBVIOUS AND THAT'S A PRETTY POOR
FIT FOR THE JOB.
AND TO GET TO THE POINT AROUND
SORT OF THE GENERAL DATA
SCIENTISTS AND MACHINE LEARNING
TESTIFIES I CAN'T COUNT THE
NUMBER OF TIMES WHO DEPLOYED
MODELS AND ASKED THEM WHAT THE
IMPACT OF THAT WAS, AND THEY
CAN'T TELL ME BECAUSE THEY WERE
JUST TOLD TO BUILD THE MODEL AND
DON'T CARE ABOUT WHAT HAPPENS
AFTERWARDS SO THE PEOPLE WHO
HAVE THE SUBJECT MATTER
EXPERTISE OR AT LEAST THE
AGILITY TO TALK TO PROJECT
MANAGER TEAMS AND TALK ABOUT
DRUG DISCOVERY IS, AND TO
UNDERSTAND THE ROLE AND MAKE AN
IMPACT IN THINGS TOTALLY KEY TO
SUCCESS.
>> YEAH, YEAH, I AGREE.
SO KEEPING MY PROFESSOR HAT ON,
MAKING DRUG SYSTEM NOT REALLY
THE PURSUE OF ACADEMIA, RIGHT WE
CAN CONTRIBUTE TO LITTLE BITS OF
THIS BUT THAT'S AN INDUSTRIAL
SCALE THING, YOU KNOW IN THE
PAST, I MEAN TYPICALLY ACADEMICS
MIGHT BE DEVELOPING NEW METHODS,
RIGHT, DOING MORE SORT OF SLOWER
LONG TIME KALE PROGEC TYPE WORK,
MAYBE FOCUSING ON LEARNING
FUNDAMENTAL BIOLOGY, YOU KNOW,
REGARDING METHODS I HAD AN
INTERESTING SORT OF TWITTER CHAT
WITH BJ [INDISCERNIBLE].
WHO WAS MAKING THE ARGUMENT THAT
WAS SO MUCH GREAT METHOD STUFF
HAPPENING IN INDUSTRY YOU SHOULD
COME OVER BECAUSE YOUR BUDGET'S
GOING TO BE BIGGER AND YOU COULD
DO MORE THINGS WHICH I THOUGHT
WAS REALLY INTERESTING BUT SO I
JUST WANTED TO ASK LIKE SO HOW
DO YOU VIEW THE ROLE OF ACADEMIA
IN THIS SPACE.
ARE THERE PARTICULAR THINGS YOU
LOOK FORWARDS US FOR OTHER THAN
THE PIPELINE OF LIKE YOUR NEXT
TEAM OR IN TERPS OF LIKE METHODS
AND THOUGHT DIRECTION?
OR NO?
>> I THINK IN ESPECIALLY IN
TERMS OF MACHINE LEARNING, I
LOVE THE FACT THAT ACADEMIA IS
DOING A LOT OF FUNDAMENTAL
RESEARCH, YOU KNOW PEOPLE ARE
DOING RESEARCH ON THINGS LIKE
HOW CAN I USE A DIFFUSION MODEL
TO GENERATE A MOLECULE?
BUT THEN, YOU KNOW, I FEEL LIKE
WE IN INDUSTRY HAVE A
RESPONSIBILITY TO TRY THAT OUT
AND PROVIDE FEEDBACK AND 1 OF
THE THINGS I TROO TO DO AT LEAST
IF SOMEBODY WILL PUBLISH A PAPER
AND THEN THEY WILL HAVE A GET--I
REPOE WITH THEIR CODE I THINK
IT'S UNDUM BENT ON US THEN TO
SAY THAT'S GREAT.
LET'S TRY THAT OUT AND MAYBE WE
CAPTAIN GIVE YOU BACK THE EXACT
STRUCTURES BUT MAYBE WE CAN GIVE
YOU BACK ADVICE ON HOW YOU CAN
MAKE THIS MORE PRACTICAL USE
GOING FORWARD.
>> I DO THINK THERE'S SOME OF
THE INNOVATION AROUND
EXPERIMENTAL TECHNIQUES THAT
GENERATE CERTAIN KINDS OF DATA
THAT THEN CAN FEED MODELS WHERE
THERE'S A BIG ASPECT OF THAT.
THAT FOSTERS IDEAS INSIDE OF
ACADEMIA, THAT WE DON'T
NECESSARILY HAVE THE DAY IN AND
DAY OUT TIME TO DO IN INDUSTRY.
>> YEAH, OR MAYBE THE PARTICULAR
EXPERTISE AND SO FORTH WITH
DIFFERENT MICROSCOPIES AND SO
ON.
>> AND SO --
>> I WOULD SAY THERE'S -- NI
DIDN'T MEAN TO INTERRUPT, JOEL,
YOU GO AHEAD.
>> NO, NO, I WAS GOING TO GO --
I FEEL LIKE I'M AGREEING WITH
YOU A LOT MARTI, AND ALSO EXPABD
ON THE ACADEMIA MODEL ISN'T ALSO
JUST LIKE, OH PARTNERSHIP WITH 1
LAB, RIGHT IN THERE'S ALL KINDS
OF CONSORTIA OPINION AN ACTUAL
UNIVERSITY WHERE WIEWR ALL
WORKING TOGETHER AND
CONTRIBUTING DIFFERENT METHODS
THAT MAY BE, YOU KNOW, A
PARTICULAR COMPANY WOULD WANT TO
INVEST IN THAT, BECAUSE THE
GROUP THING THAT YOU'RE PUTTING
TOGETHER.
SO I THINK THERE'S A BUNCH OF
DIFFERENT MODELS, ESPECIALLY AS
TECHNOLOGY GETS BIGGER AND AS
RESEARCH GETS MORE
COLLABORATIVE.
>> ALL RIGHT, COOL.
LET'S SEE, WHAT ELSE.
ON TEAMS.
IT'S EVIDENT, DRUG DISCOVERY IS
A HUGE TEAM, IT'S TEAM SCIENCE
ACROSS THE BOARD.
DO YOU HAVE AND THAT'S SO
IMPORTANT AND IT'S SO HARD, IT'S
NOT ALWAYS THAT PEOPLE COME
TOGETHER AND THEY GEL IN A WAY
WHERE EVERYBODY'S RUNNING IN THE
SAME DIRECTION, IF YOU CAN GET
THAT, CULTIVATE THAT IN ANY
ORGANIZATION I THINK WHETHER
IT'S ACADEMIC, LAB, OWN GROUP,
WHOLE PLACE, YOU KNOW IT CAN BE
AWESOME, DO YOU HAVE THOUGHTS OR
ADVICE FOR FOLKS, LIKE WHAT YOU
LEARNED ABOUT MAKING THOSE
STRONG TEAMS?
>> WOULD YOU LIKE TO TALK FOR
SEVERAL HOURS ON END ON THIS
TOPIC?
[LAUGHTER]
I JUST THINK IT'S SUCH AN
IMPORTANT TOPIC.
MANY OF YOU HAVE HEARD ME WHINE
ABOUT THAT'S WHY I CAME BACK TO
INDUSTRY IS BECAUSE I MISSED
WORKING ON INTERDEPENDENT TEAMS
THAT COULD RELY ON EACH OTHER
AND WERE RELIABLE FOR EACH
OTHER.
>> TRUST.
>> THAT IS THE FUNCTION THAT
NEEDS TO HAPPEN.
ONE OF IT IS JUST A MIND SET
THING.
I HAVE SOME OF THEM IN THE ROOM
WILL HAVE HEARD MY ANALOGY
BEFORE OF WAVE PARTICLE DUALITY
AS THE WAY WE SHOULD APPROACH
OUR LIFE, AND BEING MINDFUL
ABOUT WHEN I'M A PARTICLE
CONTRIBUTING MY EXPERTISE VERSUS
WHEN I'M A MEMBER OF A TEAM
INTEGRATING TOGETHER.
I ALSO THINK IT'S IMPORTANT AND
MOST COMPANIES SPEND A LOT OF
TIME ON SKILLS BUILDING OR
BUILDING TEAMS, HOW CAN I BE
AWARE OF MY PREFERENCES, MY
WIMBERLYS OF PAIRING MYSELF IN
THE WORLD, HOW AM I AWARE OF
THAT IN RELATIONSHIP TO THE
OTHER PEOPLE AROUND ME AND THEN
BEYOND THAT SKILLS TRAINING
AROUND WHAT ARE ORGANIZATIONAL
AND PROCESS TOOLS WE CAN USE TO
MAKE SURE THAT OUR MATRIX TEAMS
KNOW WHAT THE GOAL IS WHAT
THEY'RE CONTRIBUTING AND HOW IT
WINDS UP TOGETHER AND SO FORTH.
AND SO, IT REALLY IS A TOPIC
THAT I COULD TALK ON FOR WAY TOO
LONG, SOIME GOING TO SHUT UP AND
TURN IT OVER TO MY COLLEAGUES
PAT AND JOEL IN PARTICULAR SINCE
WE COME FROM AN INDUSTRY
PERSPECTIVE ABOUT HOW YOU THINK
IT AFFECTS TEAMS?
PAT YOU'VE BEEN IN SEVERAL
SIZES, JOEL, YOU'VE BEEN IN THE
MONSTROSITY OF GSK ALL YOUR LIFE
SO HOW DOES IT PLAY OUT?
>> I AGREE WITH EVERYTHING MARTI
SAID, THERE'S SO MUCH THAT'S
COMMUNICATION, IT'S ALL ABOUT
APPRECIATION, IT'S ABOUT
CRAFTSMANSHIP AND APPRECIATING
THE CRAFTSMAN SHN OF OTHERS BUT
I THINK MORE THAN ANYTHING, AT
LEAST FOR ME, IT'S BEEN JUST
TRYING TO FIND PEOPLE WITH THE
RIGHT MIND SET AND BUILDING THE
RIGHT TEAM.
AND TRYING TO BUILD A TEAM OF
PEOPLE WHO NOT ONLY APPRECIATIVE
OF OTHER ASPECTS BUT HIRING
ENTHUSIASTIC PEOPLE.
HIRINGMEDICINAL CHEMISTS WHO ARE
ENTHUSIASTIC ABOUT PHARMACOLOGY.
IT'S GREAT IF YOU CAN CREATE
THIS SITUATION WHERE YOU HAVE
PEOPLE WHO ARE EAGER TO LEARN
FROM 1 ANOTHER, TO ME THAT'S
WHERE THE MAGIC IS.
>> YEAH, I WOULD AGREE AND IT'S
HARD TO DO, I FEEL LIKE IT'S A
PARTICULAR CHALLENGE FOR
ACADEMIA BECAUSE WE HAVE A BIT
MORE TURNOVER, ALSO, SO THE
TEAMS BECAUSE WE'RE TRAINING AND
PEOPLE LEAVE, BUT THEN AGAIN, I
RECOGNIZE THAT PEOPLE DO LEAVE
INDUSTRY AND SO FORTH, BUT,IOLE,
SORRY, I SHOULD LET YOU SAY
SOMETHING EMPLOY >> I WOULD
OBVIOUSLY AGREE WITH PAT AND
MARTI'S EXPERIENCES, IT STARTS
AT HIRING, LOOKING AT PEOPLE
SPECIFICALLY WITH THOSE KINDS OF
QUALITIES AS OPPOSE TO LETTING
SMK WHO DOESN'T HAVE THOSE
VALUES AND FIGURE OUT WHAT DO I
DO WITH THEM NOW AND BRING THE
WHOLE TEAM DOWN, SO WE TRY TO
SCREEN FOR THOSE LIABILITIES
UNSURPRISINGLY BUT BEING
INTENGAL ABOUT HIGH PERFORMING
TEAM EXERCISES REALLY
UNDERSTANDING PEOPLE'S
COMMUNICATION STYLES.
ABSOLUTELY THE INVESTMENT IS
THERE AND SO AGAIN GETTING BACK
TO ADVISE FOR YOUNG PEOPLE INTO
THE FIELD, BEING ABLE TO
COMMUNICATE AND GET ALONG WITH
OTHERS IS JUST AS IMPORTANT AS
YOUR TECHNICAL KNOWLEDGE.
>> I WOULD ALSO AGREE WITH JOEL
ON THAT 1 AS WELL, I WOULD SAY
THE REWART RE-- REWARD STRUCTUE
AWARD STRUCTURES IN ACADEMIA ARE
DISINCENTIVE, I KNOW, I KNOW
SEVERAL.
>> RUSS ANY COMMENTS?
>> IT WASN'T LIKE THE STAR
SYSTEM WHERE IT WAS ABOUT
INDIVIDUAL STARS WHO WERE -- AND
THEN A STAR IN THE MIDDLE AND
THEN THAT WAS ALL OUT THE DOOR
AND IT WAS ALL ABOUT THE MISSION
OF THE COMPANY, AND THAT STAGE
IT WAS JUST VERY INVIGORATING
AND I SAID TO MYSELF, FOR THE
FIRST TIME IN 35 YEARS BECAUSE I
REALLY, I NEVER -- I NEVER HAD A
REAL JOB, NOW I SEE WHY BEING IN
INDUSTRY CAN BE VERY SATISFYING
BECAUSE I WAS ON THEEZ
FUNCTIONAL TEAMS WHERE THERE
WASN'T TOO MUCH BS, WE WERE
LUCKY POLITICS HADN'T OCCURRED
IN THE COMPANY AND THEY WERE ALL
ROW NOTHING THE SAME DIRECTION,
I SAID THIS IS WHY IT'S COOL
THEY WILL GET STUFF DONE AND
THEY DID, THAT WE WOULD NEVER BE
ABLE TO DO IN ACADEMIA.
WELL THAT'S 1 THING MARTI CAN
TALK ABOUT FOR A LONG TIME AND
PUBLISHING AND EVERYTHING, AND I
WILL JUST MAKE A COMMENT BECAUSE
I WANT TO MAKE SURE WE STILL
HAVE TIME.
WE WILL DO A COUPLE MORE
QUESTIONS BUT DURING OSKS, ALSO
FOR ME, THAT WAS A TIME, TOO
WHERE WE HAD THIS SHEDDING WELL,
EVERYWHERE BUT ALSO IN ACADEMIA,
THE SHEDDING OF ALL OF OUR KIND
OF LIKE NORMAL ROUTINES AND SO
PEOPLE WERE SHARING AND TALKING
AND YOU DID LOSE IT QUITE A BIT,
I DON'T MISS, I DON'T MISS THAT,
THE THINGS THAT YOU'RE
DESCRIBING THERE ARE EXACTLY THE
THEMES THAT WE'RE DESCRIBING
FROM PAT ANDIOLE.
WHEN WE WALK INTO THIS COMPANY,
OUR GOAL IS TO DRIVE MEDICINES
TOWARDS PATIENTS SO THEY FEEL
BETTER.
WE'RE ALL ALIGNED, AND GOING
TOWARDS --
>> THAT'S REALLY FUN.
>> WE ALL KNOW THERE'S THIS ARC
FORWARD THAT WE'RE ALL INVESTED
IN TOGETHER AND SO, YES, IN ANY
TIME YOU GET 2 PEOPLE TOGETHER,
THERE WILL BE SOME POLITICS,
THAT GOES WITHOUT SPEAKING BUT
THE FACT THAT IT IS SX HEAT
THAT'S WHY I CAME BACK TO
INDUSTRY, IS BECAUSE I DID THAT
WORK WITH THE D. O. E. COVID
DISCOVERY PROJECT.
>> VERY DIFFERENT.
>> NINE NATIONAL LABS AND TO SEE
THEM MERGE INTO A TEAM AND STEP
INTO ACTION WAS JUST
INVIGORATING TO AND BROUGHT ME
BACK HOME WHERE I BELONG.
SMRKS OKAY, COOL, I LOST TRACK,
THIS IS WHY I HAVE TO PAY
ATTENTION.
OKAY, OKAY, IS IT TIME FOR
PREDICTIONS?
OR IS IT TOO EARLY IN NI DO WANT
YOU ALL TO MAKE A PREDICTION
ABOUT THE FUTURE, WHATEVER THEY
SAY ABOUT THAT, ABOUT, YOU KNOW
WHAT DO YOU THINK THE DRUG
DISCOVERY PROCESS IS GOING TO
LOOK LIKE 10 YEARS FROM NOW.
YOU THINK IT WILL BE RADICALLY
DIFFERENT.
IS EVERYTHING GOING TO BE
SELF-DRIVING LABS LIKE ROBOTS.
WILL PEOPLE KNOW HOW TO PIPETTE,
OR ARE WE STILL HOPING FORMORE
DATA MAYBE 10 YEARS SO THAT PUTS
AT 2034?
>> I'M NOT TALKING FIRST.
>> WHO WANTS TO BITE FIRST LIKE
SOMETIMES YOU GET THE
IMPRESSION, YEAH, IT'S CHANGING
SO FAST BUT YEAH, ACTUALLY.
>> OKAY, I'LL MAKE A PREDICTION.
I AM WORRIED THAT MY LOVE AND
EXPERTISE ABOUT SMALL MOLECULES
IS A FADING THING BECAUSE OF
CRSPR AND BECAUSE OF -- IT'S NOT
REALLY A WORRY BUT I AM SO
EXCITED, LET'S PUT IT AS A
POSITIVE THAT NOW WHEN YOU HAVE
A GOOD TARGET, YOU HAVE THIS
ARRAY OF WAYS TO INTERFERE WITH
IT, WITH SMALL MOLECULES OF
COURSE, WITH PROTEINS, WITH
GENOME ENGINEERING, WITH THESE
DEGRADERS, AND IT'S JUST
EXPLODING AND SO, THE ARSENAL, I
DON'T LIKE TO USE A WAR -- SORRY
I TO STOP MY ALARM.
ONE SECOND.
YES, BUT THIS ALARM -- I
APOLOGIZE, ALEXIA STOP.
SORRY ABOUT THAT, IT WAS TO
REMIND ME TO BE ON THIS PANEL,
OBVIOUSLY NOT THE RIGHT TIME.
SO THE AGERINAL OF TOOLS WILL
MAKE IT VERY EXCITING BUT ALSO
CONNIFIESING ABOUT WHAT'S THE
BEST WAY TO BUILD AN INTERVEKS
AND IT MIGHT CHANGE LIKE LOTS OF
THINGS, THE ECONOMICS AND THE
LEADERS IN THE FIELD, THE WAYS,
WHAT DISEASES ARE EASY TO TREAT
AND WHAT DISEASES ARE HARD TO
TREAT, SO I THINK THAT THERE'S
GOING -- I DON'T WANT TO CALL IT
CONFUSION BUT IT MIGHT LOOK LIKE
A VERY DIFFERENT WORLD TO THOSE
OF US IF WE WERE JUST
TRANSPORTED AHEAD, IT WILL LOOK
VERY DIFFERENT, TO ANSWER YOUR
QUESTION, IT WILL LOOK VERY
DIFFERENT AND IT WILL BE A MUCH
MORE MIXTURE OF APPROACHES
TOWARDS APPROACHES FORWARD.
>> THEOR PUTTIC.
>> SO I THINK IT'S ALWAYS SAFE
TO MAKE A PREDICTION THAT THE
FUTURE WILL LOOK LIKE NOW ONLY
MORE SO, RIGHT IN AND SO, IT
WILL ALWAYS, WE WILL ALWAYS BE
REQUIRED TO FIND MEDICINES THAT
ARE EFFICACIOUS FOR THE THING,
THE DEC THAT WE NEED TO TREAT
THAT ARE SAFE, THAT HAVE ALL OF
THESE PROPERTIES TO ALLOW US TO
MAKE THEM, THAT'S NOT GOING TO
GO AWAY.
I AGREE WITH RUSS, THAT THERE'S
HUGE OPPORTUNITIES ON THE
HORIZON, I LOVE HAVING COME TO A
COMPANY THAT FOCUSES ON
BIOLOGICS AND SMALL MOLECULES,
THAT HAS BOTH OF THOSE TOOL
KITS, PLUS MANY COMBINATIONS
WITHIN IT INSIDE.
AND SO, THAT'S EXCITING AND
OPENS UP THE SET OF TOOLS THAT
WE HAVE TO PLAY WITH TO
INFLUENCE HUMAN BIOLOGY, BUT
APPROXIMATE WE DID, WE STILL
HAVE TO INFLUENCE HUMAN BIOLOGY
IN WAYS THAT ARE EFFECTIVE AND
WE HAVE TO PROVE IT.
I ALSO TRULY BELIEVE THAT THE
FUTURE WILL ALWAYS BELONG TO
THOSE PEOPLE WHO CAN -- THOSE
TEAMS, THOSE INDIVIDUALS, THOSE
COMPANIES THAT CAN HARNESS BOTH
THE HUMAN EXPERTISE AND SHE'S
NEW AUTOMATION COMPUTATIONAL
APPROACHES.
THE SIGNIFY -- CYBORG IS GOING,
NOT THE HUMAN ALONE.
>> I THINK I HAVE A SLIGHTLY
DIFFERENT SPIN, ON WHAT RUSS
SAID BECAUSE I AGREE.
I THINK WE WILL HAVE NEW MODEL
CITIZEN DALLASCOWBOYS.COM IITYS
BUT IF YOU LOOK AT THESE NEW
EMERGING MODALITIES, THERE'S
STILL A SMALL MOLECULE COMPONENT
WHETHER YOU'RE LOOKING AT RADIO
THERAPY OR ANTIBODY DRUG
CONJUGATES OR TARGETED PROTEIN
DEGRADATION, YOU STILL HAVE TO
BE ABLE TO DESIGN A SMALL
MOLECULE AND MY HOPE AT LEAST
FOR THE NEXT 10 YEARS FROM NOW
IS THAT WE HAVE A BETTER
UNDERSTANDING OF THE THINGS THAT
ARE STUMPING US RIGHT NOW
BECAUSE I THINK A LOT OF WHAT'S
STUMPS US NOW IS WE DON'T HAVE
ENOUGH OF AN UNDERSTANDING OF
ALL THE OFFTARGETS THAT WE'RE
TRYING TO AVOID.
WE DON'T HAVE ENOUGH OF AN
UNDERSTANDING OF DRUG
METABOLIZING ENZYMES AND HOW TO
DESIGN MOLECULES TO AVOID THOSE
LIABILITIES AND THROUGH A
COMBINATION OF DATA GENERATION
AND ADVANCES IN STRUCTURAL
BIOLOGY, WE CAN GIVE A LOT MORE
OF AN UNDERSTANDING AND THEN
MAYBE, THE THINGS BEYOND POTENCY
WHICH ARE REALLY, THE TIME
CONSUMING PARTS OF DRUG
DISCOVERY NOW WILL BECOME MORE
STREAMLINED.
>> YEAH.
OKAY.
JOEL?
>> I THINK BIOLOGY WILL ALWAYS
SURPRISE US.
THERE WILL BE SOMETHING
FUNDAMENTAL ANOTHER RNA SPECIES
OR SOMETHING, WHO KNOWS THAT WE
WON'T BE ABLE TO TAKE INTO,A
COUNT BUT BECAUSE OF THAT AND IT
WILL PROBABLY TAKE A WHILE JUST
A MUCH BIGGER INVESTMENT IN
STANDARDIZING CLINICAL DATA,
REALLY MAKING THE USE OF
CLINICAL TRIALS, PATIENT SAMPLES
MAKE AN IMPACT ON THE DATA SETS
WE HAVE TO BETTER UNDERSTAND
DECS AND IN BIGGER FOCUS THAN
PREVENTION OF DISEASES, AS
OPPOSE TO ACTUALLY TREATING
THEM, SO JUST GENERAL BASIC
UNDERSTANDING.
>> YEAH, THOSE WERE ALL TERRIFIC
THOUGHTS.
I WAS TOLD -- I THINK, IT SEEMS
I HOPE IT'S CLEAR THAT 10 YEARS
FROM NOW OUR DATA LANDSCAPE
HOPEFULLY IN BIOLOGY WILL BE
SIGNIFICANTLY IMPROVED FROM
WHERE WE ARE I KNOW THERE'S LOTS
OF FOLKS WORKING ON THAT, IF
PEOPLE ARE SORT OF TAKING HOLD
AND THINKING ABOUT HOW WE CAN DO
THAT EFFECTIVELY, SO AT LEAST I
THINK WE HOPE TO HAVE THAT TO
LOOK FORWARD WELL, GOODNESS, WE
COULD DO 1 MORE, WE COULD WRAP
IT UP.
WE'VE BEEN GOING FOR A WHILE.
ARE THERE ANY FINAL THOUGHTS ON
THE PANEL ANYONE WANTS TO SHARE.
IS THERE SOME BURNING QUESTION
YOU HOPED THAT I WOULD ASK THAT
I DIDN'T?
CLOSING THOUGHTS?
WE'RE QUIET, WE TALKED A LOT.
SHAYAM, ANYTHING FROM YOU,
ANYTHING YOU WANT TO ASK THESE
FOLKS WHILE WE HAVE THESE
MARVELOUS PEOPLE ON THE HOOK.
>> I MEAN WE COVERED A LOT OF,
YOU KNOW I LOT OF DIFFERENT
TOPICS, ALL I CAN SAY IS THIS
WAS EXTREMELY ENLIGHTENING AND I
CAN HEAR YOU ALL TALK FOR HOURS.
BUT I'M SURE WE'RE ALL LIKE,
GETTING TIRED AND YOU KNOW
PROBABLY WE SHOULD MOVE FORWARD,
SO THANK YOU SO MUCH FOR
EVERYONE, THAT WAS SAB LIEWT --
ABSOLUTELY AMAZING AND THANK YOU
ROMMIE FOR MODERATING THIS.
NTHANK YOU FOR PARTICIPATING.
I HAD A GOOD TIME.
>> THANK YOU FOR THE PANELISTS
WHAT I WILL DO IS I WILL SHARE
MY SLIDE HERE XI WILL CALL FOR
THE ORGANIZERS TO TURN THEIR
CAMERAS AND MICs ON AND JUST
ASK EVERYONE TO COME UP WITH A
QUICK SUMMARY OF KEY TAKE AWAYS
AND REFLECT ON THE NEXT, THE
PAST 2 DAYS BEFORE WE WRAP THIS
UP.
>> SO WE HAVE, OKAY, THE SLIDE
IS UP.
SO MAYBE ALEX YOU CAN START WITH
YOUR THOUGHTS ABOUT THESE 2 DAYS
AND WHAT YOU THINK WERE THE BEST
KEY TAKE AWAYS HERE.
>> THANK YOU.
SO I THINK REALLY HAD SUPER
EXCITING EVENT.
WE TARTED WITH A LOT OF --
STARTED WITH A LOT OF
ENTHUSIASM, BUT I WOULD SAY
ENTHUSIASMOT PART OF SPEAKERS
AND LISTENERS, THE WORKSHOP AS
BEEN OVERSUBSCRIBED SO THERE
WERE A LOT OF TREMENDOUSLY
INTERESTED PEOPLE IN THE
ACCOMPLISHMENTS.
IN NO SMALLER NUMBER OF IDEAS
AND THEIR ACTIONS FOR FUTURE
RESEARCH, SO I THINK IF ANYTHING
IN THIS WORKSHOP THAT IF PEOPLE
THINK ABOUT WHAT TO DO AND WHAT
TO CHOOSE, I THINK IT HAS BEEN
PROFESSIONAL AND STIMULATING
EMPLOY SO, 1 BIG TAKE A WAY THAT
I WOULD LIKE TO SHARE.
>> I WANT TO -- DISCUSSION,
YEAH, I THINK THIS IS IT'S A
GOOD AMOUNT OF THE PRESENTATION
THAT WE LOOK AT AND I THINK IT'S
GREAT DISCUSSION ABOUT THE
INTEGRATION OF COMMUTATIONAL
MEDICINE AND EXPERIMENTAL
VALDATION OF THOSE EMPLOY I
THINK THIS IS REALLY IMPORTANT
PART AND IT WAS A LOT OF TALKS,
ACTUALLY SHOWN THAT THE MACHINE
LEARN XG IT WAS A PART OF THE
GOAL, YOU REMEMBER 1 YESTERDAY
AND IN THE MORNING WE TRIED TO
OPEN IT'S ICSIGHTING TO SEE
BIOTECH INVOLVED IN THE
INFORMATION AND AS WELL AS
ACADEMIA SHOWING THE NEW
APPROACH AND SO NEW TRENDS AND
MULTIOMICS DATA FROM RECURSION,
GREAT TALK, ALL THE DIFFERENT
SLIDES IN WHICH THEY INCORPORATE
AND CAPITULATIZE TO BUILD
MISSION LEARNING, I THINK THIS
IS REALLY EXCITING TO SEE AND OF
COURSE BINDING IMPRESSIONS AS
WELL, DAVID SHOWED US THAT WORK
AS WELL, GREAT PANEL DISCUSSION.
IT WAS REALLY GREAT
CONVERSATION.
FACILITATE TODAY, YEAH, IT WAS
AWESOME.
>> THANKS, SHAYAM.
>> I AGREE WITH EVERYTHING SAID
EARLIER, GREAT PRESENTATIONS,
GREAT WORKSHOP, I LEARNED A LOT
GIVEN MY OLD SCHOOL TRAINING OF
LAB CHEMISTRY SO I HAVE GENERIC
POINTS I THOUGHT WERE VERY
IMPORTANT IN THIS PARTICULAR
WORKSHOP, 1 OF THE POINTS AGAIN,
IS THE TALK ABOUT BIOLOGICS AND
ANTIGENS WAS REALLY FASCINATING
AND I THINK NOW, TAKING THAT
VERTICAL MODEL AND EXPRESSING
THEM IN THE RIGHT CELL LINES AND
EXPRESSING THEM, THOSE ARE THE
NEXT QUESTIONS, THE HAPPY MEDIUM
OF OF EXPRESSING THESE MOLECULES
WOULD BE A--BITS IMPORTANT
ASPECT, I THINK, I THOUGHT
REPURPOSESSING AND
IDENTIFICATION OF THESE TARGETS
AND REPOSITIONING OF THE DRUGS
FINDING THAT INFORMATION USING
AI MACHINE LEARNING TOOLS WAS
PRETTY FASCINATING.
AGAIN THAT IS SOMETHING THAT I
ASSUME WOULD BE USED MORE
BROADLY BECAUSE OF THE BROAD
INFORMATION THAT'S ALREADY THERE
AND REPOSITIONING OF THE OLDER
DRUGS WILL BE A FASCINATING
AREA.
AS A DEVELOPER MYSELF, I HAVE
ALWAYS LOOKED AT PROCESSES, THE
PRODUCT KIND OF A THING AND
THEREFORE BRINGING THIS MACHINE
LEARNING AND AI MODELS, HOW DO
YOU DEVELOP A PRODUCT BECOMES AN
IMPORTANT ASSPEC, CAN YOU SCALE
UP IT GOES THROUGH THE MODELING
FOR THE ASPECT LAB, AND THE
MANUFACTURING SITE, I THINK
THOSE ARE IMPORTANT AREAS TO
LOOK INTO HOW MUCH IS THAT
TRANSLATION ARE THESE MOLECULES
ABLE TO SCALE, AS IS BEING SAID,
OR ARE THERE ISSUES IN SCALING
UP AND MANUFACTURABILITY OF THIS
MOLECULES, YOU KNOW?
THAT IS SOMETHING TO WATCH OUT
FOR AND I'M SURE PEOPLE ARE
LOOKING AT THIS AND BRIDGING
THAT GAP.
THE OTHER POINT IS, YOU KNOW, IT
SEEMS LIKE THE LEARNING ASPECT
OF IT, OR DEEP LEARNING ASPECT,
IS REALLY OUTPACING THE CLINICAL
DEVELOPMENT AND I WORRY THERE
MIGHT BE A REGULATORY LEARNING
THAT NEEDS TO HAPPEN, YOU KNOW
WHICH I THINK IS PROBABLY --
THERE'S A LOT OF LEARNING THAT'S
HAPPENING UP FRONT BUT MORE THAT
GAP WILL INCREASE WITH THE
MODELS COMING UP AND TRYING TO
BRIDGE THAT GAP OR NARROW THAT
GAP WOULD BE AN IMPORTANT
ASPECT, I AND THEN PREDICTION OF
TOXICITY, IMPORTANT, PKPD, AND
OTHER DRUG TARGETS WE TALKED OFF
TARGET EEIVETS AND UNDERSTANDING
THE CONTRIBUTES OF THESE VARIOUS
MOLECULES THAT ARE BEING MODELED
WOULD BE AN IMPORTANT FACET I
THINK.
THOSE WOULD BE IMPORTANT TO
MONITOR SO I LOOK AT COMPLETELY
FROM A PRODUCT DEVELOPMENT LAB
CHEMIST RADIOY OR A BIOLOGICAL
POINT OF VIEW AND I THINK
BRIDGING THE GAP WILL BE AN
IMPORTANT ASPECT, 1 OTHER POINT
I QUICKLY WANT TO MAKE IS THAT
THIS IS NOT GOING TO JUST BE AN
ISSUE FOR VACCINES AND
THERAPEUTICS BUT EVEN FROM A
DIAGNOSTIC'S POINT OF VIEW, A
LOT OF VARIABLES AND OTHER
DEVICES DO USE ALGORITHMS AND
LEARNING TOOLS AND REGULATORY
PATH FORWARD FOR SOME OF THESE
NEW TECHNOLOGIES WILL BE REALLY
CRITICAL, I THINK TO OBSERVE.
THOSE WERE MY ONLY HIGH LEVEL
POINTS BUT REALLY FASCINATING
MEETING.
I TOOK A LOT OUT OF THIS MEETING
AND THANK YOU TO YOU AND NCATS
FOR ORGANIZING THIS.
IT THIS WAS FANTASTIC.
>> THANK YOU SO MUCH, THAT WAS
AWESOME TO HEAR ALL OF YOUR
LEARNINGS AND YOU'RE BEING PART
OF THIS.
ROMMIE, I WILL GIVE YOU SOME
TIME TO ALSO SAY A WORD OR 2 AND
THEN WE CAN WRAP UP.
SURE, I MEAN JUST I SORT OF
ENJOYED JUST SEEING THE BREDTH
OF TOPICS IN THIS SPACE, JUST TO
ME IT'S ASTONISHING HOW MUCH IS
GOING ON, AND YOU KNOW JUST THE
WHOLE PIPELINE AT THE MOMENT.
SO IT WAS REALLY, IT WAS REALLY,
I THINK BENEFICIAL TO HEAR FROM
FOLKS, I'M REALLY EXCITED ABOUT
WHAT WE HEARD, ESPECIALLY THE
OPEN SCIENCE INITIATIVES, I
THINK THAT'S HUGE, YOU KNOW WE
HAVE TO REALLY GET SUCH A BETTER
GRIP ON THE DATA AND ESPECIALLY,
THERE'S A LOT OF MOVEMENT AND A
LOT OF MOVEMENT AND THINGS
HAPPENING THERE, SO I'M
PARTICULARLY, YOU KNOW I'M HAPPY
TO HEAR IT FROM ALL THESE
DIFFERENT ANGLES AND I HAD A LOT
OF FUN.
SO THANKS FOR ALSO ORGANIZING
THIS AND FOR THE PARTICIPANTS
AND SPEAKERS.
>> AWESOME THANK YOU ROMMIE.
I WILL ECHO THAT, THIS WAS
EXCELLENT.
TWO DAYS OF LEARNING FOR ME AS
WELL, SO WONDERFUL TALKS.
THANKS FOR ALL OF OUR SPEAKERS
AND ALSO THANK YOU ALL, IT WAS
WONDERFUL WORKING WITH YOU AND
OUR ATTENDEES AS WELL, SO I WILL
WRAP UP WITH 1 MORE SLIDE HERE.
SO IF YOU'RE STILL HERE THANK
YOU FOR HANGING UNTIL THE LAST
STRETCH, WE APPRECIATE YOU SO
MUCH.
WE WOULD LOVE TO HEAR YOUR
FEEDBACK, OKAY?
DID YOU ENJOY THIS EVENT.
HOW COULD WE HAVE DONE THIS
BETTER FOR YOU?
SO THIS IS A QR CODE HERE, I
THINK THE BURDEN ON THIS IS 2 OR
3 MINUTES.
SHOULDN'T BE TOO LONG SO IF YOU
COULD JUST TAKE THIS FEEDBACK
SURVEY, AND LET US KNOW THAT
WILL BE FANTASTIC WE WILL ALSO
E-MAIL YOU THE LINKS HERE, SO IF
YOU CAN'T CATCH IT RIGHT NOW,
YOU COULD DO IT LATER.
THIS WORKSHOP IS BEING RECORDED,
SO YOU WILL ALL RECEIVE THE
LINKS TO VIEW THIS, ON DEMAND
WHENEVER YOU WOULD LIKE AND 1
MORE FINAL WORD, I LEAD THE
[INDISCERNIBLE] PROGRAM, THIS
PROGRAM HELPED ORGANIZE THIS
EVENT, THIS IS MULTIPLE WAY YOU
CAN CONNECT WITH US, WE DO
MULTIPLE OTHER WORKSHOPS, AS
WELL ON DIFFERENT TOPICS ON
DISCOVERY AND SO CONNECT WITH US
AND YOU CAN LEARN MORE ABOUT OUR
PROGRAM.
ALL RIGHT, EVERYONE, I'M GOING
TO CLOSE IT UP AND ADJOURN.
I AGAIN, THANK YOU SO MUCH
EVERYONE, SPEAKERS, ORGANIZERS,
EVERYONE, HAVE A GOOD 1!
>> ALL RIGHT, BYE.