>> HI, EVERYONE WELCOME TO THE
IN SILICO DRUG DISCOVERY
WORKSHOP.
WE'RE EXCITED TO HAVE YOU JOIN
US.
I'M SER INE AT THE NATIONAL
INSTITUTES OF HEALTH.
SO WE HAVE A SUPER EXCITING
PROGRAM FOR YOU ALL TODAY, WE'D
LIKE TO START WITH OUR
INTRODUCTORY COMMENTS.
FOR THAT I'D LOVE TO INTRODUCE
DR. JONI RUTTER WHO IS THE
DIRECTOR OF THE NATIONAL CENTER
FOR ADVANCING TRANSLATIONAL
SCIENCES.
JONI, WE'RE SUPER EXCITED TO
HAVE YOU HERE TO OPEN UP THE
WORKSHOP.
THE FLOOR IS YOURS.
>> THANK YOU, SO MUCH, SARINE.
IN ADDITION TO BEING OUR EDITOR
AND CHIEF OF THE ASSAY GUIDANCE
MANUAL, SARINE IS AN AMAZING
AMBASSADOR FOR TRANSLATIONAL
SCIENCE AND THANK YOU TO OUR
ORGANIZING TEAM AT NCATS, AND
OUR COLLEAGUES AND I'M GLAD
KIMBERLY WILL FOLLOW MY REMARKS
TOO.
AND I SHOUT OUT TO UC SAN DIEGO
AND CHAPEL HILL AND IT'S SUPER
EXCITING.
AND THANK YOU ALL FOR JOINING US
TODAY THIS IS THE OCTOBER AGM IN
SILICO DRUG DISCOVERY WORKSHOP
TODAY AND TOMORROW.
IF YOU DON'T KNOW ABOUT THE
PROGRAM IT'S REALLY A WORLD
CLASS RESOURCE TO LOOK AT BEST
PRACTICES IN TRANSLATIONAL
SCIENCE AND IT PROVIDES ROBUST
GUIDELINES FOR RESEARCH, ASSAY
DEVELOPMENT, DATA ANALYSIS AND
SO ON.
WE HAVE DISCUSSIONS EQUALLY IN
THESE AREAS OF SCIENCE AND
ENSURE THE FINDINGS ARE
REPRODUCIBLE AND THEY CAN MOVE
SMOOTHLY FROM BENCH TO BEDSIDE.
THESE WORKSHOPS PLAY A KEY ROLE
IN HAVING THE CRITICAL
DISCUSSIONS WITHIN THE
SCIENTIFIC COMMUNITY TO MAKE
THAT HAPPEN.
THIS WORKSHOP WILL FOCUS ON
INNOVATION WHICH IS ONE THE
CRITICAL STEPS IN THE
TRANSLATIONAL PIPELINE.
I DON'T HAVE A SPECIAL EMPHASIS
ON HOW DATA SCIENCE AND A.I.
METHODS ARE REVOLUTIONIZING DRUG
DISCOVERY.
WE BELIEVE TEAM SCIENCE HAVING
THESE TOGETHER WITH BIOLOGISTS,
CHEMISTS, DATA SCIENTISTS AND
STATISTICIANS ALL WORKING SIDE
BY SIDE TO PUSH THE BOUNDARIES
OF WHAT'S POSSIBLE.
SO WE THINK IT'S CRITICAL TO
HAVE THESE TO BRING ALL THOSE
DISCIPLINES TOGETHER TO LOOK AT
HOW WE CAN IMPLEMENT BEST
PRACTICES.
AS WE LOOK TO THE FUTURE, A.I.
IS NOW PIVOTAL TO ACCELERATING
DRUG DISCOVERY AND IMPROVING
TRANSLATIONAL SCIENCE AND OUR
TEAMS ENHANCE THINGS LIKE HIGH
THROUGHPUT SCREENING AND
AUTHORIZING COMPOUND PROPERTIES
LIKE METABOLIC STABILITY FOR
EXAMPLE AND THESE METHODS ALSO
PREDICT SYNERGISTIC DRUG
COMBINATIONS FOR CANCER AND RARE
DISEASES AND ADVANCING THE NEXT
GENERATION OF MODALITIES AND
TARGETED ANTIBODIES THESE ARE
ALL CRITICAL MODALITIES COMING
ON SCENE AND HOW WE STUDY THEM
AND HOW WE GENERATE THEM AND HOW
WE TRANSLATING THEM TO CLINICAL
PRACTICE IS WHERE THIS IS ALL
STARTING.
ONE WAY NCATS APPLIES A.I. IN
IMPROVING HIGH THROUGHPUT
SCREENING OUTCOMES BY DETECTING
POTENTIAL ASSAY ARTIFACTS
ENSURING THE DATA WE RELY ON IS
ROBUST AND ACCURATE.
THAT'S CRITICAL FOR ADVANCING
THE PROMISING CANDIDATES
FORWARD.
WE HAVE A REALLY HIGH FAILURE
RATE WHEN WE MOVE COMPOUNDS
THROUGH THE TRANSLATIONAL
PIPELINE.
THIS IS AN AREA WE HOPE TO
ADDRESS TO BRING DOWN THAT
FAILURE RATE.
WE'RE ALSO THINKING ABOUT
APPLICATIONS IN QUANTUM SCIENCES
AND HAVE CHALLENGE COMPETITIONS
OUT ON THE STREET ON THAT.
I'M HAPPY TO PUT THOSE IN THE
LINKS WHEN I'M FINISHED HERE.
BUT THE THREE AREAS OF INTEREST
INCLUDE QUANTUM ALGORITHMS FOR
DRUG DISCOVERY AND FOR CLINICAL
RISK PREDICTIONS, DIAGNOSIS AND
THERAPEUTICS AS WELL AS QUANTUM
ALGORITHMS AND TO IDENTIFY GAPS
IN THE TRANSLATION OF IN SILICO
MODELS TO EFFECTIVE THERAPIES.
AS WE'VE SEEN CHALLENGING IN
TRANSLATING DATA FROM BIO
CHEMICAL ASSAYS TO CELL-BASED
ASSAYS AND TISSUES AND ANIMALS
TO HUMANS, SIMILAR GAPS EXIST IN
THE IN SILICO MODELS TO THEN THE
REAL WORLD APPLICATIONS THAT
LEAD TO EFFECTIVE TREATMENTS.
EACH STAGE OF THE DRUG DISCOVERY
PROCESS REQUIRES THOSE SPECIFIC
TYPES OF MODELS AND THE
PREDICTIONS MADE BY THESE MODELS
HAS TO ALIGN WITH THE BIOLOGICAL
COMPLEXITIES.
AS WE MOVE FROM COMPUTATIONAL
PREDICTS WE NEED TO LOOK AT WHEN
MODELS AND PREDICTIONS ARE BEST
SUITED FOR EACH STEP IN THE
PIPELINE.
WE ALSO NEED TO CONSIDER WHAT
NEW MODELS AND APPROACHES ARE
REQUIRES TO IMPROVE TRANSLATION
AND ENSURE THE IN SILICO
INSIGHTS LEAD TO THOSE TANGIBLE
THERAPEUTIC ADVANCEMENTS.
SO, THAT'S WHAT I'M SUPER
EXCITED ABOUT FOR THE MEETING IS
GETTING GOOD INSIGHT TO TALK
ABOUT THIS.
AND I WANT TO ACKNOWLEDGE
SOMETHING TRULY EXTRAORDINARY.
IT'S NOT EVERY DAY WE GET TO
HEAR FROM A NEWLY-MINTED NOBEL
LAUREATE AND GLAD TO HAVE DAVID
BEAKER.
HE WAS AWARDED THE 2024 NOBEL
PRIZE IN CHEMISTRY FOR HIS
GROUNDBREAKING WORK IN
COMPUTATIONAL PROTEIN DESIGN
WITH DENISE AND JOHN WITH THEIR
PROTEIN STRUCTURE WORK TOO.
THIS FIELD IS FUNDAMENTALLY
RESHAPING OUR UNDERSTANDING OF
BIOLOGY AND OPENING NEW DOORS
FOR THERAPEUTIC DEVELOPMENT AND
LOOKING FORWARD TO THESE TALKS
AS WE START TO HEAR MORE AND
APPLY THOSE LEARN TO WHAT WE'RE
GOING TO DO IN THE COMING YEARS.
WE'LL LOOK AT THE PRACTICAL
CHALLENGES OF TRANSLATING
ANTIRETROVIRAL MODELS INTO
EFFECTIVE THERAPIES.
THIS IS IMPORTANT TO NOTE
BECAUSE THE NOBEL PRIZE IN
PHYSICS WAS AWARDED FOR
FOUNDATIONAL DISCOVERIES AND
INVENTIONS THAT ENABLE MACHINE
LEARNING WITH ARTIFICIAL NEURAL
NETWORKS.
WHILE IT'S REALLY FANTASTIC TO
CELEBRATE THOSE ACHIEVEMENTS,
IT'S ALSO EQUALLY IMPORTANT THAT
WE BUILD ON THEM.
IDENTIFYING GAPS IN THE
TRANSLATIONAL PROCESS AND
FINDING WAYS TO ADDRESS THEM.
HOW DO WE ENSURE WHAT WORKS IN
THE LABS WORKS IN REAL WORLD
SETTING AND IMPROVE THE
RELIABILITY OF DRUG DEVELOPMENT?
THESE ARE KEY QUESTIONS I LOOK
FORWARD TO LARGER ABOUT OVER THE
NEXT TWO DAYS FOR THIS
CONFERENCE.
AND THIS WORKSHOP REALLY
REPRESENTS THE COLLECTIVE
EFFORTS OF INSTITUTIONS ACROSS
THE PUBLIC AND PRIVATE SECTORS
THAT ARE EXEMPLIFYING THE POWER
AND COLLABORATION OF TEAM
SCIENCE TO DRIVE THE SCIENCE
FORWARD.
AS WE KICK OFF THE MEETING TODAY
ONLY THING I ASKED FOR YOU TO
KEEP THINKING ABOUT IN THE BACK
OF YOUR MIND THAT MOVES TO THE
FRONT OF THE YOUR MIND IS PLEASE
DON'T LOSE SIGHT OF THE REAL
GOAL HERE.
THE WORK WE DO HERE TODAY WILL
SHAPE OU QUICKLY AND EFFECTIVELY
NEW TREATMENTS CAN REACH
PATIENTS AND SAVE LIVES.
WE LIKE TO THINK TO BRING NEW
TREATMENTS FOR ALL PEOPLE MORE
QUICKLY.
THANKS FOR JOINING TODAY AND
TOMORROW AND LET'S GET STARTED.
KIMBERLY, OVER TO YOU OR SARINE.
THANK YOU.
>> THANK YOU SO MUCH, JOANIE.
SO, THE NEXT PERSON FOR THE
OPENING REMARKS IS DR. KIMBERLY
SCIARRETTA.
SHE'S THE ACTING DIRECTOR
DIVISION OF RESEARCH AND
VENTURES DRIVE PART OF THE
BIOMEDICAL ADVANCED RESEARCH AND
DEVELOPMENT AUTHORITY, BARDA.
THANK YOU FOR JOINING US.
THE FLOOR IS YOURS.
>> THANK YOU, SARINE.
IT DOESN'T LOOK LIKE I CAN BE ON
VIDEO SO MAYBE THAT CAN BE FIXED
AND HOPE THE LARYNGITIS DIDN'T
STOP ME AND I'M PLEASED TO KICK
OFF THE MEETING WITH OUR
COLLEAGUES AT NCATS.
I HEARD WE'VE HAD QUITE OF
RESPONSE AND REFLECTION OF HOW
RESONATING AND IMPACTFUL THE
TOPIC IS TO ALL OF YOU.
I WOULD BE REMISS IF WE DIDN'T
THANK AND ACKNOWLEDGE ALL THE
WORKSHOP ORGANIZING COMMITTEE
AND EFFORTS PUT INTO PLANNING
THE MEETING WITH OUR COLLEAGUES
AT NCATS, BARDA AND UCSD AND
UNC.
BEFORE I DISCUSS OUR INTEREST IN
THIS SPACING AND WHY WE'RE
EXCITED ABOUT THE MEETING AND
WANTED TO PROVIDE BACKGROUND ON
BARDA.
THE BIOMEDICAL ADVANCED RESEARCH
AND DEVELOPMENT AUTHORITY WITHIN
THE ADMINISTRATION OF STRATEGIC
PREPAREDNESS AND RESPONSE WHICH
IS WITHIN THE HHS.
I KNOW QUITE A MOUTHFUL.
THE MISSION IS TO PREPARE AND
RESPOND TO PUBLIC HEALTH
EMERGENCIES AND A BIG PART OF
OUR WORK WAS RESPONDING TO
COVID-19 AND WE RESPOND TO OTHER
PUBLIC HEALTH DISASTERS LIKE
HURRICANES AND FOREST FIRES.
BARDA'S ROLE IS TO DEVELOP THE
COUNTER MEASURES AND MAKE THEM
AVAILABLE FOR THE PUBLIC HEALTH
EMERGENCIES.
WHAT DO I MEAN BY MEDICAL CO
COUNTER MEASURES AND
DIAGNOSTICS, VACCINES, ANY TYPE
OF THERAPEUTICS AND DRUGS WHICH
IS THE TOPIC OF THIS MEETING.
AND WE'RE DEVELOPING THESE
MEDICAL COUNTER MEASURES TO
RESPOND TO ANY OF THESE FLU AND
EMERGING INFECTIOUS DISEASES
WITH UNIQUE PUBLIC-PRIVATE
PARTNERSHIP AND OUR PARTNERS
HAVE HAD A TOTAL OF 95 FDA
APPROVALS AND LICENSES AND
CLEARANCES TO DEVELOPMENT OF
COUNTER MEASURES THAT WILL
DIRECTLY PROTECT AMERICANS
FACING PUBLIC HEALTH
EMERGENCIES.
THIS IS A TESTAMENT TO THE
PARTNERSHIPS OF WORKING WITH
ORGANIZATIONS LIKE YOURSELVES.
SO BARDA TRADITIONALLY FOCUSES
ON ADVANCED RESEARCH AND
DEVELOPMENT OF MEDICAL COUNTER
MEASURES.
HOWEVER WE HAVE ESTABLISHED THE
DRIVE DIVISION WHICH PLAYS MORE
IN THIS INNOVATION SPACE
SUPPORTING ENABLING TECHNOLOGIES
AND SUPPORTING SOME EARLIER
RESEARCH ESPECIALLY THROUGH SOME
OF OUR PARTNERSHIPS WITH BLUE
NIGHT AND THE BARDA ACCELERATOR
NETWORK AND OUR VENTURES TO ECHO
DR. RUTTER WE'RE IN A UNIQUE
POSITION TO BE KICKING OFF THE
MEETING AT A TIME WHEN NOBEL
PRIZES WERE ANNOUNCED FOR
CHEMISTRY AND PHYSICS FOR THIS
TOPIC.
THOUGH WE ALL UNDERSTAND THE
POTENTIAL VALUE IN SILICO BRINGS
TO DRUG DISCOVERY AND MANY HAVE
EXPERIENCE THE LENGTHY TIME AND
IDENTIFICATION OF THESE
CANDIDATES, WE DO SEE THE VALUE
THESE APPROACHES CAN REALLY
BRING TO IDENTIFYING SAFER AND
MORE EFFICACIOUS DRUGS FASTER
AND SOONER AND THIS IS
CRITICALLY IMPORTANT FOR OUR
ABILITY TO RESPOND TO PUBLIC
HEALTH EMERGENCIES.
I DID WANT TO ACKNOWLEDGE AGAIN
THE GREAT WORK BY OUR COLLEAGUES
IN THIS SPACE AND HOW IMPACTFUL
THIS WORK REALLY IS AND WE LOOK
FORWARD TO HEARING FROM
DR. BAKER AND OUR ESTEEMED
COLLEAGUES.
THE WHOLE MEETING WOULD NOT BE A
SUCCESS IF IT WASN'T FOR ALL OF
YOU.
WE'RE BRINGING TOGETHER
U.S. GOVERNMENT WITH ACADEMIC
AND INDUSTRY EXPERTS TO EXPLORE
THE FUTURE OF IN SILICO
MODELLING.
SO AN ASK IS TO FOSTER AND FORM
COLLABORATIONS AS MUCH AS
POSSIBLE IN THIS VIRTUAL
ENVIRONMENT.
WE WANT YOU TO ENGAGE IN
INFORMATION SHARING AND
HOPEFULLY GENERATING NEW IDEAS
TO ADDRESS CHALLENGES.
I HOPE WE CAN BRAIN STORM
SOLUTIONS TOGETHER AND MAYBE
SOME WILL BE THE GENESIS OF NEW
PROGRAM IDEAS.
HOPEFULLY WE'LL ALL GAIN A
BETTER UNDERSTANDING OF THE
CURRENT LANDSCAPE OF
COMPUTATIONAL DRUG DISCOVERY.
AGAIN, THIS INFORMATION WILL
DIRECTLY HELP US PREPARE FOR
FUTURE PUBLIC HEALTH
EMERGENCIES.
BARDA CURRENTLY DOESN'T HAVE
OPENING AND MOST WORK IS
FOCUSSED ON ADVANCED RESEARCH
AND DEVELOPMENT AND THESE
ACTIVITIES TYPICALLY OCCUR MORE
UP STREAM WITH OUR PARTNERS
BEFORE THEY ENGAGE WITH BARDA.
HOWEVER, AS I NOTED WE'RE
INTERESTED IN INNOVATIVE
OPPORTUNITIES AND ENABLING
TECHNOLOGIES ESPECIALLY WITHIN
THE DRUG DIVISION AND THERE WAS
A NOTABLE EFFORT IN OUR PROGRAM
REDIRECT.
IT'S A PARTNERSHIP WITH DRIVE
AND OUR DIVISION CBRN FOCUSSED
ON REPURPOSING DRUGS TO TREAT
CONDITIONS AS A RESPONSE TO
EXPOSURE TO CHEMICAL AGENTS AND
THERE WERE EFFORTS IN THIS
PROGRAM RELATED TO IN SILICO
APPROACHES.
AGAIN, THERE'S OPPORTUNITY AS WE
LEARNED FROM THE MEETING TO
CONTINUE TO EXPAND IN THIS
SPACE.
I DID WANT TO HIGHLIGHT THE
VALUE AND TRANSLATIONAL SCIENCE
HERE OF THIS WORK WE'RE GOING TO
HEAR TODAY AND HOW THAT'S GO TO
APPLY TO FUTURE EVENTS AND
DEVELOPMENT.
THIS WILL DIRECTLY IMPACT ALL OF
YOU AND HOPEFULLY SAVE AMERICANS
WHEN WE ARE FACING A PUBLIC
HEALTH THREAT IN THE FUTURE.
SO ONE RAPID DRUG IDENTIFICATION
IMMENSERY CRITICAL WHEN WE ARE
FACING A PUBLIC HEALTH EMERGENCY
AND THIS COULD BE IN THE DESIGN
OF NOVEL DRUGS AND CANDIDATE TO
CLINICALLY EVALUATE.
THE SECOND IS WE NEED TO
INCREASE OUR ABILITY TO APPLY
OUR LEARN FROM PREVIOUS THREATS
WE'VE SEEN TO NEW EMERGING
THREATS.
THAT TOUCHES ON OUR RESPONSE
CAPABILITIES AND BUILDING OUT
PLATFORMS AND MODELS AND
APPROACHES WILL ALLOW US TO BE
MORE AGILE IN RESPONSE TO FUTURE
THREATS.
THE OTHER UNIQUE THING ABOUT
DEVELOPING MEDICAL COUNTER
MEASURES FOR CBRN THREATS IS WE
CAN'T ALWAYS EVALUATE THEM IN
CLINICAL MODELS.
THEREFORE THERE'S A NEED FOR
SUFFICIENT AND EQUIVALENT MODELS
TO EVALUATE THESE PRODUCTS EVEN
OUTSIDE OF ANIMALS.
OF COURSE WE'RE ALWAYS
INTERESTED IN UNDERSTANDING
PHARMACOLOGY AND TOXICOLOGY OF
THE DRUGS.
THESE ARE SEVERAL AREAS WHERE WE
SEE THE VALUE OF IN SILICO
APPROACHING BARDA AND THE DRUGS
THAT CAN GET TO YOU ALL SOONER.
THERE ARE SEVERAL OFFICES WITHIN
BARDA FOCUSSED ON MEDICAL
COUNTER MEASURE DEVELOPMENT.
CBRN, FLU, DDDI AND DRIVE.
THAT'S THE DIVISION I SIT.
OUR MICKS IS TO SUPPORT THE
DEVELOPMENT AND ADOPTION OF
THESE INNOVATIVE TECHNOLOGIES
AND COUNTER MEASURES.
WE DON'T JUST SUPPORT THE
COUNTER MEASURE DEVELOPMENT WE
SUPPORT THE ENABLING
TECHNOLOGIES THAT ALLOW THE
PRODUCTS TO BE SUCCESSFUL.
YOU CAN ENGAGE DIRECTLY WITH US
IF YOU'RE INTERESTED IN REACHING
OUT AT HHS.gov YOU CAN ALSO
CHECK OUT SOME OF OUR PROGRAMS
AT DRIVE.HHS.gov.
YOU CAN ALSO ENGAGE WITH THE
REST OF BARDA IF YOU THERES OF
INTEREST.
EVERY SOLICITATION TOPIC HAS
POINTS OF CONTACTS IN IT WHERE
THE TOPICS ALSO YOU CAN FIND
POINTS OF MEDICAL COUNTER
MEASURES.gov.
WE OFTEN ENGAGE IN
PRE-SOLICITATION CALLS TAILORED
FOR THE AREA OF FROM OF THE
BARDA PROGRAM.
IF YOU HAVE A MORE GENERAL
INQUIRY OR TECHNOLOGY, I
ENCOURAGE YOU TO CHECK OUT OUR
PROGRAM AND THIS IS AN ABILITY
TO ENGAGE WITH ALL OF BARDA AS
WELL AS OTHER U.S. GOVERNMENT
PARTICIPANTS.
YOU CAN GET DIRECT FEEDBACK ON
YOUR TECHNOLOGY AND THAT'S AT
MEDICAL COUNTER MEASURES.gov.
SO, THIS IS WHERE YOU CAN
CONNECT WITH US.
I WANT TO PLUG BARDA INDUSTRY
DAY WE ANNOUNCED THE DATES IT'S
NEXT SUMMER.
I ENCOURAGE YOU TO FOL US ON
MEDICAL COUNTER MEASURES.gov FOR
UPCOMING INFORMATION AND THANK
YOU FOR BEING HERE AND
PARTICIPATING IN THIS IMPORTANT
MEETING AND HERE'S TO A
PRODUCTIVE MEETING AND LOOK
FORWARD TO LEARNING FROM YOU ALL
TODAY.
>> FANTASTIC, THANK YOU, KIM FOR
THE WONDERFUL OPENING REMARKS
AND LETTING US KNOW ALL ABOUT
BARDA.
OKAY, SO I'M GOING TO SHARE MY
SLIDES NEXT.
WHAT WE'LL DO IN THE NEXT 10
MINUTES GO OVER THE WORKSHOP
OBJECTIVES AND GOALS.
I'D LIKE TO INTRODUCE MY
COLLEAGUE, DR. ALEXEY ZAKHAROV
AND HE'S CO-CLAIR AND
INFORMATICS GROUP LEADER IN
NCATS IN THE EARLY TRANSLATION
BRANCH IN THE DIVISION OF PRE
CLINICAL INNOVATION.
SUBJECT MATTER EXPERT AND LEADS
MANY PROJECTS.
SO, SO WHAT I'LL DO IN THE NEXT
TWO SLIDES, I'LL GO OVER THE
PROGRAM AND ISLAND IT OVER TO
ALEXEY AND HE'LL TALK TO YOU
MORE ABOUT THE PARTICULAR
WORKSHOP.
OKAY.
SO, AS JONI MENTIONED IN HER
OPENING REMARKS, THE MISSION AND
THE FOCUS OF MY PROGRAM IS TO
TACKLE THE LACK OF
REPRODUCIBILITY AND
REPLICABILITY IN RESEARCH.
AS MANY KNOW THERE'S A VERY HIGH
RELEVANCE OF REPRODUCIBILITY IN
PRECLINICAL RESEARCH AND DATA
HAS SHOWN IN THE PAST DECADES IT
EXCEEDS 50%.
AND THIS IS ACTUALLY A STUDY WAS
DONE THAT SHOWS THIS LEADS TO
ABOUT $28 BILLION SPENT IN THE
U.S. ALONE PER YEAR ON
REPRODUCIBLE RESEARCH.
AND IT INCLUDES BIOLOGICAL
REAGENTS, MATERIALS, DATA DESIGN
AND DATA ANALYSIS AND REPORTING
AND PROTOCOLS.
WHAT WE THINK IS HAPPENING IS
THERE'S POOR TRAINING IN
EXPERIMENTAL DESIGN IN DATA
ANALYSIS AND PUBLICATIONS ARE
NOT REPORTING BASIC ELEMENTS OF
EXPERIMENTAL DESIGN.
WHAT ARE THE RESOURCES WE OFFER?
WE'RE HOPING TO ADVANCE THE
PRACTICE AND VIGOR OF
PRECLINICAL TRANSLATION AND HAVE
THREE PLAIN COMPONENTS AND
RESOURCES WE OFFER.
THE FIRST IS THE MANUAL E-BOOK.
A FREE BEST PRACTICES ONLINE
RESOURCE DEVOTED TO THE
SUCCESSFUL DEVELOPMENT OF EARLY
DRUG DISCOVERY ASSAYS SUPPORTED
BY 35 EDITORS ACROSS ALL SECTORS
AVAILABLE FOR THOSE VIEWING
ONLINE AND AT THE NATIONAL
LIBRARY OF MEDICINE AND OPEN
RESOURCE AND RUN WORKSHOPS AND
WEBINARS SIMILAR TO IT TODAY AND
THE OVER ALL GOAL IS TO
INTRODUCE AND MOST OF THE
WORKSHOP INCLUDING THIS ONE IS
GOING TO BE RECORDED.
IT'S BEING RECORDED AND
AVAILABLE ONLINE FOR VIEWING
AFTERWARDS.
WE ALSO DO COLLABORATIVE
RESEARCH FOCUSSED ON
REPRODUCIBILITY AND ROBUSTNESS
OF ASSAYS.
I'M SHOWING A QR CODE TO THE
RIGHT.
IT WILL TAKE US TO THE WEBSITE
AND IT'S A ONE-STOP SHOP YOU'LL
GET ALL OF THESE RESOURCES THERE
AFTER THE WORKSHOP.
NOW I'LL PASS AND HAND IT OFF TO
ALEXEY TO TALK MORE ABOUT THE
PARTICULAR WORKSHOP.
THE FLOOR YOURS.
>> THANK YOU.
I APPRECIATE IT.
I'M EXCITED TO SEE A LOT OF
PATIENTS OVER THE ZOOM AND NIH
VIDEOCAST.
OUR COMMITTEE HAS GOVERNMENT AND
ACADEMIA.
WE HAVE A SCIENTIFIC WHO MAKES
THE WORKSHOP HAPPEN.
AS WELL AS WE HAVE SUBJECT
EXPERTS HERE AND CHIEF FROM
BARDA AND AS WELL AS THE CHIEF
FROM ACADEMIA AND UCSD.
WE HAVE PARTICIPANTS.
SO FAR WE HAVE AROUND 3,000
REGISTRANTS FROM OVER 40
COUNTRIES.
WE HAVE AN ABOUT DISTRIBUTION.
58 FROM ACADEMIA AND 10% FROM
PHARMA AND 15% FROM BIO TECH AND
12% FROM THE GOVERNMENT.
NEXT TODAY AND TOMORROW WE'LL
PURSUE THE FOLLOWING GOALS.
WE WANT TO PROVIDE CONCEPTS AND
APPROACH AND BEST PRACTICES TO
SUCCESSFULLY DEVELOP AND
INTEGRATE ROBUST AND RIGOROUS
METHODS AND DISCUSSION IN TERMS
OF UTILIZATION OF THE METHODS
AND HOW THEY ADVANCE
TRANSLATIONAL SCIENCE AND DRIVE
DISCOVERY.
WE WANT TO SHARE KNOWLEDGE.
WE DON'T WANT TO MAKE THE
INFORMATIC PEOPLE FOR INFORMATIC
PEOPLE WE WANT TO TOUCH THE
BROADER AUDIENCE INCLUDING
CHEMISTRY AND BIOLOGISTS.
WE WANT TO ADDRESS
REPRODUCIBILITY AND
CONSIDERATIONS FOR DEVELOPING
NOT JUST ROBUST BUT REPRODUCIBLE
IN SILICO MODELS.
SECTION ONE IS TO BUILD MODELS.
SESSION 2 IS THE METHODOLOGY AND
ALGORITHMS.
SESSION 3 APPLICATION OF IN
SILICO METHODOLOGIES AND DRUG
DISCOVERY PROFS AND SESSION 4 IS
IN SILICO DRUG DISCOVERY AND THE
PANEL DISCUSSION WHICH WILL
BRIDGE THE GAPS IN
TRANSLATIONAL.
AS JONI MENTIONED TODAY, NCATS
TRANSLATIONAL INSTITUTE LOOKS
THAT THEY NOT ONLY TRANSLATE TO
ANIMALS AND ANIMALS TO THE
HUMAN.
WE WANT TO FACILITATE DISCUSSION
HOW A.I. AND MACHINE LEARNING
CAN HELP BRIDGE THE GAPS.
A BIT OF HOUSEKEEPING.
HERE IS THE AGENDA FOR THE DAY.
WE STARTED AND WE'RE GOING TO
FINISH AT 5:00 P.M.
WE'LL HAVE SEVERAL BREAKS AND
LUNGE BREAKS AT 12:15.
EACH SPEAKER WILL HAVE 15
MINUTES FOR THE TALK AND FIVE
MINUTES FOR QUESTIONS.
WE HAVE AROUND 3,000
PARTICIPANTS AND WE ENCOURAGE
YOU TO TYPE A QUESTION IN THE
Q&A PORTION OF THE ZOOM.
IF YOU SEE A QUESTION YOU LIKE,
PLEASE CLICK THE LIKE BUTTON SO
THE MORE LIKES WILL GET THE
PRIVATE TO BE ASKED.
DAY 2 HAS A SIMILAR SCHEDULE
FROM 11:00 A.M. TO 5:00 P.M.
WITH SEVERAL BREAKS AND WE HAVE
A UNIQUE SESSION.
THE PANEL DISCUSSION AS WELL AS
THE ADDITIONAL SESSION.
AND WE ENCOURAGE EVERYONE WHO
MAYBE ASKS A QUESTION OR COMES
UP WITH A NEW QUESTION PLEASE
TYPE THAT AND FACILITATE
DISCUSSION IN THE TIME SLOT.
WITH THAT SAID I WANT TO GIVE
SARINE THE OPPORTUNITY TO
INTRODUCE OUR KEY NOTE SPEAKER.
THANK YOU.
>> THANK YOU, ALEXEY.
WE'RE SUPER EXCITED TO INTRODUCE
OUR KEY NOTE SPEAKER, DR. MARTY
HEAD.
THE EXECUTIVE OF COMPUTATIONAL
AND DATA SCIENCES AT AMGEN
LEADING MECHANISTIC MACHINE
LEARNING AND COMPUTATIONAL
METHODS NO THE UNDERSTANDING OF
DISEASE BIOLOGY AND THE
DISCOVERY AND DESIGN OF BIOLOGIC
AND SYNTHETIC THERAPEUTICS AND
WORKED AT MULTIPLE INSTITUTIONS
TODAY WHERE SHE WAS DIRECTOR OF
COMPUTATIONAL BIO MEDICAL
INITIATIVE AND OF THE BIOLOGICAL
SCIENCES AND SPENT 20 YEARS IN
BIOLOGY AT GLAXOSMITHKLINE AND
WAS THE LEAD AT GSK AS THE TEAM
AND WAS TO CREATIVELY APPLY ALL
RELEVANT COMPUTATIONAL TOOLS TO
PROGRESSING DRUG DISCOVERY
EFFORTS FROM DRUG SELECTION TO
THE CANDIDATE FOR CLINICAL
TRIALS.
MAR
MARTY, WE LOOK FORWARD TO YOUR
TALK.
>> SO, IT'S A PLEASURE TO BE
WITH ALL OF YOU TODAY.
THANKS TO ROMI AND ALEXEY AND
GLAD TO SEE I WAS READING YOUR
MIND IN THINKING IING ABOUT THE
TOPICS I WANTED TO SHARE AND
THINKING ABOUT DRUG DISCOVERY
THEN AND NOW.
OF LATE WE SEE PAPER AFTER
PAPER, PRESS RELEASE, PRESS
RELEASE, PRESS RELEASE ABOUT
A.I. AND DRUG DISCOVERY.
IT FEELS TO LIKE TO ME WE HAVE
HYPE TO GO AND IT'S A RANDOM
SELECTION OF THINGS THAT WILL
SHOW UP IN GOOGLE IF SCHOLAR IF
YOU SEARCH FOR A.I. AND DRUG
DISCOVERY.
I COULD HAVE HAD HUNDREDS AND
THOUSANDS MORE STAMS.
I'D LIKE TO PAUSE AND TAKE A
DEEP CALMING BREATH HOW WE COMB
THROUGH IT TO IDENTIFY WAYS TO
ACCELERATE THE HUNT FOR SAFE AND
EFFECTIVE THERAPEUTICS THAT WILL
MAKE A DIFFERENCE FOR PATIENTS
AROUND THE WORLD.
WE'RE GOING TO TALK ABOUT THE
PREHISTORY AND MOVE ON TO
THINKING ABOUT THE NOW AND THEN
MAYBE A FEW THOUGHTS ABOUT HOW
WE SHOULD APPROACH THE FUTURE
FROM A PHILOSOPHICAL VIEW POINT.
I HOPE SOME OF THE THINGS I
SHARE TODAY WILL SET UP
COLLEAGUES IN THE FIELD FOR
THEIR TALKS IN THE COMING TWO
DAYS.
FIRST, I WANT TO POINT OUT THIS
PAPER FROM 1937 ABOUT THE EFFECT
OF STRUCTURE ON THE REACTIONS OF
ORGANIC COMPOUNDS ESPECIALLY
BENZENE DERIVATIVES.
THIS IS THE WORK OF LOUIS HAMET
LOOKING AT INITIALLY AS YOU
CHANGE SUBSTITUENTS AND LEARN
FROM THAT THE SAME SUBSTITUENTS
GO TO OTHER PROPERTIES.
I LIKE TO USE THIS EXAMPLE TO
REMIND EVERYONE WE HAVE BEEN
DOING MACHINE LEARNING SINCE
BEFORE THERE WERE MACHINES TO
LEARN ON.
THERE'S NOTHING NEW UNDER THE
SUN AND THERE'S MANY THINGS NEW
UNDER THE SUN.
WHAT IS IT THAT TOOK US FROM
DOING MACHINE LEARNING ON GRAPH
PAPER TO WHERE WE ARE NOW.
YOU'LL ALSO NOTE THE METROPOLIS
ALGORITHM MANY WORK WITH DAY IN
AND DAY OUT THAT EXISTED IN
THEORY AND IN THOUGHT AND IN
PAINSTAKING PRACTICE LONG BEFORE
THE COMING OF ANY ACT AND PLA
PLAIN -- MANIAC I'M SHOWING HERE
THE LEADING CUTTING-EDGE
COMPUTER INSTALLED LOS ALAMOS
AND THE COMPUTING ARCHITECTURE
ENABLED THE ABILITY TO APPLY
THIS ALGORITHM AND TO DO THIS
WORK THAT LEADS TO SO MANY WORK
IN METHODS EVEN NOW TODAY.
THESE ARE CONCRETE EXAMPLES OF
THE PAST AS COMPUTATIONAL
CHEMISTS.
I HAVEN'T EVEN NOTED AT THIS
TIME, QUANTUM CHEMISTRY,
MOLECULAR DYNAMICS, NEURAL NETS,
ALL CONCEPTS DEVELOPED IN THOSE
EARLY DAYS BEFORE THE ADVENT OF
COMPUTERS BUT THEN ENABLED JUST
REALLY A FLOURISHING OF NEW
THOUGHTS AND IDEAS BECAUSE OF
THE EXISTENCE OF THAT HARDWARE.
HERE AT THE BEGINNING OF THIS
TALK I'D LIKE TO OFFER EARLY
REFLECTIONS.
ONE OF THOSE IS THAT
COMPUTATIONAL CHEMISTRY IS A
DISCIPLINE WITH ITS OWN RICH
HISTORY AND AREAS OF RESEARCH
FOCUS THAT ARE WORTHY OF MINDS
AND ATTENTION ESPECIALLY FOR
THOSE WHO WORKED SO MANY YEARS
IN SMALL MOLECULE DISCOVERY.
WE FEEL LIKE THE NEW KIDS ON THE
BLOCK RELATIVE TO ORGANIC,
SYNTHETIC CHEMISTRY GOING ON FOR
CENTURIES.
BUT WE ARE A DISCIPLINE WITH A
HISTORY, WITH A PRESENT AND
FUTURE.
I'D ALSO LIKE TO POINT OUT THESE
NEW COMPUTATIONAL ARCHITECTURES
OPENS UP NEW WAYS OF COMPUTING
AND EVEN NEW WAYS OF THINKING.
AND FINALLY YOU MAY NOT HAVE
NOTICED IN THE PICTURE BEFORE
BUT TWO OPERATORS OF MANIAC AND
TWO AUTHORS ON THE METROPOLIS
PAPER WERE WOMEN AND THEY HAVE
BEEN IN COMPUTATIONAL CHEMISTRY
SINCE THE EARLY DAYS AND FOR
GROUPS AND COUNTRIES.
WE'RE A DIVERSE COMMUNITY THAT
COMES TOGETHER TO DO THIS WORK
TO ATTACK THIS DISCIPLINE AND
MAKE A DIFFERENCE.
NOW WE'LL TRANSITION TO WHAT HAS
CHANGED BETWEEN THEN AND NOW.
I DON'T THINK I'M GOING TO STEAL
THUNDER FROM STEVEN BURLEY BUT I
AM GOING TO USE PDB AS MY
EXAMPLE ON WHICH TO ANCHOR THIS
TRANSITION FROM THEN TO NOW.
WE CAN SEE THE PHENOMENAL GROWTH
OF THE NUMBER OF CRYSTAL
STRUCTURES AVAILABLE TO US TO
LEARN FROM, TO TRAIN ON, TO USE
FOR FUNDAMENTAL QUESTIONS ABOUT
SPECIFIC PROTEIN SYSTEMS.
SO, I'M GOING TO POINT OUT A FEW
LANDMARKS ALONG THIS GRAPH.
THE FIRST IS THE 1982 PAPER
COMING OUT THEIR LABS OF TECH IN
CLINICAL RESEARCH AT UCSF.
THE THING THAT'S AMAZING TO ME
ABOUT THIS IS THAT IN 1982,
IRWIN AND SEVERAL OTHER PEOPLE
FAMILIAR TO MANY OF US IN THE
ROOM WERE ALREADY THINKING ABOUT
PROTEIN LIGAND DOCKING AT THE
TIME THERE WERE ONLY 117 PROTEIN
STRUCTURES IN THE EDB.
THE EARLY NASCENT EDB.
IN ADDITION TO BEING ONE OF THE
MOST GRACIOUS HUMAN BEINGS I'VE
EVER HAD THE OPPORTUNITY TO MEET
HE WAS PRESCIENT HOW THE
COMPUTING TOOLS WITH THIS SMALL
AMOUNT OF DATA AT THE TIME COULD
LEAD TO BIG THINGS FOR THE
FUTURE.
I'LL GO ON TO MENTION THE CAS
COMPETITIONS A LARGE EXPERIMENT
TO PROACTIVELY AND PREDICTIVELY
ASSESS THE PERFORMANCE OF
ALGORITHMS FOR PREDICTING
PROTEIN STRUCTURES.
THIS IS THE EXACT TIME PERIOD
WHEN I WAS FINISHING MY Ph.D.
THINKING ABOUT GEOMETRIC
PROPERTIES OF PROTEIN STRUCTURES
AT DUKE UNIVERSITY WITH MICHAEL
PRESONT AND MOVING TO THE CENTER
FOR ADVANCED TECHNOLOGY FOR MY
POSTDOC AND I SAT OUTSIDE THE
OFFICES OF JOHN NOLT AND HAD THE
OPPORTUNITY TO LEARN FROM HIM
AND SEE HOW HE THOUGHT ABOUT THE
PROACTIVE STUDIES OF THESE
MODELS THAT WE'RE BUILDING
ACROSS THE PARTITION.
AND REALLY THINKING AHEAD OF
TIME HOW CAN WE RIGOROUSLY
UNDERSTAND HOW OUR ALGORITHMS
ARE TRULY PERFORMING.
MOVING FORWARD IN TIME, I CALL
OUT IN THE EARLY 2000s WORK I'M
HIGHLIGHTING HERE THE WORK I WAS
MOST DIRECTLY ASSOCIATED WITH
BUT AT THE SAME TIME, OUR
FRIENDS AND COLLEAGUES LIKE KATE
AND GEORGIA AND CHRISTOPHER AND
OTHERS AT MERCK WERE DOING
SIMILAR ANALYSES AND LOOKING
CRITICALLY AT HOW THOSE DOCKING
ALGORITHMS THAT HAD BEEN
DEVELOPED OVER THE YEARS FROM
1982 PUBLICATION, HOW WERE THEY
ARE PERFORMING ON THE DRUG
DISCOVERY PROGRAMS WE WERE DOING
IN THE PHARMACEUTICAL INDUSTRY?
HOW DO WE KNOW WHICH PROGRAM TO
USE?
HOW DO WE KNOW WHICH PREDICTIONS
IT TAKE SERIOUSLY AND WHICH TO
NOT?
SO A CONTRIBUTION I THINK WE IN
THE INDUSTRY MADE TO ACADEMIA
AND TO SOFTWARE DEVELOPERS IN
REALLY HAVING THAT KIND OF
ROBUST TEST ON DATA THAT SAT
BEHIND OUR PROPRIETARY BARRIERS.
AND OF COURSE THAT LED THROUGH
THE NIH AND THE FUNDING
MECHANISMS THERE TO FIRST THE
CESAR AND D3R RESOURCE THAT MADE
SOME OF THAT DATA FROM INSIDE
INDUSTRY CURATED AND AVAILABLE
FOR MAKING BLIND PREDICTIONS FOR
FOLKS WHO WERE NOT INSIDE OUR
WALLS TO BE ABLE TO EXPAND THAT
COMMUNITY OF PEOPLE TO USE THE
RIGOROUS EVALUATIONS OF TOOLS WE
WERE USING.
AND OF COURSE WE ALREADY HEARD
AND HIGHLIGHTED THE WORK OF
ALPHA FOLD IN 2021 PERFORMING
THE BEST IN THAT KAS COMPETITION
THAT JOHN CREATED ALL THOSE
YEARS AGO.
CREATING THE ABILITY FOR US TO
LEARN FROM THE STRUCTURES ALL
THE TIME AND HAVE PREDICTIVE,
PROSPECTIVE EVALUATION AT HAND
TO TEST OUT AND SEE THE STEP
CHANGE IN PERFORMANCE WHICH THEN
LED OF COURSE TO THE NOBEL PRIZE
EARLIER THIS YEAR.
ONE THING I'D SAY TO YOU AT THE
START OF YOUR CAREER NOW LOOK
BEING AT THIS HISTORY, LOOKING
AT THIS AMAZING WORK THAT'S
BEING DONE BY THESE NOBEL
LAUREATES AND OTHERS AROUND THE
RESEARCH COMMUNITY, DON'T WORRY.
THE WORK IS NOT DONE.
THERE IS STILL MUCH TO DO EVEN
IN UNDERSTANDING PROTEIN
STRUCTURE.
EVEN IN UNDERSTANDING BEYOND
PROTEIN STRUCTURE TO PROTEIN
DYNAMICS AND THE INTERACTIONS OF
PROTEINS WITH OTHER THINGS.
THERE IS STILL MUCH FOR US TO
DO.
SO I'M GOING TO TAKE A PAUSE
HERE TO OFFER MORE REFLECTIONS
BASED ON THAT EXAMPLE OF THE
WORK THAT'S BEEN TO ON OVER THE
YEARS.
FIRST IS THAT THE PDB GREW TO
ITS CURRENT SIZE BECAUSE OF A
STEADY ACCRETION OF DATA OVER
TIME.
THERE'S A ROUGHLY GEOMETRIC
PROGRESSION IN THE GROWTH OF THE
NUMBERS OF STRUCTURES DEPOSITED
EACH YEAR.
BECAUSE THE REPOSITORY EXISTS
FOR US THAT DATA HAS GROWN OVER
TIME TO APPROACH 250,000
STRUCTURES THAT ARE AVAILABLE
NOW.
MOST WERE GENERATED FOR PRIMARY
PURPOSE BECAUSE SOMEONE HAD A
SPECIFIC INTEREST IN A
PARTICULAR PROTEIN.
FOR A SPECIFIC DISEASE
INDICATION, FOR A SPECIFIC
FAMILY OF PROTEINS THAT THEY
WANTED TO UNDERSTAND HERE'S
WHERE I CALL OUT STRUCTURES FROM
THE EARLY DAYS THAT SOME PLAY
REMEMBER WHERE THERE WERE
LITERALLY HUNDREDS OF STRUCTURES
WITH SMALL CHANGES BEING MADE TO
CREATE POCKETS IN THE INTERIOR
MUCH THE PROTEIN.
RAT ONE TIME, A BIG PROPORTION
OF THE STRUCTURES IN THE PDB
WERE OF THIS ONE PROTEIN WITH
THESE STRUCTURES CREATED TO ASK
QUESTIONS OF PHYSICAL CHEMISTRY
ABOUT THE STRUSHGZ AND CALLING
THAT OUT BECAUSE BOTH THE
PRIME -- STRUCTURES AND CALLING
THAT OUT BECAUSE THE USE OF THE
STRUCTURES ARE BOTH IMPORTANT.
AND THE IMPORTANCE OF DATA
QUANTITY, DATA QUALITY, DATA
INTEGR
INTEGRITY WHERE I CALL OUT
PIONEERS WHO SAT WITH ME ALL
THOSE YEARS AGO AND ALL THE REST
OF THE PDB CREW WHO WERE
PRESCIENT IN CREATING A ROBUST
METAONTOLOGY TO DESCRIBE PROTEIN
STRUCTURES EVEN BACK TO THE
ORIGINAL PDB FORMAT THAT WAS
TRAPPED IN A CERTAIN NUMBER OF
CHARACTERS ON EACH LINE TO THE
FORMATS THAT ENABLED MORE
FLEXIBILITY IN THE METADATA.
THE TENACITY IN ENSURING THAT
THEY IMPOSE STANDARDS SO THAT
WHEN YOU SUBMIT A STRUCTURE TO
THE PDB, IT HAS THE DATA QUALITY
BAKED INTO IT AND HANGING OUT
THAT CARROT OF THE PUBLICATION
PUBLISHERS HAVING THE POLICY
THAT YOU MUST SUBMIT AND GET A
PDB CODE BEFORE YOU CAN PUBLISH
THE STRUCTURE.
THAT COLLECTION OF THINGS
ENSURED THAT THE DATA THAT IS IN
THERE IS RELIABLE FOR US TO USE
TO LEARN FROM AND EVEN MORE
IMPORTANTLY THE DECISIONS SORT
OF IN THE MID LIFE OF THE PDB TO
ENSURE STRUCTURE FACTORS WERE
INCLUDED AS WELL SO WE CAN GO
BACK AND REFINE STRUCTURES THAT
HAVE QUESTIONABLE THINGS AND ASK
METHODS OF IMPROVEMENTS.
THE FINAL REFLECTION I WANT TO
OFFER ABOUT THIS EXAMPLE I
SHARED WITH YOU IS THAT EACH OF
THOSE 250,000 STRUCTURES IS A
DYNAMICISH SNAPSHOT OF A
PARTICULAR STATE OF A PARTICULAR
PROTEIN CONSTRUCT.
AND SITTING IN A STRUCTURE FORM
AND THE CHALLENGE OF HAVING
MULTIPLE MODELS THAT MAP TO THE
DATA YOU SEE IN THE NMR
EXPERIMENTS AND IN CRYO-EM THE
TEMPERATURES AND MATRICES AND
GENERATING A STRUCTURE.
IT IS ALL A SNAPSHOT THAT HAS
ENCAPSULATES SOME OF THE MOTION
WE KNOW IS THERE BUT ONLY ONE
PICTURE OF WHAT THAT STRUCTURE
TRULY IS.
THIS IS MY OPPORTUNITY TO REMIND
ALL OF US AGAIN THAT EVEN WET
LAB EXPERIMENTS ARE A MODEL OF
THE THING THAT WE REALLY WANT TO
UNDERS
UNDERSTAND.
WE OFTEN CUT OFF THE KINASE
DOMAIN WITHOUT THINKING ABOUT
THE OTHER DOMAINS.
WE THINK ABOUT THEM, WE DO,
WE'RE CAREFUL SCIENTISTS BUT NOT
HAVING THE DATA THAT NECESSARY
TELLS US WHAT'S GOING ON WITH
THE EXTRA DOMAINS.
SO YES, A REMINDER FOR US ALL
THAT EVEN WET LAB EXPERIMENTS
ARE MODEL SYSTEMS OF THE BIOLOGY
THAT WE CARE ABOUT THAT GOES ON
WITH PATIENTS WHO HAVE DISEASE.
AND SO NOW WE'RE GOING TO MOVE
ON TO -- SO WHAT HAS CHANGED
SINCE THEN?
WE TALKED ABOUT THE GROWTH OF
DATA.
WE TALKED ABOUT HOW COMPUTING
WAS ORIGINALLY A TRANSFORMATIVE
ACT THAT ENABLED NEW WAYS OF
COMPUTING AND THINKING.
MY FATHER, MANY YEARS AGO BUILT
OUR FIRST COMPUTER.
OLD 8088 SITTING THAT LOOKED
LIKE THIS HERE AND MY PHONE
WHICH I'M KEEPING TRACK OF THE
TIME I'VE BEEN SPEAKING, HAS
MORE POWER THAT THAT COMPUTER
THAT SAT ON THE DESK IN OUR
HOUSE IN FLORIDA.
AND COMPETING ARCHITECTURE HAS
GROWN TO SYSTEMS LIKE THE N
VIDDIA DGX BOXES AND INSTALLING
FRONTIER, THE WORLD'S FASTER
SUPER COMPUTER.
THERE'S CAPABILITIES AVAILABLE
TO US AT THE TOUCH OF A COMPUTER
AND INCLUDING THE CLOUD
COMPUTING TIME WE HAVE A LOT
AVAILABLE TO US.
AS WE THINK TO THE FUTURE THERE
ARE NEW ARCHITECTURES COMING.
JONI MENTIONED QUANTUM
COMPUTING.
THERE'S A COURSE NEURO MORPHIC
COMPUTING NEURO PHYSICS AND IT
TAKES OUR UNDERSTANDING OF THE
BRAIN AS A WAY OF THINKING ABOUT
COMPUTE ARCHITECTURES FOR THE
FUTURE.
AND THERE'S UNTOLD THOUGHTS FOR
THE FUTURE NO ONE HAS DREAMT OF
YET OPENING UP NEW OPPORTUNITIES
FOR COMPUTING AND I'D LIKE TO
CHALLENGE YOU TO THINK ABOUT
WHAT IS THE NEW WAY OF THINKING
THAT THESE NEW ARCHITECTURES CAN
UNLEASH?
NOT JUST HOW CAN I TAKE
CLASSICAL ALGORITHMS AND
HOPEFULLY MAKE THEM RUN FASTER
ON THE VERY EXPENSIVE, VERY
ENERGY HUNGRY QUANTUM COMPUTERS
WE'RE BUILDING, BUT HOW DOES
THAT NEW WAY OF MOVING FROM BITS
TO KU
KUBITZ DEVELOP NEW INSIGHT AND
ACHIEVE TRANSLATIONAL VISIONS
ALL OF US HAVE?
I'M GOING TO TELL YOU A STORY
FROM THE SECOND HALF OF THE LAST
DECADE FROM THE EARLY DAYS WHEN
THE PUBLICATIONS STARTED SHOWING
UP IN THE RAPID FIRE OF FRENZY
OF PUBLICATIONS I SHARED WITH
YOU EARLY.
I WANT TO THINK ABOUT USING THE
EXAMPLE NOT TO SAY IT IS THE BE
ALL AND END ALL BUT WHAT CAN WE
LEARN FROM THE EARLY ERA OF A.I.
AND DRUG DISCOVERY THAT HELPED
TEACH US WHAT WE NEED TO DO NEXT
TO MAKE SURE THESE METHODS ARE
AS IMPACTFUL AS POSSIBLE.
I'LL TALK TO YOU ABOUT A
PUBLIC-PRIVATE PARTNERSHIP AT MY
TIME RAT GSK AND THE
PUBLIC-PRIVATE PARTNERSHIP UNDER
THE AUSPICES OF THEN VICE
PRESIDENT BIDEN'S CANCER
MOONSHOT.
THIS WAS A PROJECT AIMED AT
CREATING THE CLOSED ACTIVE
LEARNING GROUP WHERE WE HAD
PROPERTY FEEDING INTO GENERATIVE
MOLECULAR DESIGN ALGORITHMS AND
FEEDING THAT DATA INTO ACTIVE
LEARNING MECHANISMS WHEN WE SEE
OUR ACTIVE MODELS DRIFTING OUT
OF CONTROLS CAN WE FILL DESIGNS
TO FILL THE GAPS IN THE SPACES
WHERE COMPUTING WAS AGAINST.
CAN WE TRIGGER DESIGNS THAT MOVE
US TOWARDS THE MOLECULES THAT WE
NEED TO BE DRUGS.
SO MANY OF YOU WILL HAVE SEEN
MANY VERSIONS OF THIS KIND OF
THING IN THE YEARS SINCE WE
BEGAN AGGRESSIVELY APPLYING
THESE KINDS OF TECHNIQUES TO
DRUG DISCOVERY AND
COMPUTER-AIDED DRUG DESIGN.
WE DID TWO PILOT STUDIES AS PART
OF THIS PROJECT.
ONE HISTORICAL EXAMPLE FROM
KINASE INHIBITORS AT GSK.
THE SECOND SELECTIVE H1
ANTAGONIST WITH A COMPANY IN SAN
DIEGO.
SIMILAR DESIGN CRITERIA SHARED
BETWEEN THE TWO WITH SOME
SPECIFIC DESIGN CRITERIA FOR
EACH INDIVIDUAL ONES AND
SPECIFIC QUALITIESES --
QUALITIES AND QUANTITIES WE HAD
TO WORK WITH AND DID A NUMBER OF
STUDIES DOING GENERATIVE DESIGN,
MAKING MOLECULES, TESTING IT AND
DOING A PROSPECTIVE, PREDICTIVE
ASSESSMENT AND USING IT AND
LEARNING FROM IT IN ACTION
BUILDING THE PLANE WHILE WE'RE
FLYING AND LEARNING WHAT WORKS
WHILE IT'S FLYING.
WHAT CAN WE LEARN FROM THE ONE
EARLY EXPERIMENT IN THESE KINDS
OF METHODS?
THE FIRST THING WE CAN AND DID
LEARN IS GENERATIVE DESIGN AND
MULTI-PARAMETER OPTIMIZATION
SHIFTS THE DISTRIBUTION OF
PREDICTIVE AND MEASURED
PROPERTIES IN THE DESIRED
DIRECTION.
SO, JUST A COUPLE OF EXAMPLE OF
GRAPHS OF PAPERS FROM COLLEAGUES
AT THAT TIME LOOKING AT THE
AURORA KINASE EXAMPLE WHERE THE
TRAINING DATA AND THE FIRST SIX
MONTHS DATA THAT IS MEASURED IS
GRAPHED HERE AND THE BEST 250
OVER THOSE ITERATIONS, YOU CAN
SEE WE HAVE MOVED THE PROPERTIES
OF SELECTIVITY IN THE DIRECTION
WE WANTED AND ALSO BEGAN
SHIFTING SOME OF THE
PROPERTIES -- THE OTHER
PROPERTIES THAT GO INTO MAKING A
DRUG THAT WE CARE ABOUT AS WELL.
OTHER SNAPSHOTS TO LET YOU SEE
THIS IN ACTION AS WE GO THROUGH
ITERATIONS OF THE GENERATIVE
DESIGN ALGORITHM SEEING THE
DESIGN MOLECULES PULL AWAY FROM
THE SPACE OF THE ONES WE STARTED
WITH BOTH THERE AND IN THIS
PICTURE.
AND SO THEY ABSOLUTELY TAKE THAT
WHOLE DISTRIBUTION AND MOVE IT
IN THE RIGHT DIRECTION.
HOWEVER, IT IS ABSOLUTELY TRUE
THAT SPECIFIC PREDICTIONS FOR
SPECIFIC MOLECULES ARE VERY
UNLIKELY TO BE ACCURATE.
AND SO IF WE TREAT THE NUMBER
FOR SOLUBILITY AS A PREDICTION
OF WHAT IT WILL BE, WE'RE USING
IT IN THE WRONG WAY.
IT IS NOT A PREDICTION OF THE
NUMBER.
IT'S A PREDICTION AROUND THAT
DISTRIBUTION AND WHERE IT'S
HEADING.
SO JUST A COUPLE SNAP SHOTS.
THIS IS THE PREDICTIVE MODEL FOR
A TYPICAL EXPERIMENTAL MODEL FOR
QT PROLONGATION THE
PHYSIOLOGICAL OBSERVABLE WE WANT
TO UNDERSTAND.
YOU CAN SEE IT HAS SOME SIGNAL
THERE BUT IT IS FAR FROM
PERFECT.
SOME OF OUR MODELS ARE EVEN
WORSE.
THERE'S SORT OF A SIGNAL BUT A
WHOLE LOT OF MOLECULE PERFORMING
NOT PARTICULARLY WELL.
SOME ARE AT THE BOTTOM OF THE
ASSAY MEASURABILITY WHICH MAY
EXPLAIN IT BUT NONETHELESS IT IS
CLEAR IT'S NOT A LOUD SIGNAL
SHOWING UP HERE AND THE MODELS
ARE FAR FROM PERFECT AND SHOULD
NOT USE THEM FROM SIGNAL POINT
PREDICTION BUT ENOUGH SIGNAL TO
MOVE THAT DISTRIBUTION IN THE
DIRECTION WE WANT IT TO GO.
IT HIGHLIGHTS THE ESSENTIALITY
LOOP OF WET LAB AND DRY LAB IN
THE PROCESS.
JUST A THOUGHT THAT OCCURS TO ME
FROM THE TIME I'VE WATCHED THIS
WORK GOING ON, I THINK IT IS
HELPFUL IF WE THINK ABOUT SOME
OF AS THE GENERATIVE DESIGN
ALGORITHMS BY THINKING ABOUT THE
LANGUAGE THAT IS APPROPRIATE FOR
THE OBJECT WE'RE DESIGNING AND
THE TASK WE'RE DESIGNING FOR.
SO LET ME TELL YOU ABOUT MORE
ABOUT WHAT I MEAN ABOUT THAT.
MANY PEOPLE IN THIS VIRTUAL ROOM
WILL BE FAR MORE EXPERT ON THIS
THAN I.
SO I'M GOING TO JUST GIVE YOU A
SOMEWHAT HIGH LEVEL DESCRIPTION.
BUT IN THE EARLY DAYS, WE WERE
VERY MUCH USING VARIATIONAL AUTO
ENCODERS FOR DESIGN AND SPACE.
WE WOULD TAKE OUR MODEL AND
ENCODE IT INTO THAT CONTINUOUS
SPACE AND THEN DECODE IT AT THE
BACK END.
I WAS VERY LAZY WHEN I WAS
GENERATING THIS LOOP OF GRAPHIC
JUST USING THE SAME MOLECULE
INPUT AND OUTPUT.
BUT GENERALLY YOU'D GET MUCH
DIFFERENT MOLECULES AS YOU
NAVIGATED WITH SPACE.
AND OFTEN TIMES BECAUSE -- LET
ME STEP BACK AND SAY THIS AGAIN.
MOLECULES CAN BE THOUGHT OF AS
CONTINUOUS.
THEY'RE CLEARLY CONTINUOUS
DISTRIBUTION OF ELECTRON DENSITY
ACROSS A SPACE THAT EXISTS IN
THE WORLD.
BUT THEY ACT AS IF THEY WERE
ATOMS CONNECTED WITH BOND ANGLES
AND ROTATIONS.
A CONTINUOUS REPRESENTATION OF
THIS ORGANIC CHEMISTRY VIEW OF A
SMALL MOLECULE IS A MISMATCH IN
LANGUAGE STYLE AND LEADS TO US
GENERATING MOLECULES THAT ARE
SOMETIMES UGLY THAT CHEMISTS
CAN'T MAKE.
SO WE SPEND A LOT OF ENERGY
NEEDING TO FILTER AND APPLY
OTHER APPROACHES TO FORCE OUR
MOLECULES THAT WE DESIGN INTO
REASONABLE CHEMICAL SPACES.
CALLING THAT OUT HERE THAT WE'VE
DRAWN IT AS CHEMICALLY
REASONABLE BUT IT'S NOT ALWAYS
HOW THE ALGORITHMS MAKE BUT THE
MODELS FOR HUMAN LANGUAGES
ALTERS AN ALTERNATIVE PATH
FORWARD AND SO WE HAVE A
LANGUAGE FOR DESCRIBING SMALL
MOLECULES.
WE HAVE MORE THAN ONE BUT THIS
IS SMILES AND THIS IS A MOLECULE
SIMPLE ENOUGH I COULD WRITE THIS
BY HAND AND UNDERSTAND IT BUT WE
CAN ALSO DESCRIBE MORE
COMPLICATED MOLECULES THAT ARE
LESS SIMPLE TO WRITE BY HAND
USING THIS LANGUAGE.
FOR THOSE OF YOU WHO ARE
ASSOCIATED WITH AMGEN, YOU MUST
RECOGNIZE THIS AS OUR MARKETED K
RAS INHIBITOR FOR CERTAIN KINDS
OF CANCERS.
BECAUSE WE HAVE THIS LANGUAGE WE
CAN USE WE CAN USE TECHNIQUES AS
A CORE DESIGN MODULE.
WE CAN MASK PARTS OF THE
STRUCTURE AND LEARN WHAT
TRANSFORMATIONS ARE POSSIBLE AND
MAKE PREDICTIONS OF A HUGE
NUMBER OF PROPERTIES ABOUT THOSE
MOLECULES AND WE CAN GENERATE
NEW MOLECULE DESIGNS.
SO THIS APPROACH OFFERS AN
ALTERNATIVE PATH WE EXPECT
YOU'LL HEAR MORE ABOUT AS WE GO
THROUGH THESE TWO DAYS FOR
THINKING ABOUT HOW WE DO
GENERATIVE DESIGN OF SMALL
MOLECULE STRUCTURES.
THE SAME APPROACHES ARE EQUALLY
APPLICABLE TO LARGE MOLECULE
THERAPEUTICS TO ANTIBODIES AND
PROTEIN THERAPEUTICS AND ENZYMES
AS THERAPEUTICS.
THEY'RE THE NATURAL LANGUAGE
WE'RE USING IS THE LANGUAGE OF
AMINO ACIDS AND WE CAN TAKE THE
MODELS GENERATED IN A DIFFERENT
SPACE AND APPLYING THEM IN A NEW
AND FRESH WAY TO THE QUESTIONS
THAT WE CARE ABOUT IN DRUG
DISCOVERY.
SO JUST A FEW CONCLUDING
REFLECTIONS FOR ALL OF US HERE
AND THEN TURN IT OVER TO
QUESTIONS AND COVERNVERSATIONS M
THE AUDIENCE.
FIRST THING I WANT TO SAY IS,
YES, THERE'S BEEN AMAZING,
AMAZING INNOVATIONS AND
SUCCESSES BASED ON USING A.I.
FOR IN SILICO DRUG DISCOVERY.
IF WE RELY ON THAT ALONE IT WILL
FAIL.
IT TAKES MORE THAN THAT TO
DISCOVER A DRUG THAT ACTUALLY IS
SAFE AND EFFICACIOUS FOR OUR
PATIENTS.
AND SO WE NEED TO THINK ABOUT A
WHOLISTIC VIEW ON THE
COMPUTATIONAL SIDE AND ANALYTICS
IN FRONT OF THE END USER AND
ALLOWS THIS EM TO -- THEM TO
MAKE SENSE AND ALGORITHMS TO
EXPOSE PATTERNS IN THE DATA THAT
LET THEM MAKE DECISIONS BASED ON
THE DATA THEY HAVE AT HAND.
AND OF COURSE PREDICTIVE MACHINE
LEARNING AND GENERATIVE
ARTIFICIAL INTELLIGENCE
ALGORITHMS HAVE AN IMPORTANT AND
EXCITING ROLE FOR US NOW AND IN
THE FUTURE AS ONE OF THE TOOLS
WE USE.
BUT WE ALSO SHOULDN'T ABANDON
THE PHYSICS BASED SIMULATIONS
WE'VE DONE SO MANY YEARS THAT
ANSWER QUESTIONS AT A DIFFERENT
LEVEL OF DETAIL THAT PROVIDE US
WITH MECHANISTIC UNDERSTANDING,
MECHANISTIC HYPOTHESES THAT LET
US PROBE THE SYSTEM IN A
DIFFERENT WAY.
FINALLY, THINKING ABOUT SYSTEMS
MODELLING.
WE HEARD JONI ALLOWED TO THIS
EARLIER AS WELL.
SINCE THE LATE '80s, EARLY '90s
AS AN INDUSTRY, WE MADE A BET ON
RATIONALE DESIGN AS THE WAY TO
DO DRUG DISCOVERY AND MADE THE
ASSUMPTION A SINGLE MOLECULE
INTERACTING WITH A SINGLE
PROTEIN WILL HAVE A BENEFICIAL
IMPACT ON DISEASE AND ON HEALTH.
WE ALL KNOW THE SITUATION IS
MORE COMPLICATED.
WE KNOW THERE ARE NETWORKS OF
PROTEINS AND CO-FACTORS AND
OTHER BIOLOGICAL FACTORS THAT
INTERACT IN A VERY COMPLICATED
NETWORK AND WE NEED TO BE
THINKING ABOUT NOT JUST A.I.
APPROACHES TO THIS BUT A HUGE
COLLECTION OF APPROACHES TO
UNDERSTANDING IF I IMPINGE ON
THE COMPLICATED SYSTEM IN ONE OR
TWO OR MORE PLACES, HOW DOES
THAT AFFECT PERCOLATE THROUGH
THE SYSTEM TO HAVE THAT IMPACT
ON DISEASE THAT WE WANT TO HAVE?
DRUG DISCOVERY IS HARD.
THE THINGS WE NEED TO HAVE IN A
SINGLE MOLECULE TO ENABLE US TO
HAVE A HIGH PROBABILITY THIS
MOLECULE WILL BE SUCCESSFUL IN
GETTING TO THE SITE OF ACTION
AND HAVING THE IMPACT THAT WE
NEED FOR EFFICACY, WHILE ALSO
BEING SAFE AND NOT HAVING OFF
TARGET EFFECTS OR EFFECTS
RELATED TO THE TARGET AND
KNOWING WE CAN MANUFACTURE THE
MOLECULE IN SUFFICIENT
QUANTITIES TO MAKE A DIFFERENCE
ALL HAVE TO BE IN THE SPECIAL
MOLECULES THAT TRANSLATE ONWARD
IN THE CLINIC AND FOR US TO GET
THERE AND TO SOLVE THOSE HARD
PROBLEMS, WE REQUIRE AN
INTEGRATED SET OF DRY LAB AND
WET LAB TOOLS TO MAKE PROGRESS.
FINALLY, I HOPE YOU'VE HEARD A
THREAT COMING THROUGH IN THE
REMARKS THAT AS WE CARRY OUT OUR
COMPUTATIONAL EXPERIMENTS, WE
NEED TO DO SO WITH RIGOR.
RIGOR IN THE WAY DATA HAS BEEN
COLLECTED.
RIGOR IN THE WAY DATA HAS BEEN
ORGANIZED AND CATEGORIZED AND
HAD THE METADATA PLACED AROUND
IT.
RIGOR IN THE WAY WE APPROACH OUR
COMPUTATIONS AND BEING VERY
CAREFUL TO NOT FOOL OURSELVES.
MANY HAVE HEARD ME SAY MANY
TIMES THAT I LOVED MOVING TO
COMPUTATIONAL CHEMISTRY BECAUSE
I NEVER GOT TAR IN MY TEST TUBE.
MY CULTURES NEVER DIED I NEVER
HAD MY EXPERIMENT -- MY
EXPERIMENT ALWAYS WORKED UNLESS
THE PROGRAM CRASHED AND THAT'S
BOTH A STRENGTH AND WEAKNESS.
I ALWAYS GET AN ANSWER BUT HOW
DO I KNOW IF THAT ANSWER HAS ANY
MEANING FOR DECISION MAKING FOR
DRUG DISCOVERY.
THAT IS THE PLACE WHERE WE HAVE
TO BE TRULY RIGOROUS AND NOT
FOOL OURSELVES.
AND FROM THE BEGINNING OF MY
TALK I LIKE TO REITERATE THAT
COMPUTATIONAL CHEMISTRY IS A
DISCIPLINE WITH ITS OWN RICH
HISTORY AND IT'S OWN EXCITE
BEING FUTURE AND THAT WOMEN AND
THE DIVERSE COMMUNITY OF
PRACTITIONERS HAVE BEEN IN THE
FIELD IN THE EARLIEST DAYS AND
WILL BE ON TO THE FUTURE AS
WELL.
THE BODY OF WORK I TALKED TO YOU
ABOUT TODAY CAME FROM THIS SET
OF ORGANIZATIONS AND WITH THAT,
SARINE, I THINK I DID OKAY ON
TIME.
>> PERFECT.
>> I AM GOING TO STOP SHARING
AND WE CAN GO TO A Q&A SESSION.
>> THANK YOU SO MUCH, MARTI.
SO INSPIRING TO HEAR ABOUT ALL
YOUR REFLECTIONS IS AMAZING AND
TO HEAR A LITTLE BIT ABOUT ASSAY
AS WELL BECAUSE I WAS AN ASSAY
BIOLOGIST AND WHAT A WONDERFUL
COLLABORATION AND YOU TALKING
ABOUT WOMEN BEING PART OF THIS
IS JUST EXTREMELY INSPIRING.
THANK YOU SO MUCH FOR DOING
THAT.
LOTS OF QUESTIONS, MARTI.
WE'LL START TACKLING SOME OF
THEM.
THANK YOU SO MUCH FOR EVERYONE
THAT ASKED QUESTIONS.
WE'RE GOING TO TACKLE AS MANY AS
WE CAN TODAY.
SO FIRST QUESTION WE HAVE HERE
HAS ANY STUDY COMPREHENSIVELY
COMPARED THE EXPERIMENTAL
PROTEIN STRUCTURES IN THE PDB TO
THEIR ALPHA FOLD MODELS?
>> I FEEL CERTAIN THAT WORK HAS
BEEN DONE BUT I CAN'T POINT TO
ANY SPECIFIC EXAMPLE.
AS WE HAVE STEVEN COME BEING
LATER IN THE SERIES, I WOULD
DEFINITELY WANT TO BE ASKING HIM
THAT QUESTION.
I WILL SAY THAT IN SOME OF
THE -- I DON'T ACTUALLY WORK FOR
A LIVING SO MUCH ANYMORE.
I JUST TALK TO PEOPLE FOR A
LIVING BUT WHEN I'VE GONE BACK
AND LOOKED TO STRUCTURES,
THEY'RE CLOSE ENOUGH FOR DOING
SOME WORK WITH AND THERE ARE
SOME QUESTIONS ESPECIALLY SINCE
THE EARLIEST VERSIONS OF THAT
ALGORITHM DIDN'T INCLUDE
CO-FACTORS OR LIGANDS WE KNOW.
FOR PROTEINS THAT DON'T HAVE
STRUCTURAL MODELS, THERE'S
CLEARLY SOME THINGS FOR US TO
LOOK ON THERE AND THAT'S
EXCITING.
I'M SURE THERE'S STUDIES LIKE
THAT.
IF ANYONE IN THE AUDIENCE KNOWS
OF SOME, FEEL FREE TO DROP THEM
IN THE CHAT FOR FOLKS TO PAY
ATTENTION TO.
>> THANK YOU, MARTI.
BECAUSE THIS IS A DISCUSSION AND
I SEE STEVEN ON THE LINE HERE,
WOULD YOU LIKE TO ADD ANYTHING
TO THAT IF YOU ARE LISTENING?
>> WHAT ARE THE FUTURE TRENDS OR
A.I. METHODS LIKE ALPHA FOLDS?
>> ALL THE ABOVE.
IT'S A VERY LAME ANSWER BUT
HAPPENS TO BE WHAT I TRULY
BELIEVE.
I DO THINK QUANTUM COMPUTING AND
THINKING IN KUBBITZ MAYBE OPENS
UP POSSIBILITIES FOR US TO THINK
ABOUT THE STATE FUNCTION AND A
RICHER WAY TO THINK ABOUT THE
BOLESMAN DISTRIBUTION BECAUSE
WE'RE THINKING IN KUBITZ.
I NEVER WANT TO MISS A CHANCE TO
SAY THERE'S ALTERNATIVES TO
QUANTUM COMPUTING THAT MAY BE
EQUALLY INTERESTING AND RICH AND
MAY HAVE A LOWER ENERGY
FOOTPRINT THAN QUANTUM
COMPUTING.
SO YES, THESE NEW METHODS OF
COMPUTING, ABSOLUTELY TRUE AND
YES, METADYNAMICS AND UNDERSTAND
GOING ON IN THE PROTEIN SYSTEMS
AND HOW THEY INTEGRATE INTO
NETWORKS AND I CLEARLY BELIEVE
THAT'S A PLACE WE SHOULD PAY
ATTENTION TO BUT I'D REALLY
CHALLENGE ALL OF YOU AS YOU
THINK ABOUT THINKING OF THE
TRANSLATIONAL NEEDS OF OUR
PATIENTS, WHAT ARE THIS OPEN
QUESTIONS OUT THERE AND HOW CAN
WE THINK OF KNITTING TOGETHER
THE TECHNIQUE AND TACKLING NEW
ONES THAT TACKLE FUNDAMENTAL
QUESTIONS.
THAT'S WHERE THE NOBELS FOR
CHEMISTRY AND MEDICINE AND
PHYSICS WILL COME IN THE FUTURE
IS FROM THINKING ABOUT IT FROM
THAT ANGLE AND USING THESE AS
TOOLS.
>> THANK YOU SO MUCH, MARTI.
MORE QUESTIONS SO, LET'S TACKLE
SOME MORE.
HOW CAN WE IMPROVE THE
VALIDATION OF A.I. M.L. S.
>> MY FRIEND WILL TALK TO YOU
TOMORROW ABOUT THAT EXACT TOPIC.
I DO KNOW I HAVE BIASES FROM MY
OWN HISTORY WHERE A DEEP
PREFERENCE FOR FEWER MODEL
PARAMETERS RATHER THAN MORE
MODEL PARAMETERS OF AN INTENSE
DESIRE WE NOT OVER FIT OUR
MODELS JUST BECAUSE WE HAVE SO
MUCH SPACE AND SO MUCH ROPE TO
HANG OURSELVES AND WOULD HEAR A
THEME AROUND THE NEED FOR
PROSPECTIVE, BLIND CHALLENGES
WHERE WE DON'T ALREADY KNOW THE
ANSWER AND MAKE BOLD AND
PREDICTIVE PREDICTIONS WE THEN
GO BACK AND TEST AND THEN
FINALLY ALWAYS HAVING VERY
CAREFULLY AND WELL CHOSEN NUL
MODELS TO WHICH WE COMPARE.
SO IT IS NOT ENOUGH TO SAY MY
NEW METHOD DOES GREAT ON THE
SYSTEMS.
IT NEEDS TO HAVE UNDERSTANDING
AND NEEDS TO COMPARE TO OTHER
MUCH SIMPLER MODELS SO WE KNOW
THE TRUE ADDED VALUE OF THE TIME
AND ENERGY AND INTELLECT WE'RE
SPENDING ON THAT MODEL.
BUT I AM GOING TO STOP THERE
BECAUSE I KNOW THAT THERE'LL BE
INTERESTING AND USEFUL STUFF
WITH THAT TOMORROW.
>> THANK YOU SO MUCH.
THAT WAS VERY HELPFUL.
THANK YOU.
ANOTHER QUESTION HERE, HOW DOES
THE CONCEPT OF GENERATIVE
MOLECULAR DESIGN IS DIFFERENT
FROM SCAFFOLD OR ATOM HOPPING?
>> HMM.
INTERESTING QUESTION.
IN SOME WAYS IT'S NOT REALLY
THAT DIFFERENT.
JUST LIKE I SAID WE'VE BEEN
DOING MACHINE LEARNING SINCE
BEFORE THERE WERE MACHINES TO
LEARN ON.
WE PROBABLY HAVE BEEN DOING
GENERATIVE DESIGN SINCE BEFORE
THERE WERE GENERATIVE A.I.
ALGORITHMS.
SO IN SOME WAYS IT'S A NEW NAME
FOR AN OLD CONCEPT.
SOME OF THE NEW ALGORITHMIC
THINGS ARE NOVE -- INNOVATIONS
BUT WHEN I WAS USING FLOW, QXP,
ALGORITHM IN THE OLDEST WAYS
YOU'VE SEEN, WE HAVE THE ABILITY
TO IDENTIFY CONNECTION POINTS
AND TAKE A SET OF REAGENTS AND
DO THE COMPUTATIONAL SYNTHETIC
CHEMISTRY TO EXPLORE WHAT
HAPPENS AS TO THE FINDING AND
NOT GREAT PREDICTIVE BINDING
AFFINITY AS YOU MAKE THOSE PAIR
WISE CHANGES.
SOME OF WHAT'S DIFFERENT IS THE
INCREASE IN THE AMOUNT OF
COMPUTE WE HAVE AVAILABLE AND
THE APPROACHES FOR DOING THE
MULTI-OBJECTIVE OPTIMIZATION.
THOSE TOO HAVE A PRECEDENT IN
THE PAST WORK WE'VE DONE.
AND SO THIS IS THE PLACE WHERE I
STOP AND I SAY AGAIN, THERE IS
NOTHING NEW UNDER THE SUN BUT
THERE'S ALWAYS INNOVATION AND
GROWTH AND CREATIVITY FOR THE
FUTURE.
>> THANK YOU SO MUCH, MARTI.
WE'LL TAKE ONE MORE QUESTION
BECAUSE THERE'S SO MANY.
I DON'T WANT TO BLEED INTO THE
LUNCH BUT WANT TO TAKE ONE MORE
QUESTION.
>> I WILL SAY I'LL STAY ON AND
AT LEAST TYPE IN A FEW ANSWERS
TO THE QUESTIONS THAT ARE IN THE
CHAT BEFORE I LOG OFF AND GO
FIND MY LUNCH HERE.
>> THAT WOULD BE SUPER NICE OF
YOU.
WE APPRECIATE IT.
HERE'S ONE MORE QUESTION TO
ANSWER WHY AND PAMARTI WILL TRYO
ANSWER AS MUCH AS THE CAN AFTER.
AND WE'RE IN NEED OF MORE DATA
TO TRAIN IN SILICO MODELS?
>> BIOLOGICS.
BACK IN MY DAYS AT GSK, EVERY
MOLECULE MADE AND REGISTERED
WENT THROUGH A SET OF STANDARD
ASSAYS.
THOSE ASSAYS AS WE NOTED ARE
MODEL SYSTEMS FOR THE THING WE
CARE ABOUT.
BUT IT MEANT OVER TIME WE HAD
MILLIONS AND MILLIONS AND
MILLIONS OF DATA POINTS.
IN THE SAME ASSAY RUNNING ON THE
SAME ROBOTICS BY THE SAME
OPERATOR OVER MULTIPLE YEARS.
SO ON THE OTHER HAND, TO CREATE
A BIOLOGIC TAKES TIME AND
EXPRESSING SOMETHING IN CELLS
THAT CAN DIE ON YOU SO IT'S A
PLACE ON MY MIND.
ANOTHER PLACE ON MY MIND IS
AROUND NOW THAT I HAVE THE
PRIVILEGE TO WORK WITH THE
COMPUTATIONAL BIOLOGY TEAM IN MY
DEPARTMENT, THINKING ABOUT WHAT
ARE THE WAYS THAT LET US TEASE
APART BIOLOGY IN MORE
PHYSIOLOGICALLY RELEVANT MODEL
SYSTEMS RECOGNIZING THOSE TWO
ARE MODELS BUT THAT DATA FEELS
IMPORTANT TO ME AND REALLY THE
DATA THAT WOULD ALLOW US TO
START THINKING IN THIS MORE
SYSTEMS LEVEL MODELLING
PERSPECTIVE IS A PLACE THAT
FEELS ANOTHER PLACE THAT FEELS
RICH TO ME.
>> THANK YOU SO MUCH, MARTI,
THAT WAS SUPER HELPFUL.
SO SO, WE'RE GOING TO GO TO A
LUNCH BREAK AND NOW BACK AT
12:45.
MARTI, AGAIN, THANK YOU FOR ALL
OF US AT THE ORGANIZING
COMMITTEE AND ATTENDEES.
WHAT A WONDERFUL PRESENTATION.
IF YOU CAN JUST ANSWER SOME
QUESTIONS LIKE YOU SAID, THAT
WOULD BE FANTASTIC.
EVERYONE, I'LL SHARE MY SLIDE
DECK SO YOU SEE ON YOUR SCREENS
THE AGENDA.
SO WE DO HAVE A LUNCH BREAK
RIGHT NOW AND WE WILL BE BACK
EXACTLY AT 12:45 TO CONTINUE OUR
WORKSHOP WITH SESSION 1.
SUPER EXCITED.
BE BACK EXACTLY AT 12:45. THANK YOU SO MUCH.
>> THE SESSION'S OBJECTIVE IS TO
PROVIDE BEST TRACTS IN BUILDING
AND MAINTAINING DATABASES USED
FOR DEVELOPING ROBUST AND
RIGOROUS A.I.-BASED DRUG
DISCOVERY MODELS AND METHODS.
AND OUR FIRST SPEAKER FOR THE
SESSION I'M EXCITED TO INTRODUCE
DR. BARBARA ZDRAZIL AND HAS ABLE
ACADEMIC TRACK RECORD AND
INTERNATIONAL REPUTATION AND
WORKS AS CHEMBL COORDINATOR AT
THE INSTITUTE IN CAMBRIDGE, U.K.
AND AIDS IN DRUG DISCOVERY DATA
AND LOWER THE BARRIERS TO USAGE
GLOBALLY AND AUTOMATE DATA
EXTRACTION AND CURATION
PROCESSES WITH LOSS IN DATA
QUALITY AND TODAY HER TALK IS
ENTITLED 15 YEARS OF CHEMBL
CHALLENGES AND OPPORTUNITIES.
THE FLOOR IS YOURS AND LET YOU
SHARE YOUR SLIDES.
THANKS FOR THE KIND INTRODUCTION
AND OPPORTUNITY TO SPEAK HERE
TODAY.
THANKS A LOT TO THE ORGANIZERS.
I'D LIKE TO TALK TO YOU ABOUT
CHEMBL DRUG DISCOVERY DATABASE
HOPEFULLY MOST HAVE HEARD ABOUT
OR USED THE DATA.
AT THE OCCASION OF CHEMBL'S 15th
ANNIVERSARY WE RECENTLY LAD A
SYMPOSIUM AND AND WANTED TO
CHANGE PICTURES OF FORMER AND
CURRENT GROUP MEMBERS.
YOU SEE IN THE ABOVE PART OF THE
PRESENTATION WE HAD A NICE CAKE
AND PEOPLE ATTENDING.
THAT WAS A NICE THING TO DO.
AND BEFORE I START TALKING ABOUT
CHEMBL, I WANTED TO BRIEFLY
INTRODUCE OUR INSTITUTE.
SO I WORK AT THE EUROPEAN
BIOINFORMATICS INSTITUTE AND
THIS IS PART OF A LARGER
EUROPEAN INFRASTRUCTURE OF THE
EUROPEAN MOLECULAR LABORATORIES.
THERE ARE SIX DIFFERENT SITES
YOU CAN SEE ON THE SCREEN.
AND WE ARE BASICALLY FUNDED
LARGELY BY THE 20 MEMBER STATES
OF EMBL.
THE EBI WHERE I WORK HOST OPEN
RESOURCES FOR THE LIFE SCIENCES.
A FOCUS ON BIG DATA AND SEE THEM
ON THE SLIDE GROUPED BY
DIFFERENT TOPICS AND WE ARE
REALLY SO CAMPBELL IS IN THE
SECTOR OF SMALL MOLECULES AND WE
HOST THE CHEMICAL ENTITIES OF
BIOLOGICAL INTEREST DATABASE AND
THERE ARE OTHER RESOURCES AND
DATABASE FOR INSTANCE AND THE
DATA IN EUROPE AND THOUGH MOST
DON'T FOCUS ON SMALL MOLECULES
THEY REFERENCE OR INCLUDE SMALL
MOLECULE DATA AND THIS LINKS
WITH THE ACTIVITY DATA AND THE
STRUCTURAL DATABASE DATA.
WATCH OUT FOR THAT.
AND OUR CAMPUS IS GROWING SO
THERE'S A FOCUS TO INCREASE AND
EXPAND THE BIO INDUSTRIES AND IT
WILL BE ONE OF THE HIGHEST IN
COMPUTATIONAL BIOLOGY.
KEM BALL IS A DATABASE WHICH IS
NOW QUITE REACHED ITS 15 YEARS
THIS OCTOBER.
WHAT IS INTERESTING TO NOTE IS
OVER TIME CHAM -- CHEMBL HAS
GROWN IN SIZE AND IN TERMS OF
ITS CONTENT AND ALSO IN TERMS OF
THE COMPLEXITY OF THE DATA.
AND HAS AN EFFECT ON THE DATA
SCHEMA.
WHEN IT WAS EXTRACTED FROM THE
LITERATURE, STRUCTURE DATA AND
MOLECULES ALONG WITH THE BIO
ACTIVITY DATA AND ASSAY DETAILS.
YOU CAN SEE HERE IN THE MIDDLE
OF THE SLIDE IT WAS A TINY
DATABASE SCHEMA AND WE EXPOSE
TABLE TO THE PUBLIC.
THAT OF COURSE MEANS WE NEED TO
CURATE THE DATA AND THE PROCESS
TAKES A BIT LONGER.
THIS IS MORE TEDIOUS BUT HOPE TO
KEEP THE GOOD QUALITY THAT
CHEMBL IS KNOWN FOR.
AND WHAT YOU CAN ALSO SEE ON THE
SLIDE IS HOW THE DATA CONTENT
HAS BASICALLY CHANGED.
ON THE GRAPH IN THE BOTTOM LEFT
YOU CAN SEE HOW THE SHARE OF BIO
ACTIVITIES FROM THE DIFFERENT
MAIN SOURCES IN CHEMBL VERSUS
THE NATIVE DATA SETS HAS CHANGED
OVER TIME.
AND MOST IS FROM THE ADDITIONAL
CHEMISTRY LITERATURE BUT DONATED
BY DATA DONATION FROM DEDICATED
PROJECTS AND SO ON.
OF COURSE THIS CHANGES THE WAY
WE NEED TO THINK AND ADJUST THE
DATA DEPOSITION PROCESS AND THE
DATA IS NOW MORE DIVERSE AND
MORE COMPLEX WHICH INFLUENCES
OUR CURATION STRATEGY.
ON THE LEFT-HAND SIDE YOU CAN
SEE ALL THE DIFFERENT SOURCES SO
THE MAIN HIGH LEVEL SOURCES OF
INFORMATION THE DATA HAS BEEN
DEPOSITED INTO CHEMBL AND
LITERATURE AND DEPOSITED DATA
SETS.
THERE'S BIO ACTIVITY DATA
EXPECTED AND WE USED OUR
DATABASE FOR THAT BUT THIS IS
RATHER SMALL COMPARED TO THE
WHOLE SIZE AROUND 186,000
COMPOUNDS.
THE WAY WE IDENTIFIED THE PAPERS
WE WANTED TO INTEGRATE DATA FROM
WITH THE BIO ACTIVITY WAS
IDENTIFIED WITH ILLUMINATING THE
DRUGGABLE GENOME PROJECT.
IT'S A FOCUS ON UNDER STUDIED
TARGETS.
AND SOME OF THE DATA HAS MADE IT
INTO CHEL BALL.
WE HAD TWICE A DEPOSITION FROM
PUB CHEM.
THIS ISN'T SOMETHING WE'RE DOING
REGULARLY ON A REGULAR BASIS.
AND CURATE DRUG AND CANDIDATE
INFORMATION IN CHEMBL.
THIS IS NOT STORED WITH THE BIO
ACTIVITY DATA BUT EXTRACTED FROM
A VARIETY OF SOURCES THAT I'LL
EXPLAIN IN A MINUTE.
AND WHAT WE CAN SAY IS THE BIO
ACTIVITY DATA IN CHEMBL COVERS
ALL THE DIFFERENT STAGES OF THE
DRUG DISCOVERY AND DEVELOPMENT
PROCESS.
FROM TARGET DISCOVERY TO LEAD
DISCOVERY AND OPTIMIZATION AND
PRECLINICAL DEVELOPMENT AND THE
CLINICAL PHASES.
IN TERMS OF QUANTITY, MOST THE
DATA IS COVERING THE PRECLINICAL
STAGE.
AND I WANTED TO MAKE A POINT
THIS IS A TEDIOUS PROCESS SO WE
TAKE DATA FROM A VARIETY OF
SOURCES AND THE CLINICAL TRIALS
USING APPLICATIONS FOR CLINICAL
COMBOUNDZ AND THEN WE SUPPLEMENT
THOSE WITH ADDITIONAL SOURCES
LIKE DAILY MED FOR INDICATIONS.
AND DIFFERENT ANNOTATIONS.
ON ONE HAND WE HAVE TO OFTEN
NAME THE CHEMICAL TO A STRUCTURE
IN CHEMBL IN THE DATABASE.
OFTEN THOSE CHEMICAL STRUCTURES
ARE NANNOTATED AND INDICATE THE
DISEASE THE DRUG WAS INTENDED
FOR AND WE ALSO PROVIDE
INFORMATION.
AND WHAT IS NEW SINCE THE LAST
RELEASE OF CHEMBL 34 WE INCLUDED
DRUG FROM THE UNITED MEDICINE
AGENCY AND FIRST THE DATA IS
COLLECTED UP A SEPARATE DATABASE
INSTANCE WE INTERNALLY CALL DRUG
BASE AND MAKES ITS WAY TO CHEMBL
ONCE A YEAR APPROXIMATELY AND
FROM CHEMBL WE BASICALLY DONATE
THE DATA TO OPEN TARGETS.
AND IT'S A PLATFORM.
SO, WE WANTED TO MOVE FORWARD IN
THE PAST TWO YEARS I JOINED THE
CHEMBL TO FLAG.
FIRST WE UPDATED OUR PRODUCT
LIKENESS CAUSE OR HAVE AN
ALGORITHM TO REFLECT PROJECTS
AND IT WAS A FRAGMENT-BASED
APPROACH AND NOW UPDATED THIS TO
THE ALGORITHM PUBLISHED SOME
YEARS AGO AND TOOK AN
IMPLEMENTATION AND WE ALSO
INTRODUCED A NATIONAL PRODUCTS
FLAG.
NOW WE HAVE FLAGGING ALL
MOLECULES THAT WOULD NET TO A
MOLECULE IN THE DATABASE AS A
NATURAL PRODUCT IN CHEMBL.
WHAT I THEN DID WITH THE PRODUCT
LIKENESS I WANTED TO ANALYZE AND
THERE WAS DRUGS AND SYNTHETIC
COMPOUNDS AND THIS IS BASICALLY
WHAT CHEMBL REFLECTS AND LOOKING
ACROSS THE CORE AND JOURNALS IN
THE RIGHT BOTTOM CORNER YOU SEE
IT'S APPROXIMATELY THE SAME IN
DIFFERENT JOURNALS BUT JOURNAL
OF NATURAL PRODUCTS ON AVERAGE
THE MOLECULES PUBLISHED IN THIS
JOURNAL HAVE A HIGHER LIKENESS
CORE WHICH ISN'T SURPRISING
EITHER.
AND I JUST COLLECTED ALL THE
DRUGS REPORTED IN CHEMBL WITH
THE FIRST APPROVAL DATE AND
PLOTTED THE LIKENESS CORE AND
WHAT YOU CAN SEE IS THERE IS A
SLIGHT DECREASE IN NATURAL
PRODUCT LIKENESS SINCE LIKE 20
OR 10 YEARS.
IT'S LIKELY GOING DOWN.
THIS LIKENESS AND WE STARTED A
WE'RE WORKING AT THE INSTITUTE
AND HE'S AN ORGANIC CHEMIST
INTERESTED IN NATURAL PRODUCTS
AND PUBLISHED ON PSEUDO NATURAL
PRODUCTS.
WHAT THIS IS IS BASICALLY
NATURAL PRODUCT FRAGMENTS
COMBINED IN NEW WAYS THAT CANNON
SYNTHESIZ
SYNTHESIZED AND AWE ANALYZED
NATURAL PRODUCT LIKENESS AND
PSEUDO NATURAL PRODUCT LIKENESS
IN THE DATABASE.
WHAT WE DID IS WE COMPARED
CLINICAL COMPOUNDS VERSUS
BACKGROUND COMPOUNDS.
ALL THE OTHER COMPOUNDS MARK AS
CLINICAL COMPOUNDS SO CLINICAL
CANDIDATES OF DRUGS IN CHEMBL
BECAUSE WE WANTED TO SEE DO WE
SEE AN ENRICHMENT OF PRODUCTS
OVER TIME.
IS THIS AN INCREASING POPULAR
CONCEPT.
SO DO WE SEE A NATURAL SELECTION
IN SMALL MOLECULE DRUG
DISCOVERY.
WE SAW THIS ENRICHMENT.
IF YOU WANT IT KNOW MORE ABOUT
THIS WE RECENTLY PUBLISHED THIS
SO PLEASE GO READ OUR ARTICLE.
WE'RE HAPPY TO ANSWER QUESTIONS
ABOUT THIS.
BUT IN THE FRAMEWORK OF THE
COLLABORATION WE PUBLISHED A
RELIABLE DATA SET FOR COMMUNITY
USE.
BECAUSE WE WORK IN THIS
ENVIRONMENT WHERE WE MAKE
EVERYTHING OPENLY AVAILABLE
WE'RE INTERESTED TO MAKE OUR
SOFTWARE AVAILABLE AND ALSO TO
CREATE THOSE DATA SETS FOR
COMMUNITY USE AND THE DATA SET
WE USED FOR THIS ANALYSIS IS A
DATA SET OF COMPOUND TARGETS
THAT HELPS EXPLORE DIFFERENCES
BETWEEN CLINICAL CANDIDATES AND
OTHER COMPOUNDS.
THE PAPER CAME OUT I THINK TWO
DAYS AGO AND IT DESCRIBES HOW
THE DATA SET HAS BEEN
ESTABLISHED AND WE ALSO PROVIDE
THE CODE ON GITHUB AND DIFFERENT
DATA RELEASES AND MAKE IT
AVAILABLE AND HOPING IT'S USEFUL
TO THE COMMUNITY.
FROM NATURAL PRODUCTS TO
CHEMICAL PROHIBITS I THINK IS AN
EMERGING TOPIC AND THEY'RE
HIGHLY SELECTIVE TOOL MOLECULES
THAT HELP YOU UNDERSTAND THE
BIOLOGY OF THE TARGET.
WE'RE STILL PART OF PROJECT
CALLED UB OPEN NOW COME TO ITS
END NEXT YEAR AND WHERE THE
EARTH RAM WAS ON ONE HAND TO
CREATOR SYNTHESIZE NEW HIGH
QUALITY OPEN ACCESS CHEMICAL
PROBES AND THEY SHOULD TARGET
NOVEL TARGET FAMILIES LIKE
CARRIERS OR LIGASES AND ALSO
ESTABLISH A CHEMO GENOMICS
LIBRARY.
AND CHEMBL WAS CHOSEN AS THE
DATABASE FOR THE DATA AND WE
DID.
THEN IN ADDITION, WE WORKED ON
CREATING A SPECIALIZED BESPOKE
RESOURCE FOR THE OPEN GATEWAY
AND SO THE WAY THE DATA IS
FLOWING FROM AN INTERNAL
DATABASE CALLED SCARAB AND
SEARCHS TO THE GATEWAY.
AND A PART FROM THIS WE STARTED
FLAGGING CHEMICAL PROBES BY
ANNOTATING PROBES FROM CHEMICAL
PROBES DOT-ORG AND ALSO HIGH
QUALITY PROBES.
THE REASON I MENTION THIS
PROJECT IN PARTICULAR IS BECAUSE
THIS PROJECT IS OR WAS AND IS
QUITE CHALLENGING BECAUSE OF THE
COMPLEXITY OF THE DATA THAT THEY
WANT THE US TO HOST IN CHEMBL.
IF YOU THINK OF CELL
AVAILABILITY DATA AND AT
DIFFERENT CONCENTRATIONS,
MULTIPLE TIME POINTS, THIS IS
QUITE COMPLICATED TO STORE IN A
RELATIONAL DATABASE LIKE CHEMBL
AND MAY HAVE KEPT VISUALIZATIONS
AND MAY BE STORED IN CHEMBL.
OUR SOLUTION WAS TO HOST THE KEY
DATA IN CHEMBL AND THEN HAVE
SOME ON THE GATEWAY YOU CAN SEE
ON THE RIGHT-HAND SIDE AND FOR
OTHER DATABASE LIKE IMAGES, WE
LINK OUT TO OTHER DATABASES LIKE
THE BIO IMAGE ARCHIVE WHICH IS
ALSO EBI DATA RESOURCE.
WE HAVE THE OPPORTUNITY ALREADY
A FEWER YEARS AGO WITH CHEMBL
24, THE INFRASTRUCTURE SO
DEVELOPMENTS HAVE BEEN MADE.
THERE HAS BEEN AN ACTIVITY
PROPERTY STABLE FOR INSTANCE
INCLUDED AND THESE ARE METADATA
TABLES THAT ALLOW YOU TO STORE
ADDITIONAL INFORMATION SO THAT
YOU DON'T HAVE TO SPLIT THAT IN
MULTIPLE ASSAYS BUT CAN GROUP
THE COMPLEX DATA ALL TOGETHER.
THIS LEADS ME TO TALK ABOUT DATA
CURATION.
AS WE STORE THIS WE NEED TO
CURATE.
THE MORE DATA WE GET THE MORE
CURATION WE HAVE TO DO.
THIS IS A SLIDE I WOULD USUALLY
SHOW AT THIS BEGINNING OF SUCH A
TALK BUT I THINK IT'S NICE TO
SHOW NOW BECAUSE IT SHOWS THE
DIFFERENT ASPECTS OF DATA
CURATION IN CHEMBL.
WE STANDARDIZE CHEMICAL
COMPOUNDS.
THIS IS ALL PUBLISHED AND YOU
CAN FIND THE WORK FLOW WE'RE
USING IN A PAPER AND GENERAL OF
CHEM INFORMATICS AND THE
STRUCTURE PIPELINE IS OPEN ON
GITHUB.
WE ALSO CURATE AND STANDARDIZE
THE BIO ACTIVITY DATA.
WE DO SOME FLAGGING AND
CALCULATE THE P CHEMBL VALUE.
BUT MOST THE TIME WE SPEND ON
CURATING ASSAY INFORMATION AND
THAT'S MOST RELEVANT TO THE
WORKSHOP.
WHAT'S IT MEAN?
ON ONE HAND YOU NEED TO ASSIGN
YOUR TARGET AND IT'S TEMPTING TO
THINK THE TARGET IN AN ASSAY IS
ALWAYS A SINGLE PROTEIN WHICH
CERTAINLY ISN'T TRUE.
IN MOST CASES IT'S NOT A SINGLE
PROTEIN.
OFTEN, IT'S EITHER A PROTEIN
COMPLEX OR PROTEIN FAMILY OR
EVEN SOMETHING NOT A PROTEIN
LIKE A CELL LINE OR TISSUE OR
WHOLE ORGANISM.
WE NEED TO ASSIGN THE TARGET AND
THEN LINK TO ALL KINDS OF
ONTOLOGIES AND WHAT USES UP MOST
OF THE TIME WE SPEND DURING A
RELEASE OR CURATION CYCLE.
AND AND WE'RE AWARE OF THE
ARROWS WHEN ONE USES OUR DATA OR
INTRODUCED BEFORE HAND.
ON ONE HAND IF YOU LOOK AT THE
FIGURE IN THE LEFT CORNER AT THE
BOTTOM HERE, THERE CAN BE
MISTAKES IN JOURNAL ARTICLES.
WE CAN'T DO ANYTHING ABOUT THIS.
THEN DURING OUR IN-HOUSE
CURATION LOOK AT MECHANISMS.
SO WE CAN'T REALLY INFLUENCE
WHAT IS HAPPENING BEFORE THE
DATA REACHES US BUT WE CAN
IMPROVE OUR DATA CHECKS AND
CLEAN UP OUR DATA.
THERE'S BEEN A RECENT ARTICLE BY
GREG LANDROM THAT DISCUSSES THIS
AND IT WAS HELPFUL AND WE'RE
WORKING ON RAISES AWARENESS OF
THE STRUCTURE AND ANNOTATIONS
AND ALSO WANT TO AT SOME POINT
RELEASE OUR DATA CHECK NOTEBOOK
TO THE COMMUNITY AND MAKE MORE
RECOMMENDATIONS.
THE REASON I MENTIONED THIS IS
YOU NEED TO BE AWARE IT DEPENDS
ON THE USE CASE HOW MUCH YOU
WANT TO REFINE THE DATA SET AND
WHAT YOU PUT OUT WHEN YOU USE
THE DATA FOR MODELLING PURPOSE.
SO HERE'S ONE EXAMPLE.
YOU CAN COLLECT ALL THE DATA OR
YOU CAN FURTHER FIELD FOR THE
HUMAN TARGET.
OR YOU CAN GO EVEN DEEPER AND
JUST CONSIDER WILD TYPE.
THESE ARE ALL QUESTIONS YOU NEED
TO ASK YOURSELF BEFORE USING OUR
DATA.
WE CAN'T TAKE RESPONSIBILITY FOR
HOW YOU ARE USING OUR DATA BUT
WE THINK WE ARE IN A POSITION TO
SHORTLY RELEASE MORE
RECOMMENDATIONS.
I DON'T HAVE TIME TO GO INTO A
LOT OF DETAIL HERE BUT WE DO A
LOT OF DATA CHECKS FOR DIFFERENT
ASPECTS OF THE DATA.
AND JUST TO END THIS TALK, I
WANTED TO MENTION TWO MORE
THINGS.
SO PROTEIN ANNOTATIONS IN CHEMBL
IS MAYBE SOMETHING NOT SO WELL
KNOWN SO WE HAVE BEEN
INTRODUCING VARYING SEQUENCES
TABLE IN CHEMBL 27.
THIS INFORMATION IS AVAILABLE
BUT IT'S NOT INFORMATION YOU
WILL FIND AT THE TARGET LEVEL.
BUT AT THE ASSAY LEVEL AND YOU
NEED TO BE AWARE WHEN YOU
COMBINE DATA FROM DIFFERENT
TARGETS AND HAVE TO CONSIDER THE
PROTEIN VARIANT ANNOTATIONS.
WE LATELY WORK TOGETHER WITH A
LAB TO UPDATE THE DATA VARIED
EXTRACTION PIPELINE.
SO THEY DID IT IN A SLIGHTLY
DIFFERENT WAY AND TOGETHER WE
COULD ENHANCE THESE ANNOTATIONS.
THIS HAS BEEN PUBLISHED IN THE
ARCHIVE SO HAVE A LOOK AT THE
WORK WE DID HERE.
THAT'S MY FINAL SLIDE AND FOR ME
THE MOST IMPORTANT ONE AND IT'S
UNFORTUNATE I CAN'T TALK LONGER
ABOUT THIS THIS IS ALSO
UNPUBLISHED CONTENT AND WE HAVE
THIS AMAZING DATA BUT HOW DO WE
KNOW WHAT WE CAN CAN COMBINE TO
BUILD MACHINE LEARNING MODELS
AND WE'RE TRYING TO IMPROVE
ASSAY AN OHIO -- ANNOTATIONS
AND WE LOOK AT THE END POINT AND
FORMAT AND THE READ OUT LIKE
IC50 K I.
WHAT WE'RE NOT DOING IS FOR
INSTANCE ANNOTATING THE
EXPERIMENTAL METHODS.
WE DEVELOPED MODELS TO NOW
IDENTIFY THE DESCRIPTION AND MAP
IT TO THE ONTOLOGY.
THIS IS WORK IN PROGRESS BUT
HOPING WE CAN RELEASE THIS
INFORMATION NEXT YEAR.
AND WE ALSO IN THE COURSE OF
WRITING A PAPER ABOUT THIS I
THINK MY TIME IS OVER BUT I'M
ALSO COMING TO AN END.
I WANT TO THANK THE WHOLE TEAM.
SO IT'S A LARGE TEAM AROUND 25
PEOPLE THAT ARE MAINTAINING OUR
FULL RESOURCES AND THESE PEOPLE
ARE LISTED HERE.
THE PEOPLE INVOLVED IN THE
PROJECT I'VE PRESENTED.
AND THEN ALSO SOME FORMER
MEMBERS WHO HAVE BEEN PART OF
THESE BIG PROJECTS AND SOME
MEMBERS FROM ANOTHER LAB THAT
WERE WORKING WITH US ON THE
VARIANT PIPELINE.
AND LAST BUT NOT LEAST I WANT TO
SAY THE CAKE IS ONLY HALF EATEN
AND THERE'S A LOT OF THINGS TO
DO AND LOOKING FORWARD TO THE
FUTURE.
>> THANK YOU, BARBARA, FOR A
WONDERFUL PRESENTATION.
I HEAR YOUR THOUGHTS ABOUT THE
IMPORTANCE OF ASSAY ANNOTATION
AND HOPEFULLY WE'LL BRING YOU
BACK TO A WEBINAR AND YOU CAN
TALK MORE ABOUT IT BECAUSE WE'D
MOVE TO HEAR THAT.
WE HAVE MULTIPLE QUESTIONS FOR
YOU SO WE'LL START TAKING
QUESTIONS.
HERE'S ONE.
SO HOW DO YOU ENSURE THAT
CAMPBELL CAPTURES SUFFICIENT
DETAILS AND HOW IS IT ENSURED
TWO IC50KI VALUES ARE REPLICATES
OF THE SAME EXPERIMENT FOR A
MOLECULE.
>> THE FIRST PART, HOW DO WE
MAKE SURE THAT WE HAVE A HOLD IN
THE NECESSARY CONDITIONS.
THE ROUGHLY NEGATIVE ANSWER IS
WE CAN'T.
WE HAVE TO BASICALLY RELY ON
WHAT THE DEPOSITORS WOULD GIVE
US.
WHAT WE DO HAVE IS A MANUAL THAT
DESCRIBES THE KIND OF
INFORMATION WE NEED IF DATA'S
BEEN DEPOSITED TO CHEMBL BUT
WE'RE QUITE FLEXIBLE IN TERMS
HOW IT'S BEING DESCRIBED IN THE
ASSAY DES -- DESCRIPTION AND
OTHERWISE YOU PRESS ANYTHING IN
THE SAME SCHEMA.
TO MAKE SURE THE NECESSARY
CONDITIONS ARE DELIVERED WE NEED
TO USE THE RIGHT BIOLOGY TO ASK
THE RIGHT QUESTIONS.
IT MAY BE SOMETIMES WE'RE
MISSING SOME METADATA.
SO WE CAN'T GUARANTEE THIS SO IN
THAT CASE THE DEPOSITOR TAKES
THE RESPONSIBILITY ON PAPERS AND
WE'RE RELYING ON EXTRACTORS TO
EX TRACT THE INFORMING.
WE NOW HAVE 15 YEARS OF
EXPERIENCE TO WORK WITH US.
THE USER WITH THE SAME FORMAT
AND STRUCTURE SO PRETTY
CONFIDENT THEY WOULD EXTRACT
EVERYTHING WE NEED BUT WE CAN'T
SAY WITH 100%.
AND THE SECOND ONE, I DON'T
REMEMBER NOW.
>> IT WAS HOW IS IT ENSURED TWO
OF IC50KI VALUES INTEREST THE
REPLICATE OF THE SAME MOLECULE?
>> I'M NOT SURE I UNDERSTAND.
USUALLY MOST THE DATA WE DON'T
REPORT REPLICATES AT ALL SO I'M
NOT SURE I UNDERSTAND THE
QUESTION.
USUALLY FOR ONE ASSAY, ONE
COMPOUND TARGET PAIR, GET ONE
MEASUREMENT.
IF PEOPLE WOULD WANT US TO STORE
REPLICATES THAT'S PART OF THE
METADATA WE WOULD STORE.
>> THANK YOU, BARBARA.
>> PLEASE WRITE IN THE CHAT AND
I CAN ANSWER LATER IF I
MISUNDERSTOOD THE QUESTION.
>> FANTASTIC.
ANOTHER QUESTION HERE IS IN
CHEMBL, DIFFERENT VARIANTS OF
THE SAME PROTEIN ARE ASSIGN THE
SAME TARGET I.D.
WHAT MEASURES ARE BEING TAKEN TO
ADDRESS THIS ISSUE?
WE THINK THE INFORMATION WE GIVE
IS NOT TO ADDRESS THE ISSUE OF
IDENTIFYING VARIANTS.
WE COULD INTRODUCE A NEW TARGET
IDENTIFIER FOR EVERY VARIANT.
THE QUESTION IS, IS IT
NECESSARY?
THE WAY WE STORE IT IS MAYBE NOT
SO CONVENIENT FOR EVERYONE BUT
THE INFORMATION IS THERE SO
WE'RE IN DISCUSSION TO CHANGE IT
OR NOT.
IT WOULD REQUIRE TO GO OVER
LEGACY DATA LIKE 14,000 TARGETS
TO ADDRESS THIS.
IT'S MORE OF AN ISSUE OF HAVING
THE FUNDS TO DO THAT.
WE MIGHT DO IT OR NOT BUT JUST
BE ASSURED THE INFORMATION IS
THERE.
IT'S JUST STORED AT THE ASSAY
LEVEL.
>> THANK YOU SO MUCH, BARBARA.
THERE'S SO MANY MORE QUESTIONS
SO IF YOU COULD JUST TYPE SOME
ANSWERS IT WILL BE FANTASTIC AND
WE'LL MOVE ON TO THE NEXT
SPEAKER.
WE APPRECIATE YOUR TIME.
THANK YOU SO MUCH.
ALL RIGHT.
LET'S ME OF ON TO THE NEXT
SPEAKER WHO IS DR. STEVEN
BURLEY.
HE'S AN EXPERT IN DATA SCIENCE
AND BIOINFORMATICS AND
STRUCTURED BIOLOGY AND
STRUCTURED DRUG DISCOVERY FOR
ONCOLOGY AND DIRECTOR OF THE
DATABASE BANK AND ALSO WITHIN
RUTGERS THE STATE UNIVERSITY OF
NEW JERSEY SERVES AS UNIVERSITY
PROFESSOR AND HENRY RUTGER CHAIR
AND THE CANCER RESEARCH PROGRAM
CO-LEADER WITHIN THE INSTITUTE
OF NEW JERSEY.
HE HAS PUBLISHED EXTENSIVELY IN
DATA SCIENCE AND BIOINFORMATICS
AND ARTIFICIAL INTELLIGENCE,
MACHINE LEARNING AND CLINICAL
ONCOL
ONCOLOGY.
HE'LL TALK ABOUT PROTEIN DATA
BANK FROM THE GLOBAL PANDEMIC TO
mRNA VACCINES AN PAXLOVID.
THE FLOOR IS YOURS.
>> YOU SHOULD NOW SEE MY SLIDE.
VERY GRATEFUL FOR YOUR KIND
INTRODUCTION AND THE HONOR OF
PRESENTING IN THE HONOR OF
PRESENTING IN THIS IN SILICO
DRUG DISCOVERY WORKSHOP.
TODAY I'LL PRESENT REAL WORLD
EXAMPLES OF THE IMPORTANCE OF
DATA PRESERVATION AND FAIR
SHARING.
IN THIS TALK ENTITLED PROTEIN
DATA BANK FROM TWO EPIDEMICS TO
THE PANDEMIC AND mRNA VACCINES
AND PAXLOVID.
MY PLAN IS TO TELL YOU ABOUT THE
CRITICAL ROLE THAT STRUCTURAL
BIOLOGISTS AND THE PDB TOGETHER
PLAYED IN FIGHTING THE COVID-19
PANDEMIC.
THE PUNCH LINE WAS PUT FORWARD
FROM DR. FAUCI HE CITED THE
IMPORTANCE OF 3-D VIRUS
STRUCTURE INFORMING AND SAID
SHOW ME A PERSON WHO WAS
VACCINATED, GOT COVID AND TOOK
PAX PAXLOVID AND DIED AND I'LL
COVER THE DATA BANK PARTNERSHIP
AND THE RCSP DATA BANK AND MY
ORGANIZATION THE U.S. DATA
CENTER FOR THE WWPDB.
OUR ONE-STOP SHOP FOR VIRUS
STRUCTURE DATA AND THEN SPEND
THE BULK OF MY TIME TALKING
ABOUT HOW STRUCTURAL BIOLOGISTS
AND THE PDB PLAY ROLES IN TAMING
OF THE COVID-19 PANDEMIC.
AND CLOSE WITH A SOBERING
PRESCRIPT.
THE PDB HAS BEEN CONTINUOUSLY
FUNDED AND FOUR OF THE EARLY
STRUCTURES ARE ILLUSTRATED IN
THE RIGHT PANEL.
IN BIOLOGY, FUNCTION FOLLOWS.
THIS VARIATION ON THE FAMOUS
PHRASE EXPLAINS SUCCINCTLY THE
FUNCTION OF A MACRO MOLECULE IS
DETERMINED BY ITS SHAPE AND 3-D
STRUCTURE.
SINCE ITS INCEPTION THE ARCHIVE
HAS BECOME THE SINGLE GLOBAL
RESOURCE FOR ATOMIC LEVEL 3-D
VIRUS STRUCTURE INFORMATION.
AT PRESENT THE PDB HOUSES MORE
THAN 225,000 STRUCTURES OF
PROTEINS, NUCLEIC ACIDS AND
VIRUSES AND MACRO MOLECULAR
MACHINES.
RECOGNIZING THE GLOBAL
IMPORTANCE OF THE PDB IT'S BEEN
MANAGED JOINTLY SINCE 2003 BY
THE WORLDWIDE PROTEIN DATABASE
PARTNERSHIP AND DATA CENTERS IN
THE U.S., EUROPE, JAPAN AND THE
PEOPLE'S REPUBLIC OF CHINA AND
TWO REPOSITORIES FOR MICROSCOPY
AND THE DATA BANK SERVES AS A
DATA CENTER FOR THE PARTNERSHIP
AND AS THE DESIGNATED WW PDB
KEEPER FOR THE DATA BANK AND
HEADQUARTERED AT RUTGERS IN NEW
JERSEY WITH THREE ADDITIONAL
PERFORMANCE SITES AT U.C. SAN
DIEGO AND SAN FRANCISCO.
WE OPERATE FIVE INTERLOCKING
SERVICES THAT CONVERT GLOBAL
DATA INTO GLOBAL KNOWLEDGE FOR
THE PUBLIC GOOD.
ONCE 3-D VIRUS STRUCTURE DATA
ARE ARCHIVED IN THE PDB, OUR
RESEARCH FOCUS WEB PORTAL SERVES
AS A RESOURCE FOR EXPERIMENTALLY
DETERMINED PDB STRUCTURES
ALONGSIDE 1 MILLION MODELS.
AND PUBLIC DOMAIN INFORMATION TO
MANY RESEARCHERS AND THEIR
TRAINEES AND EDUCATORS AND THEIR
STUDENTS AROUND THE WORLD AT NO
COST WITH NO LIMITATIONS ON
USAGE.
FOR THE AVOIDANCE THE PROMINENCE
AND RELIABILITY OF BOTH TYPES OF
3-D VIRUS STRUCTURE DATA
EXPERIMENTALLY DETERMINE AND
COMPUTATIONALLY PREDICT THE
CLEARLY IDENTIFIED ON THE
WEBSITE.
PDB DATA ARE ESSENTIAL FOR
RESPONDING TO EMERGING VIRUSES.
SINCE THE EARLY 2000s THE WORLD
HAS FACED DOWN THREE MAJOR
OUTBREAKS OF CORONAVIRUS
OUTBREAKS THAT JUMP THE SPECIES
TO HUMAN AND THEY KILLED JUST
OVER 800 PEOPLE BETWEEN 2002 AND
2004.
HERE AFTER I'LL REFER TO SARS
COV TO SARS COV1 TO AVOID
CONFUSION AND TO DATE IT'S
KILLED MORE THAN 900 INDIVIDUALS
AND REMAINS A PUBLIC HEALTH
THREAT IN THE MIDDLE EAST AND
ASIA.
DESPITE THE TWO WARNING SIGNS
FEW COUNTRIES PREPARED FOR THE
POSSIBILITY OF A MUCH MORE
CORONAVIRUS EPIDEMIC.
FORTUNATELY FOR THE GLOBAL
COMMUNITY, STRUCTURAL BIOLOGISTS
AND THE PDB LAID THE GROUND WORK
FOR A SUCCESSFUL RESPONSE TO THE
INEVITABLE THIRD CORONAVIRUS
WAVE.
THE EFFECTIVE VACCINES WERE
DESIGN AND ANTIVIRAL AGENTS WERE
DEVELOPED WITH THE BENEFIT OF
OPEN ACCESS TO PDB STRUCTURES OF
CARS COV2 AND MERS AND PROTEINS.
SARS COV2 THE UNSEEN ENEMY IS
SHOWN ON THE RIGHT WITHIN A
RESPIRATORY DROP LET IN A
HAUNTING PAINTING BY THE AUTHOR
OF THE RCSV PDB WHO AUTHORED THE
SERIES MORE THAN 20 YEARS.
THE ORGANIZATION OF THE GENOME
IS DEPICTED SCHEMATICALLY.
ALL CORONAVIRUS GENOMES ARE VERY
LONG, SINGLE STRANDED POSITIVE
SENSE POLY A DEDEN DEDENNY --
PROTEINS ARE EXPRESSED WITHIN A
PAIR OF PROTEINS AND THERE'S
EXCISED FROM THE POLY PROTEIN BY
TWO SARS COV2 PROTEASES NON
STRUCTURE PROTEINS THEMSELVES.
THE PROTINNASE HAS ARROWS.
AND MORE IMPORTANT MAIN PROTEASE
CUTS AT 10 SIGHT WITH LIGHT BLUE
INVERTED TRIANGLES.
THOUGH WE DIDN'T KNOW IT AT THE
TIME, PATHOGEN PREPAREDNESS
BEGAN IN THE 2000s WITH WORK ON
SARS COVE 1 AND THE MAIN
PROTEASE OR EMPRO.
AND THEY USED THE ALPHABET.
WITHOUT THE MAIN PROTEASE IT
CANNOT CLEAVE STOPPING AN
INFECTION IN ITS TRACKS.
THIS STRUCTURE WAS DETERMINED IN
2003 WE MY COMPANY FCX
PHARMACEUTICALS.
IT'S THE ONLY PDB STRUCTURE WITH
THE PRIMARY PUBLICATION
APPEARING IN THE NEW YORK TIMES.
MY COMPANY RELEASED THE
STRUCTURE TO THE PUBLIC SO WORK
ON COUNTER MEASURES COULD BEGIN
WITHOUT DELAY.
TODAY 98 CRYSTAL STRUCTURES OF
THE PROTEASE HAVE BEEN
CONTRIBUTED TO THE ARCHIVE.
THE FIRST PDB STRUCTURE OF THE
ENTIRE EXTRA CELLULAR STRUCTURE
OF THE TRIMERIC STRIKE PROTEIN
WAS DETERMINED USING SINGLE
PARTICLE MICROSCOPY.
TO DATE MORE THAN 70 SARS COV1
STRUCTURES HAVE SBN CONTRBEEN CD
AND CONFIRMATIONS AND COMPLEXES
WITH THE CELLULAR RECEPTOR AND
CONVERTING ENZYME 2 AND
VARIATION ANTIBODY FAB
FRAGMENTS.
THE FIRST PDB STRUCTURE OF MERS
PROTEIN WAS THAT OF THE ACE 2
BINDING DOMAIN OF THE SPIKE
PROTEIN SHOWN HERE IN THE RIGHT
PANEL IN A RIBBON
REPRESENTATION.
TO DATE, FOR MERS MORE THAN 70
SPIKE PROTEIN RELATED STRUCTURES
HAVE BEEN CONTRIBUTED TO THE
ARCHIVE ALONGSIDE MORE THAN 40M
PRO STRUCTURES.
I'LL RETURN TO THE SARS AND MERS
PROTEIN STRUCTURES LATER BECAUSE
THEY PLAYED CRITICAL ROLES IN
THE VACCINE DESIGN.
SINCE THEY WERE DEPOSITED IN THE
PDB THE RCSP PROTEIN DATA BANK
HAS BEEN RESPONDING TO THE
PANDEMIC.
WITH OUR PARTNERS WE PRIORITIZED
COMPLETE DEPOSITION, RIGOROUS
VALIDATION AND EXPERT BIO
CURATION OF COVID-19 RELATED
PROTEIN STRUCTURES THAT STRONGLY
ENCOURAGE IMMEDIATE
PREPUBLICATION RELEASE OF THESE
DATA.
WE CREATED A CORONAVIRUS PAGE ON
OUR WEB PORTAL UPDATED WEEKLY
WITH EACH RELEASE CYCLE ADDING
APPROXIMATELY 300 NEW STRUCTURES
OF ANY STRIFE TO THE ARCHIVE
EVERY WEDNESDAY AND DEVELOPED
OUTREACH RESOURCES SUCH AS THE
ANIMATED HAND WASHING VIDEO
VIEWED MORE THAN 500,000 TIMES
AND PDB MOLECULE OF THE MONTH
ARTICLES AUTHORED BY DAVID
GOODSELL FEATURING THE PROTEASES
AND THE POLYMERASE AND SPIKE
PROTEINS.
THE RIGHT PANEL SUMMARIZES OUR
NEARLY COMPLETE ATOM BEING LEVEL
3-D KNOWLEDGE OF SARS COV2
PROTEIN STRUCTURES EMBODIED IN
MORE THAN 4500 PDB STRUCTURES
RELEASED.
THAT REPRESENTS NEARLY 2% OF THE
ENTIRE PDB ARCHIVE.
THE LEFT PANEL SHOWS AN ARTIST
RENDITION OF THE LIPID BILAYER
OF SARS COV2 FUSING WITH THE
PLASMA MEMBRANE OF A CELL IN
ANOTHER HAUNTING PAINTING FROM
DAVID GOODSELL.
THE VIRAL ENTRY PROCESS IS
BLOCKED BY THE IMMUNE RESPONSE
TO mRNA VACCINES THAT COME ON
TOO SOON.
THE VERY FIRST PDB STRUCTURE OF
A SARS COV2 SPIKE PROTEIN ID6VSB
WAS RELEASED IN 2020.
THERE'S ABOUT 1900 SPIKE PROTEIN
RELATED STRUCK STURZ IN THE PDB.
LIKE EARLIER STRUCTURES THEY
PROVIDE IMPORTANT INSIGHTS INTO
RECEPTOR BINDING, FUSION WITH
THE BILAYER AND THE PLASMA MANY
MEMBRANE AND ANTIBODY DESIGN.
THE FIRST mRNA VACCINE DESIGNS
WERE GENERATED IN JANUARY 2020
BASED ON STRUCTURES OF THE COV1
PROTEINS SIMILAR IN AMINO ACID
AND 3-D STRUCTURE TO THEIR SARS
COV2 COUNTERPART.
WHEN DESIGNING MODERNA'S 1273
VACCINE ENCODING THE SPIKE
PROTEIN, RESEARCHERS AND THE FOR
PROFIT COMPANY REPORTED RELIANCE
ON PDB STRUCTURES OF DOUBLE
PROLEAN FORMS OF THE SARS COV1
AND MERS SPIKE PROTEINS.
THE DOUBLE PROLENE MUTATION
STABILIZES BOTH PROTEINS IN A
HIGHLY IMMUNOGENIC PREFUSION
CONFIRMATION.
I BELIEVE THE VACCINE DESIGNERS
AT BIO EN TECH USED THE SAME
BECAUSE THEY'RE INAUGURAL
VACCINE ALSO ENCODED A SARS COV2
PROTEIN BEARING THE IDENTICAL
PROLENE CHANGE.
THE REST OF THE STORY IS WELL
KNOWN TO YOU.
TWO ESSENTIALLY IDENTICAL mRNA
VACCINES AGAINST COVID-19 WERE
BROUGHT TO THE MARKET IN RECORD
TIME.
TO DATE, THESE VACCINES AND
THEIR SUCCESSORS HAVE BEEN
ADMINISTERS TO MORE THAN 5.5
MILLION INDIVIDUALS WITH
IMPRESSIVE RESULTS.
THE VACCINE DESIGN WITH THE
BENEFIT OF OPEN ACCESS TO PDB
STUK TOU STRUCTURES CREDITED WI
SAVING MILLIONS OF LIVES AND YOU
HAVE TO LOVE THE PROTEIN DATA
BANK.
I'LL NOW TURN FROM THE SPIKE
PROTEIN AND VACCINE DESIGN TO
DISCOVERY OF SMALL MOLECULE
ANTISARS DRUGS.
DRUG PRE PURPOSING PROVED TO BE
IMPORTANT FOR THE POLYMERASE
SHOWN ID6YYT.
THE HOLLOW ENZYME CONSISTS OF
THE SMALLER NSP7 AND LARGER 12
AND LARGER POLYMERASE SUB UNIT
AND THE MOLECULAR CHOP STICKS
HOLDING THE DUPLEX.
GILEAD DEVELOPED A TREATMENT FOR
HEPATITIS C AND RECEIVED
EMERGENCY USE AUTHORIZATION FOR
SARS COV2 FROM US FDA AND
ANOTHER WAS INCORPORATED IN THE
PRODUCT AND THIS EXPLAINS THE
MECHANISM OF THE ACTION AT THE
ATOMIC LEVEL OF THE REPURPOSED
DRUG IN 3-D AND THIS ONE
DEVELOPED FOR THE EQUINE
ENCEPHALITIS VIRUS NOW A PUBLIC
HEALTH THREAT IN THE UNITED
STATES.
IT RECEIVED EIGHT EMERGENCY USE
AUTHORIZATION FOR SARS COV2 FROM
THE U.S. FDA IN 2021.
THE RIGHT PANEL SHOWS PDB OZU
INCORPORATED INTO THIS RNA
PRODUCT OF THE HOLLOW ENZYME.
LIKE REMDESIVIR THIS SHOWS THE
MECHANISM OF ACTION OF THE
REPURPOSED DRUG AT THE ATOMIC
LEVEL IN 3-D.
MOVING ON I'LL NOW DESCRIBE
STRUCTURE GUIDED DRUG DISCOVERY
FOR SARS COV2 MAIN PROTEASE THE
ACHILLES HEEL OF THE VIRUS.
IT'S THE TARGET OF PFIZER'S DRUG
PAXLOVID A FIXED DOSE.
IT HOUSES MORE THAN 1500 CRYSTAL
STRUCTURES OF SARS COV2 EMPRO.
THE FIRST OF ID UNIFORM 7
CONTRIBUTED IN JANUARY 2020 BY
COLLEAGUES BASED IN SHANGHAI
APPROXIMATELY TWO WEEKS AFTER
THE GENOME BECAME PUBLICALLY
AVAILABLE THE ORIGIN STORY
BEGINS IN THE EARLY 2000s WITH
DEVELOPMENT OF PF-008321 SHOWN
IN THE RIGHT PANEL INHIBITING
SARS COV1 DRUG.
THIS WAS INTENDED AS AN
INJECTABLE ANTIVIRAL AGENT FOR
ACUTELY ILL SARS COV1 PATIENTS
AND THIS INITIATED IN 2003
FACILITATED BY THIS AND
REGRETTABLY BUT UNDERSTANDABLY
PFIZER ALTERED THE PROJECT
BECAUSE SARS COV1 AND THE
COMPANY JUDGED THERE'D BE NO
COMMERCIAL MARKET FOR THE DRUG.
AS THE PANDEMIC HIT, PFIZER
QUICKLY REACT VATED THE SARS
COV1 AGENT FOR OUT PATIENT USE
FACILITATED BY OPEN ACCESS TO,
THE FIRST STRUCTURE I DESCRIBED
EARLIER.
THE RESULT WAS NIRMATRELVIR.
YOU HAVE TO LOVE THE PROTEIN
DATA BANK.
AND WHAT WAS SHOWN ON THE
PREVIOUS SIDE COVALENTLY ACT
BEING INHIBITORS OF THE PROTEASE
BINDING TO THE CYSTEINE RESIDUE
AND IT CATALYZES A SINGLE TURN
OVER REACTION FORM THEY CAN
ENACTIVE COVALENT ADAPT IN THE
RIGHT PANEL PREVENTING PROCESS
OF THE VIRAL POLY PROTEIN AND
STOPPING THE INFECTION IN ITS
TRACK.
IT'S STRUCTURALLY SIMILAR TO THE
FIRST GENERATION SARS COV1 EMPRO
INHIBITOR AND TO BE ADMINISTERED
AS A PILL TO NEWLY INFECTED
INDIVIDUALS.
NIRMATRELVIR IS RAPIDLY
METABOLIZED AND A POTENT
INHIBITOR PROLONGS THE HALF LIFE
BY PREVENTING DEGRADATION AS IT
PASSES THROUGH THE LIVER.
PAXLOVID, A FIXED DOSE
COMBINATION OF THE TWO DRUGS
RECEIVED EMERGENCY USE
AUTHORIZATION FROM U.S. FDA IN
2021.
LESS THAN TWO YEARS AFTER PUBLIC
RELEASE OF THE PUBLIC GENOME
SEQUENCE.
THE SPEED WITH WHICH PFIZER WAS
ABLE TO MOVE WAS UNPRECEDENTED.
AT THE END OF MY TALK I'LL COME
BACK THAT PFIZER REPORTED IT'S
ALSO ACTIVE AGAINST THE MAIN
PROTEASE OF BOTH SARS COV1 AND
MERS COV.
THIS LEADS ME TO A SOBERING
PRESCRIPT.
THE ARCHIVE HOUSES THE P450
ISOSOME.
THE RIGHT PANEL SHOWED THE
INHIBI
INHIBITOR.
IT WAS DISCOVERED BY ABBOTT AS A
PROTEASE IN HINTER AND EXPLAINS
WHY IT'S ALSO USED FOR
PROLONGING THE HALF LIVES OF
OTHER PROTEASE INHIBITORS
SUSCEPTIBLE TO DEGRADATION.
THE FACT THAT THIS INHIBITS THE
MAIN PROTEASES OF SARS COV2 AND
COV1 IS NOT SURPRISING.
IN 2021, FOUR UNDERGRADUATES
WORKING AT RUTGERS WITH ME IN
OUR GOAL WAS TO UNDERSTAND EMPRO
AMINO ACID SEQUENCE AND 3-D
STRUCTURE AND ACTIVE SIDE AND
THE DIVERSITY ACROSS ALL KNOWN
CORONAVIRUSES.
THIS WORK HAS NOT BEEN PUBLISHED
SO I'LL SUMMARIZE OUR RESULTS IN
DETAIL.
ALL CORONAVIRUS MAIN PROTEASES
FALL INTO FOUR WITHIN THE
CORONAVIRUS GENUS.
TAKE A MOMENT TO NOTE HOW CLOSE
SARS COV1 AND COV2 ARE TO ONE
ANOTHER.
THIS IMAGE SHOWN IN THE RIGHT
PANEL EXPLAINS HOW THE EMPRO
ACTIVE SITE CAN BE HAVE
DESIGNATED SITES.
THE INVARIANT ACTIVE SITE
CYSTEINE IN THE RED CIRCLE IS
LOCATED BETWEEN THE S1 PRIME AND
S1.
LIKE FULL LENGTH SEQUENCES, ALL
CORONAVIRUS MAIN PROTEASE ACTIVE
SITES FALL INTO ONE OF FOUR
DIFFERENT GENERA BASED ON
CLUSTERING OF THE RESIDUES THAT
LINE THE SITE.
YOU CAN SEE FROM THE DETAIL OF
THE CLATOGRAM THEY'RE MORE
SIMILAR TO ONE ANOTHER THAN THE
FULL LENGTH AMINO ACID
SEQUENCES.
CONCENTRATION OF THE RESIDUES
LINING THE PLAIN PROTEASE ACTIVE
SITES OF ALL KNOWN CORONAVIRUSES
IS NOTHING SHORT OF REMARKABLE.
EACH COLUMN IN THE RIGHT PANEL
SHOWS THE FREQUENCY OF EACH OF
THE 20 POSSIBLE AMINO ACIDS
LINING THE ACTIVE SITE.
DARK BLUE IDENTIFIES AMINO ACIDS
THAT DO NOT OCCUR IN THAT
POSITION AND THERE'S A LOT OF
DARK BLUE REFLECTING EMPROACTIVE
SITES ARE CONSERVED ACROSS ALL
CORONAVIRUSES.
FOR EXAMPLE, SARS COV1 AND COV2
ARE MADE OF THE SAME AMINO ACID
RESIDUES IN THE SAME POSITIONS.
THERE ARE 12 AMINO ACID RESIDUES
INVARIANT ACROSS ALL
CONFERENCES.
BASED ON COMPUTER MODELLING
STRUCTURES HOUSED IN THE PDB YOU
CAN SEE IN THE RIGHT PANEL THE
POLY PROTEIN SUBSTRATES MODELLED
CONSISTENT OF SIX AMINO ACID
RESIDUES P1, P5 AND CATALYZE
CLEAVAGE OCCURS IN THE PRIME IN
EACH OF THE SITES DISTRIBUTED
THROUGH THE LENGTH OF THE POLY
PROTEIN.
THE INVARIANT ACTIVE SITE
CYSTEINE IS VISIBLE WHERE IT
BREAKS THE BOND BETWEEN P1 AND
P1 PRIME.
>> WE HAVE A FEW MINUTES.
>> I'LL BE DONE ALMOST
IMMEDIATELY.
THE CONSEQUENCES ARE ALSO HIGHLY
CONSERVED.
THIS SHOWS THE FREQUENCY OF EACH
OF THE POSSIBLE AMINO ACIDS
APPEARING P1 THROUGH P1 PRIME.
TO JUMP TO THE BOTTOM, THERE'S
DOUBLE EVOLUTIONARY SELECTION
PRESSURE WORK ON BOTH 3-D
STRUCTURES OF CONFERENCE, M
PROACTIVE SIGHT FOUND IN THE
CORONAVIRUS POLY PROTEIN.
DURING VIRAL EVOLUTION, ACTIVITY
OF THE ENZYME HAS TO BE
PRESERVED AND EACH OF THE TEN
AMINO ACIDS CANNOT CHANGE
SIGNIFICANTLY.
WE THERE PREDICT IT WILL BE
EFFECTIVE AGAINST MANY OF THE
KNOWN CORONAVIRUSES NOT JUST
COV1 AND MERS COV AND BEYOND THE
WORK WE'RE TESTING THE IN SILICO
PREDICTIONS AND PROTEASES FROM A
DIVERSE ARRAY OF CORONAVIRUSES.
UNDERSTANDING CONSTRAINTS ON THE
MAIN PROTEASE ACTIVE SITE
STRUCTURES IN 3-D AND THE CUT
SITE AMINO ACID TELLS US THE
FREE MARKET FAILED US.
IT'S DEFINED AS AN INEFFICIENT
DISTRIBUTIONS OF GOODS AND
SERVICES WITHIN THE ECONOMY.
SUCH PHENOMENA OCCUR WHEN
INDIVIDUAL INCENTIVES FOR
RATIONALE ECONOMIC BEHAVIOR DO
NOT LEAD TO A RATIONALE OUTCOME
FOR THE OUTCOME OF SOCIETY.
SIMPLY PUT, WE CANNOT ALWAYS
RELY ON CAPITALISM TO GET THE
RIGHT OUTCOME WHEN IT COMES TO
THE PUBLIC GOOD.
FOR BETTER PANDEMIC
PREPAREDNESS, PFIZER COULD HAVE
BEEN INCENTIVIZED WITH PUBLIC
MONEY IN THE 2000s TO CONTINUE
WORKING ON DISCOVERY OF BROAD
SPECTRUM M PRO INHIBITORS.
WE KNOW INVESTMENT OF A FEW
HUNDRED MILLION U.S. DOLLARS IN
A SARS COV1 INHIBITOR COULD HAVE
SAVED MILLIONS BEFORE THE mRNA
VACCINES BECAME AVAILABLE AND
PREVENTED ECONOMIC LOSSES TO THE
TRILLIONS OF U.S. DOLLARS.
WHEN THE MERS EPIDEMIC STRUCK
THERE SHOULD HAVE BEEN THE
DESIRE ABILITY OF MEASURES
TARGET BEING NEW CORONAVIRUSES.
EXPERTS KNEW IT WAS NO LONGER OF
IF BUT WHEN THE NEXT CORONAVIRUS
EPIDEMIC WOULD STRIKE WITH THE
RISK OF BECOMING A GLOBAL
PANDEMIC.
THANKS TO THE STRUCTURAL BIOLOGY
COMMUNITY AND OPEN ACCESS PDB
EXPERTS KNEW THE ACTIVE SITES
WERE NEARLY IDENTICAL IN 3-D
STRUCTURE AND THEY'RE POLY
PROTEIN CUT SITE AMINO ACID
SEQUENCES WERE SIMILAR.
THE FREE MARKET FAILED BUT AND
THE DATA BANK STORED THE
INFORMATION.
WE HAVE PFIZER MAKING SIZABLE
INVESTMENT IN DEVELOPMENT OF
SARS COV1M PRO INHIBITOR SETTING
THE STAGE FOR RAPID DISCOVERY
AND DEVELOPMENT AND EMERGENCY
USE AUTHORIZATION OF PAXLOVID.
SIMILARLY, THE FEDERAL
GOVERNMENT AND PFIZER INVESTED
IN VACCINE TECHNOLOGY.
ONE OF THE FEW SILVER LINING IS
VACCINE DISCOVERY AND
DEVELOPMENT HAS BEEN
REVOLUTIONIZED.
OVER THE NEXT THREE YEARS WE CAN
SEE HOPE TO PATIENTS AND
FAMILIES IN THE AREA OF CANCER.
I'M ALSO HOPEFUL A MORE
CONCERTED AND EFFECTIVE APPROACH
TO PANDEMIC PREPAREDNESS
WORLDWIDE.
I'VE EXPLAINED HOW OPEN ACCESS
TO RESEARCH DATA GENERATED WITH
PUBLIC AND PRIVATE FUNDS
PARTICULARLY THOUSANDS OF PDB
STRUCTURES ENABLED BASIC AND
APPLIES RESEARCHERS TO COMBAT
COVID-19 PANDEMIC TO SAVE TENS
OF MILLIONS OF LIVES.
JOIN ME AND DR. FAUCI IN LOVING
THE DATA BANK.
THANK YOU FOR YOUR ATTENTION AND
ALL THE PEOPLE DEPICTED IN THE
SLIDE AND OUR WORLDWIDE PROTEIN
DATA BANK COLLEAGUES WHO MADE
THIS POSSIBLE, OUR FUNDERS AND
NSF AND NIH AND WE'RE LISTED
HERE AS ARE OUR HOSTS AT RUTGERS
AND UCSD AND UCSF.
THANK YOU FOR YOUR ATTENTION.
>> THANK YOU FOR A WONDERFUL
TALK.
IF YOU CAN TURN YOUR CAMERA ON
TO TAKE A QUESTION OR TWO.
LET'S TAKE ONE OR TWO QUESTIONS.
WE CAN'T TAKE MORE BECAUSE WE'RE
BLEEDING INTO THIS BREAK.
IS THERE PUBLICATION THAT
DETAILS THE PROTOCOLS OR
PROFESSIONS OF PDB CURATION?
>> THERE IS A JOURNAL BIO
CURATION THAT DOCUMENTS ALL OF
THAT.
WE HAVE ON OUR DATA BANK DOT ORG
WEBSITE A DETAILED EXPLANATION
OF CURATION PROCESSES UNDERTAKEN
WHEN EVERY NEW STRUCTURE COMES
IN THE PROTEIN DATA BANK.
THE PROTOCOLS FOR VALIDATION AND
BIO CURATION ARE GLOBALLY
UNIFORM BETWEEN COLLEAGUES IN
JAPAN AND THE PEOPLES REPUBLIC
OF CHINA, EUROPE AND THE U.S.
>> THANK YOU.
NEXT QUESTION, HOW DO THE
DETERMINE ACTIVE SITES OF THE
PROTEIN WITH NO REFERENCE
STRUCTURES OR LIGANDS.
IS THERE A WEBSITE TO HELP WITH
THIS?
>> A TEAM DID AN ANALYSIS OF
ENZYME ACTIVE SITES MANY YEARS
AGO.
I DON'T RECALL WHERE IT WAS
PUBLISHED AND THE THRUST OF
THEIR RESULTS WAS LIKELY TO BE
THE ENZYME ACTIVE SITE WHEN
DEALING WITH A CATALYTIC ENTITY.
>> FANTASTIC.
THANK YOU.
NEXT QUESTION, WE'LL TAKE ONE
MORE.
RCSB OFFERS 3-D STRUCTURES IN
PDB 5 FORMAT WITH OTHERS.
MANY RESEARCHERS FAVORS THE
FORMAT BUT IT HAS LIMITATIONS.
ARE THERE PLANS TO MODIFY THE
FORMAT OR A NEW ONE TO INCLUDE
MORE DETAILED INFORMATION ABOUT
PROTEINS IN A USER FRIENDLY WAY?
>> EXCELLENT QUESTION.
THAT'S ALREADY HAPPENED.
THE ARCHIVAL FORMAT FOR THE PDB
IS THE PDB DATA STANDARD CLOSELY
RELATED TO THE DERIVATIVE
STANDARD USED BY ALPHA FOLD DB
AND THE MODEL ARCHIVE THE
ADVANTAGE IS IT'S BOTH MACHINE
AND HUMAN READABLE.
IT'S INFINITELY EXTENSIBLE TO
STRUCTURE SIZE AND COMPLEXITY
AND THE ADDITION OF NEW DATA
STANDARDS TO SUPPORT NEW TYPES
OF EXPERIMENT AND THINGS LIKE
POST TRANSLATIONAL
MODIFICATIONS, ETCETERA.
I KNOW THE 80 COLUMN PUNCH CARD
FORMAT OF THE ORIGINAL PDB
FORMAT IS VERY EASY FOR PEOPLE
TO USE BECAUSE THEY'RE FAMILIAR.
BUT I THINK IN THE LONG RUN WE
ALL HAVE TO MOVE TO THE DATA
STANDARD IF WE WANT THE PDB TO
BE USEFUL GOING FORWARD.
MANY STRUCTURES WE'RE NOW
ARCHIVING CANNOT BE DEPICTED IN
THE LEGACY FORMAT.
THOSE STRUCTURED DATA FILES ARE
NO LONGER AVAILABLE IN LEGACY
PDB FORMAT.
THEY'RE ONLY AVAILABLE IN THE
DATA STANDARD.
THOSE CONSUMING PDB DATA GO WITH
THE FLOW AND YOU'LL FIND THE
STANDARD IS EASY TO LEARN, EASY
TO READ AND EASY TO, WOULD WITH
FROM A SOFTWARE ENGINEERING
STANDPOINT.
>> FANTASTIC THANK YOU FOR A
WONDERFUL PRESENTATION AND
TAKING ON THE QUESTIONS.
WE APPRECIATE IT SO MUCH.
ALL RIGHT, EVERYONE.
I'LL SHARE MY SLIDE DECK SO NEXT
WE'LL HAVE A SHORT BREAK AND
WE'LL START SESSION 2 AT 2:00.
10 MINUTE BREAK AND BACK ON IN
SILICO METHODOLOGIES USED IN
DRUG DISCOVERY. SEE YOU IN 01 MINUTES.
>> THANK YOU WELCOME BACK TO
SESSION 2.
SUPER EXCITING SESSION FOR YOU
ON IN SILICO METHODOLOGIES USED
IN DRUG DISCOVERY IT'S
CO-CHAIRED BY ALEXANDRE VARNEK
AT THE SCHOOL -- ALEXANDER
TROPSHA FROM THE UNIVERSITY OF
NORTH CAROLINA CHAPEL HILL AND
IS AN ELECTED FELLOW OF THE
AMERICAN INSTITUTE OF
ENGINEERING AND A CO-FOUNDER OF
PREDICTIVE LLC A UNC START-UP
SPECIALIZING IN BUILDING MODELS
AND TOOLS FOR PREDICTION AND
HELPED PUT THE WORK SHOP
TOGETHER.
THANK YOU, THE FLOOR IS YOURS.
>> I'LL BE BRIEF.
I WANT TO START BY THANKING
SPEAKING FOR HIGHLIGHTING THE
IMPORTANCE OF DATA AND DATABASES
THAT CAN BE USED FOR
APPLICATIONS.
IN THIS SESSION, WE'LL HEAR FROM
FOUR EXPERTS IN ANALYTICS, USING
AVAILABLE AND SYNTHETICALLY
FEASIBLE METHODS OF MACHINE
LEARNING AND FEASIBLE MODELLING.
THE FIRST SPEAKER IS ALEXANDER
VARNEK OF CHEMISTRY AND THE
DIRECTOR OF THE SCIENCE AT THE
UNIVERSITY OF STRASBOURG, FRANCE
AND PART OF A GROUP AND
PUBLISHED BOOKS AND ARTICLES AND
CHAPTERS AND IS AN EDI
EDITOR-IN-CHIEF AND HAS BEEN
HIGHLIGHTED IN WORKSHOPS IN THE
LAST YEARS IN STRASBOURG AND HE
IS THE RECIPIENT,
CONGRATULATIONS, AGAIN FOR THE
AWARD FROM THE AMERICAN CHEMICAL
SOCIETY.
YOU HAVE THE FLOOR.
>> THANK YOU VERY MUCH.
I'LL SPEAK ABOUT SPECIFIC
METHODS OF EXPLORATION OF LARGE
CHEMICAL SPACES.
NOWADAYS, CHEMISTRY GENERATES A
LOT OF DATA.
AND DATABASES AND MORE THAN 26
ARE STORED IN THE DATABASES OF
LARGE COMPANIES.
THE CHEMICAL LIBRARY IS BASED ON
THE SIZE CORRELATES WITH
SCREENING.
THIS HOWEVER IS NOT OBVIOUS.
THE QUESTION ARISES HOW TO FIND
THE LIBRARY THAT'S THE MOST
SUITABLE FOR A PARTICULAR
MOLECULE.
I WOULD LIKE TO PRESENT THE
APPROACH WHICH INCORPORATION OF
THE LIBRARIES AND ALSO IN
COMBINATION WITH GROUPS MAY HELP
DESIGN NOVEL MOLECULAR ENTITIES.
CHEMOGRAPHY CONSIDERS DATA
DISTRIBUTION ON MAPS AND
STRUCTURES AND HOW SUCH MAPS
COULD BE OBTAINED.
A MOLECULE CAN BE PRESENTED AS
AN OBJECT IN THE CHEMICAL SPACE
IN ORDER TO OBTAIN THE MAP WE
NEED TO USE THIS.
THERE'S VARIOUS METHODS AND IN
TERMS OF DATA DISTRIBUTION.
BUT SOME REASON WE SELECTED THE
METHOD THIS SLIDE SHOWS THE
ALGORITHM.
FROM THE LARGE CHEMICAL LIBRARY
WE SELECT A SMALL REPRESENTATIVE
DATA SET AND THEN THE ALGORITHM
MOLECULE.
THIS IS POSITION APPROACHES THE
MAP.
THIS PROJECTION IS DESCRIBED BY
THE LOCATION IN DIFFERENT MODES.
SUCH PROJECTION IS ACROSS AND
HAS DISTRIBUTION AS YOU MAY SEE
IN THE RIGHT CORNER OF THIS
SLIDE.
IN TURN, THIS DISTRIBUTION CAN
BE INCLUDED RELATED.
AND WE HAVE INDIVIDUAL OBJECTS
AND IT CAN CONSIDER THE
PROBABILITY DISTRIBUTION AND
OPENS AN OPPORTUNITY FOR THE
APPROACH FOR MODERN TASKS.
AND THERE'S INFORMATION ABOUT
VARIOUS MOLECULAR PROPERTIES
USING GTM LANDSCAPES.
AND ONE CAN CONSIDER THREE TYPES
OF LANDSCAPES.
DENSE LANDSCAPE, CHARACTERIZING
THE DATA DENSITY, THE CLASS
LANDSCAPE AND THE POPULATION IN
DIFFERENT AREAS OF THE MAP AND
ACTIVITY LANDSCAPE.
EACH OF THESE MAPS CAN BE
ENCODED BY THE DISTRIBUTION OF
CHEMO TYPE AND ACTIVITIES IN THE
CHEMICAL SPACE.
THE LANDSCAPE CHARACTERIZES THE
DATA IN THE DISTRIBUTION.
THEY CAN BE USEFUL.
THUS FOR THE MODE THE LANDSCAPE
CAN BE USED TO PREDICT A CLASS
AND WORKS AS A CLASSIFICATION
MODEL.
THE LANDSCAPE WORKS LIKE A MODEL
TO PREDICT THE ACTIVITY AND IF
THE COMPOUND IS PROJECTED IN
THIS AREA IT'S CONSIDERED TO BE
OUT OF THE MODEL OF THE DOMAIN.
IT CAN BE USED TO ANALYZE
CHEMICAL SPACE AND LIBRARIES.
HERE WE CONSIDER THE CHEMBL
DATABASE CONTAINING 2 MILLION
PROPERTIES AND ACTIVITIES.
6:ON THE NEXT ONE CAN EASILY
IDENTIFY FIVE DENSITY ZONES
WHICH CAN BE ASSOCIATED.
BIOLOGICAL ACTIVITIES ARE SHOWN
ON THE SLIDE.
EACH LANDSCAPE IS STUDIED FOR
BIOLOGICAL TARGETS.
WE BUILT MORE THAN 700 OF DATA
AND FOR ANY NEW COMPOUND
PROJECTED ON THE MAP ONE CAN
IDENTIFY THE MOST SIMILAR
COMPOUND.
AND PREDICT THE PHARMACOLOGIC
PROFILE OF ACTIVITIES USING THIS
ACTIVITY OF LANDSCAPES.
THE NEXT PART OF MY TALK HAS
PAIRWISE COMPARISON FOR A GIVEN
LIBRARY.
I'LL SHOW A FEW EXAMPLES AND THE
FIRST HAS PAIR WISE COMPARISON
FOR COMMERCIALLY AVAILABLE
COMPOUNDS ALL TOGETHER
STRUCTURES WERE CONSIDERED AND
WE TESTED AND WHICH WOULD
ENHANCE THE LIBRARIES.
ON THE OTHER HAND CHEMBL COULD
BE RECOMMENDED FOR THE
BIOLOGICAL MEASURES.
WE HAD DISCOVERED DIFFERENT
COMPOUND CLASSES.
AND THIS ACCOMMODATES THESE AND
WE NEEDED TO IDENTIFY THE ZONE
OR COMPOUNDS FROM ONE DATABASE.
THIS PART WAS NOT ABLE TO
IDENTIFY THE EXISTING LARGER
SPECIFIC SOURCE AND FOR EACH
SELECTED ZONE WE MAPPED.
AND WE CAN CONTINUE THE MAPS AND
THIS CONTAINED COMPOUNDS AND
POPULATED BY ZINC OR THE
COMPOUNDS FROM WHICH WE CAN
CONSTRUCT PARTICULAR CHEMO TYPES
AND STRUCTURES.
AND THIS ALLOWS US TO EXTRACT
MORE THAN 1,000 TYPES NOT BIO
LOGICALLY TESTED AND MORE THAN
20,000 CHEMO TYPES IN THE
COMMERCIAL CHEMICAL SPACE AND
MITIGATED THE CHEMICAL SPACE
WITH SO MANY INDIVIDUAL MAPS.
SO NEXT EXAMPLE AND THIS
COLLECTION CONTAINS 2.2 MILLION
COMPOUNDS.
THE COMPANY INTENDED TO
DIVERSIFY THE LIBRARY FROM THE
DATABASE CONTAINING MORE THAN
WHAT WAS LIMITED.
SO WE PROPOSED THE UNIQUE
MAXIMUM MANY STRATEGIC TOURS
FROM SEGMA AND ALSO USED THE
ACTIVITY AND IN SUCH A WAY WE
SELECTED SOME COMPOUNDS BEFORE
THE PROPERTY SELECTION.
SO THIS DELIVERED SPACE.
THIS IS FROM A LIBRARY AND IN
THIS DEMONSTRATED THE SPACE IS
NOT REDUNDANT BUT COMPLIMENTARY
CONTAINING 40 BILLION STRUCTURES
AND THIS IS REPRESENTED A SUBSET
CHOSE LITTLE IN THE SPACES.
SO YOU CAN SEE HERE BETWEEN HERE
AND WHEN YOU LOOK AT THE
LANDSCAPES WE CAN SAY THE
DATABASE IS ENRICHED BY MORE
INDEXES IN THE COMPOUNDS.
OKAY.
LET'S NOW CONSIDER MULTIPLE
ANALYSIS.
HERE WE COMPARED ONE LIBRARY AND
THE GOAL IS TO SELECT LIBRARIES
THAT RESPOND ACCORDING TO THE
SCORE.
AND IN THIS STUDY WE CONSIDERED
THE NAME FOR THE LIBRARY AS
EXTERNAL AND CHEMBL AS A
REFERENCE DATABASE.
SO JUST TO REMIND YOU IT
REPRESENTS THE COLLECTION OF
SMALL MOLECULES OR DNA.
AND USING COLLECTION OF
COMMERCIALLY AVAILABLE RULES,
ONE CAN GENERATE THOUSANDS
CONTAINING BILLIONS AND IT
SHOULD BE NOTED THAT SYNTHESIS
IS PRETTY EXPENSIVE.
THEREFORE SELECTION OF THE
PARTICULAR DRUG DISCOVERY
CAMPAIGN IS AN IMPORTANT
CHEMICAL TASK.
USING THE LIBRARY CAN REPRESENT
AND WE CAN EASILY SUGGEST A
METRIC FOR EXAMPLE A MODEL WHICH
CAN ASSESS OVERLAP BETWEEN THE
TWO DATABASES.
AND THEN PERFORM COMPUTATIONAL
EXPERIMENT USING A COLLECTION OF
39,000 MODELS IN THE DATABASE
AND DESIGNER TOOL AND WE
DESIGNED THE SIZE FROM EACH OF
THESE WE SELECT A REPRESENTATIVE
DATA SET OF 1 MILLION COMPOUND
AND CONSIDERED 2.5 BILLION
COMPOUNDS ALTOGETHER AND INSECT
DELL WITH CHEMBL AND SELECT THE
SCORE.
WE DISCOVERED IT ALREADY COVERED
MORE THAN 50% OF TYPES OF CHEMBL
AND THEN WE WERE LOOKING FOR THE
DEL WHICH RECOVERS THE AREA AND
WE DISCOVERED THREE COVERED MORE
THAN 80% OF THE SPACE AND
PROJECTED ON THE MAP WE
DISCOVERED THE AREA OF COMPOUNDS
WHICH ARE NOT BY THE DEL
CHEMISTRY.
WHEN YOU CONSIDER THE CHEMICAL
LIBRARIES, WE KCAN CONSIDER THE
SPACING IN WHICH EACH DATA POINT
IS AN INDIVIDUAL LIBRARY.
AND WE CAN VIEW ANOTHER WHICH WE
CALL GTM AND LOOK AT THE
RELATIONSHIP BETWEEN LIBRARIES
IN THIS SPACE.
THIS IS AN EXAMPLE SO IN WHICH
WE PROMOTE THE DELS AND
RESPECTIVE CHEMBL ON THE BOTTOM
LEFT -- AND THE CLOSEST IN THE
DATABASE YOU CAN SEE THE CLOSEST
DEL IS THE MOST SIMILAR TO THE
LANDSCAPE OF CHEMBL AND CAN
ANALYZE REACTION USED TO PULLED
THE DELS AND CONCLUDE THEY HAVE
FEW DELS AND REACTIONS.
THEN AND WE HAVE THE APPLICATION
IN COMBINATION TO GENERATE NOVEL
MOLECULE STRUCTURES AND CHEMICAL
REACTIONS.
SO THE IDEA IS SIMPLE.
SO LET'S CONSIDER WE CAN USE THE
11 VECTORS AND CAN LOOK AT WHERE
WE CAN MANUALLY SELECT AN AREA
POPULATED BY USEFUL STRUCTURES.
AND WE APPLIED THIS APPROACH
SEVERAL YEARS AGO AND WE HAVE
STRUCTURES AND STANDARDS.
AND WE HAVE CHEMICAL FORMATIONS
AND VARIANTS.
SO FOR THIS PURPOSE WE USED
CHEMICAL REACTIONS STORED IN THE
DATABASE AND THIS MAP HAS
VARIABLES AND WE LOOKED AT THE
DATA AND THEN WE DID VALIDATE
THIS DISCOVERY OURSELVES BUT WE
FOUND THE NEW REACTION WHICH IS
LISTED AND THEN WE EXTENDED THIS
APPROACH ON THE SPACE BUILT ON
THE VARIABLES AND WE USED THE
DATA AND WITH THE MODEL
DISPLAYED WE USED THE OUTPUT
PAST THE CHEMICAL STRUCTURE SO
THE TECHNOLOGY CAN BE USED.
SO, NOW I'D LIKE TO SPEAK ABOUT
OTHER MOLECULES.
THE PROBLEM WITH CONVENTIONAL
PIPELINE WITH DIFFERENT
POPULATION IS IT'S TIME
CONSUMING.
CERTAINLY IT'S A FUNCTION OF THE
MOLECULE INTRODUCED BUT WE HAD 4
MILLION COMPOUNDS AND WE DECIDED
TO BUILD A NEURAL NETWORK WHICH
SKIPS THE ENUMERATION STEP AND
PREPARATION.
SO I WOULD LIKE TO GIVE DETAILS
OF THIS NEURAL NETWORK GIVEN IN
THE REFERENCE IN THE PAPER.
THIS IS AN ILLUSTRATION AND THIS
MODEL IS ON THE SMALL SET WITH
90 MILLION DELS.
YOU MAY SEE THE MAPS BUILT WITH
THE NEURAL NETWORK AND WHAT IS
IDENTICAL TO ME MAP BUILT FOR
ENUMERATED CHEMICAL STRUCTURES.
FINALLY, RECENTLY WE PERFORMED
A BENCHMARK WITH CONSIDERATIONS
COMPARED THE INITIAL SPACE IN
THE SPACE IN THE MAP.
WE USED MORE THAN 100 SETS FROM
CHEMBL AND THIS REDUCED THE
BENCHMARK STUDY AND FOR RAY
GIVEN DATA SET, ALL TECHNIQUES
ARE PARTICULARLY AT THE SAME
LEVEL AND NEVER HAVE 100%
PRESERVATION IT'S 50% OR 70%.
BUT GTM OUT PERFORMS OTHER
METHODS WHEN WE PROJECT THE
FORM.
THAT'S WHY OUR CHOICE OF GTM IS
WELL FUNDED.
SO VERY LAST SLIDE.
I WOULD LIKE JUST TO SUMMARIZE
THE GTM AND HAS DATA PROBABILITY
DISTRIBUTION SUITABLE OR DATA
ANALYSIS AND WE HAVE DATA POINTS
TO GO FROM INDIVIDUAL
PROJECTIONS TO DAILY
DISTRIBUTION.
THE MODEL IS REPRESENTATIVE OF
THE SUBSET.
THE LANDSCAPES CAN BE USED FOR
REGRESSION OR SPECIFICATION TEST
AND ENABLES CONVERGENCE OF
LIBRARIES AND FINALLY ET
FACILITATES DE NOVO DESIGN.
I'D LIKE TO THANK MY
COLLABORATORS WHO PARTICIPATED
IN THIS WORK AND OUR PARTNERS
AND WOULD LIKE TO THANK YOU FOR
YOUR ATTENTION.
>>
>> THANK YOU VERY MUCH FOR AN
EXCELLENT PRESENTATION.
AND ANALYSIS OF GTM AND WHAT IT
CAN DO.
THEY HAVE BEEN A FEW QUESTIONS
ON THE SIDEBAR WHICH YOU'LL READ
SOME TO YOU.
SOME YOU ANSWERED AS YOU
PROGRESSED.
ONE OF THE QUESTIONS THAT I'D
LIKE TO SELECT IS THE ZINC
DATABASE, WHY DID YOU USE CHEMBL
AS A REFERENCE LIBRARY FOR DEL.
>> WE CONSIDERED A TASK OF
COLLECTION OF DEL FOR PRIMARY
SCREENING.
WHEN THE TARGET IS NOT KNOWN SO
WE NEED TO HAVE AS A REFERENCE A
MORE DIVERSE DATA COLLECTION.
THAT'S WHY.
WE CONSIDERED THE DATA
COLLECTION.
>> THANK YOU.
ANY COMMENT ON WHETHER AND HOW
YOU CAN USE GTM TO ADDRESS THE
QUESTION OF THE CLIPS?
>> I WOULD SAY GTM WE HAVE WORK
ON THE TOPIC BUT GTM IS NOT THE
BEST TOOL TO IDENTIFY THE CASE
BECAUSE YOU CAN'T WORK WITH THE
DISTRIBUTION AND CAPTURES MOSTLY
THIS LANDSCAPE.
>> THANK YOU.
AND THE LAST QUESTION GIVEN THE
TIME CONSTRAINTS, WHEN YOU
COMPARE GTM WITH OTHER, THE
PRESENTATION IS 2-D BUT
COMPOUNDS ARE 3-D.
>> WE PUBLISHED PAPERED ON
CHEMICAL REACTIONS AND WE HAVE
PROTEINS FOR A SERIES OF
CHEMICAL REACTION AND CAN BE
USED FOR ANALYSIS OF COMPLEX 3-D
STRUCTURES.
>> OKAY.
THANK YOU.
IF YOU HAVE TIME, WOULD YOU
PLEASE ANSWER SOME QUESTIONS IN
THE Q&A ONLINE YOU COULD KINDLY
DO THAT AND THANK YOU FOR AN
EXCELLENT TALK.
LET'S MOVE TO THE NEXT SPEAKER
IN THE SESSION, GISBERT
SCHNEIDER.
HE IS ONE OF THE HIGHEST CITED
SPECIALISTS IN CHEMISTRY.
HE HAS SPEARHEADED THE FUSION OF
MACHINE LEARNING AND A.I. WITH
MEDICINAL AND BIO ORGANIC
CHEMISTRY AND CREATED WITH
COINING THE TERMS AND NOT JUST
CREDITED WITH TERMS BUT HAS DONE
A LOT OF RESEARCH IN THESE AREAS
AND DEVELOPED GROUNDBREAKING
ALGORITHMS FOR MOLECULAR DESIGN
LEADING TO PROFOUND
TRANSFORMATION IN THE WAY WE
ATTEMPT TO DISCOVER NEW DRUGS.
HE'S A PREVENTION WITH
COMPUTERIZED DESIGN IN ETH
ZURICH AND HAS RECEIVED MULTIPLE
AWARDS.
HIS TALK IS ON DENOVEE DESIGN.
>> THANK YOU.
IT'S A PLEASURE DISCUSSING SOME
OF OUR WORK AND OUR IDEAS
REVOLVING AROUND DE NOVO DESIGN.
WE'RE ACTUALLY COMBINING MACHINE
LEARNING AND APPLIED MEDICINAL
CHEMISTRY UNDER ONE ROOF IN ONE
LAB AND MAYBE NEW MOLECULE AND
CHALLENGE A.I. AND MACHINE
LEARNING TOOLS UNTIL THEY BREAK
TO SEE WHICH DEGREE CAN WE USE A
CERTAIN TECHNOLOGY FOR DE NOVO
DESIGN.
NOW, WHY IS IT SO DIFFICULT TO
COME UP WITH NEW DRUGS?
ONE REASON IS THE SEARCH SPACE
IS VIRTUALLY INFINITE.
THE TASK IS TO PICK ONE MOLECULE
OR 10 MOLECULES, MAYBE 100
MOLECULES FROM A VIRTUALLY
INFINITE SEARCH SPACE.
TRADITIONALLY THIS HAS BEEN DONE
IT'S ONE OF THE DRIVING FORCES
OF DRUG DISCOVERIES AND VIRTUAL
SCREENING AS WE ALREADY HAVE
HEARD IN THE PRECEDING TALKS,
CAN DELIVER USEFUL COMPOUNDS WE
CAN TEST IN THE LAB.
NOW, DE NOVO DESIGN AND IN
PARTICULAR A PARTICULAR ASPECT
OF GENERATIVE A.I. WHICH I WILL
HIGHLIGHT IN MY TALK INVERTS
THIS CONCEPT.
WE'RE NO LONGER SCREENING FOR
NEW MOLECULES BUT CREATING AND
GENERATING THE MOLECULE WE WANT
TO SEE.
IN A WAY, WE CAN AVOID THE VAST
SEARCH SPACE BY ASKING A
COMPUTER ALGORITHM TO COME UP
WITH HOPEFULLY OPTIMAL OR AT
LEAST LOCALLY OPTIMAL SOLUTIONS
IN THIS MOLECULAR SPACE.
SO DE NOVO DESIGN RESHAPES THE
DRUG DISCOVERY INVERTING
SCREENING TO DESIGNING
MOLECULES.
IT HAS THE POTENTIAL TO
COMPLEMENT AND POTENTIALLY
REPLACE HIGH THROUGHPUT
SCREENING AT LEAST TO SOME
DEGREE.
THE MOST IMPORTANT ASPECT OF DE
NOVO DESIGN I THINK IS THE TOOLS
COME UP WITH SURPRISING NOVEL
IDEAS WHICH WE HAVEN'T
CONSIDERED OR A CHEMIST HASN'T
CONSIDERED AT THE TIME.
THE AHA EFFECT OF ALGORITHMS IN
MY VIEW IS ONE OF THE MOST
IMPORTANT ASSETS WE HAVE WHEN WE
USE THESE TOOLS.
BEFORE I DIVE INTO THE WORLD OF
DE NOVO DESIGN ALGORITHMS, I'D
LIKE TO MENTION THAT WE'RE
WORKING AT THE EDGE OF CHAOS
WHEN WE INTERFERE WITH LIVING
BIOLOGICAL ALGORITHMS THE
PROBLEMS OF DATA AND MODELS AND
THE PROBLEM OF NON LINEARITY AND
WE HEARD ABOUT ACTIVITY CLIFFS
IN THE PREVIOUS TALK AND THIS
BOILS DOWN TO PARTIAL
PREDICTABILITY.
WE HAVE TO CONCEDE OUR
IMPERFECT, INCOMPLETE
UNDERSTANDING OF THE MOLECULAR
PATHOLOGY AND MOLECULAR COURSES
OF DISEASE AND ACCEPTING THE
FACT WE WILL NOT BE ABLE TO MAKE
PERFECT PREDICTIONS BUT WE CAN
OF COURSE USE DE NOVO DESIGN
WITH A.I. IN A VERY FRUITFUL
MANNER.
AND BECAUSE DRUG DISCOVERY IS
NOT A CLASSIC ENGINEERING
DISCIP
DISCIPLINE WE HAVE TO GO THROUGH
THE DESIGN CYCLE HIGHLIGHTED
HERE IN THIS CARTOON WE START
WITH HYPOTHESIS AND IN CHEMISTRY
IT'S A MOLECULAR STRUCTURE WE
THEN SYNTHESIZE IN THE LAB AND
THE MOLECULES HAVE TO BE TESTED
AND VALIDATED BASED ON THE
RESULTS WE CAN RELEARN AND
REFINE OUR HYPOTHESIS.
OVER THE PAST YEARS WE'VE SEEN A
CLEAR MOVE FROM HUMAN GENERATED
HYPOTHESIS TO MACHINE GENERATED
HYPOTHESES AND LEARNING.
AT THE SAME TIME ROBOTS TAKE
OVER CERTAIN PARTS IN THE
LABORATORY.
THE IDEA AND CONCEPT IS ALREADY
OUT THERE TO HAVE FULLY
AUTOMATED THIS DISCOVERY LAB AND
WE'LL HEAR ABOUT SOME OF THESE
TODAY AND TOMORROW.
CHEMISTRY AND MEDICINAL
CHEMISTRY ARE LEFT BEHIND WHEN
IT COMES TO AUTOMATED DESIGN.
HERE'S AN EXAMPLE.
ON THEN LEFT SIDE YOU SEE A
GENERATIVE MODEL OF SUCH A
HALLWAY WITH COLUMNS AND ON THE
RIGHT A CONCRETE PRINTER WHICH
HAS BEEN BUILT BY MY COLLEAGUE
AND ONCE YOU HAVE THE DESIGN THE
3-D PRINTERS IMMEDIATELY
GENERATE THE BUILDING BLOCKS FOR
NEW ARCHITECTURAL DESIGNS.
IF IT WERE ONLY SO EASY WITH
CHEMICAL SYNTHESIS I'D SHOW
EXAMPLES OF WHAT WORKS BE AND
DOESN'T IN CHEMISTRY AND HOW
A.I. COULD POTENTIALLY OVERCOME
THIS BOTTLENECK OF CHEMICAL
SYNTHESIS IN THE DISCOVERY
CYCLE.
FOR DE NOVO DESIGN WE HAVE TO
HAVE STRUCTURAL GENERATION AND
SCORING AND SELECTION AND
OPTIMIZATION OF THE HYPOTHESIS.
THERE'S VARIOUS TOOLS AVAILABLE
TO GENERATE NEW STRUCTURES.
SCORING AND SELECTION IS THE
MOST CRITICAL AND HERE WE HAVE
THE GREATEST NEED FOR NOVEL
IDEAS AND PROSPECTIVE RESEARCH
AND FINALLY IN PARTICULAR
CERTAIN ACTIVE LEARNING
ALGORITHMS AVAILABLE FOR
OPTIMIZATION.
LET'S START WITH METHOD 1.
I'LL PRESENT FOUR DE NOVO DESIGN
METHODS AND I'LL START WITH A
HISTORIC ONE.
BECAUSE IT'S STILL QUIT ACTUAL
AND QUITE USEFUL NAMELY, DE NOVO
DESIGN IN A LOW DATA SCENARIO
WHEN THERE'S MAYBE ONE COMPOUND
AVAILABLE YOU WISH TO MIMIC AND
MAKE A SCAFFOLD OF.
THE IDEA IS IT MAKE SMALL
MOLECULAR BUILDING BLOCKS AND
ANNOTATE THEM IN THE DATABASE TO
WHICH POTENTIAL CHEMICAL
TRANSFORMATION THEY COULD BE
USED IN.
A RULE-BASED ARCHITECTURE UNDER
LIES IS THE BASIS HERE OF THIS
MACHINE LEARNING TOOL.
YOU DEFINE BUILDING BLOCKS AND
CHEMICAL TRANSFORMATION AND CAN
ENUMERATE AND REITERATE THROUGH
THE CHEMICAL SPACES.
THESE APPROACHES ARE RULE-BASED
MOLECULAR DESIGN ARE EXTREMELY
VALUABLE WHEN ONLY LIMITED DATA
IS AVAILABLE AND AT THE SAME
TIME WITH A NEW DESIGN BECAUSE
WE USE BUILDING BLOCKS AND
CHEMICAL TRANSFORM S FOR CON
STRU
INSTRUCTING MOLECULES THEY COME
UM WITH A PATHWAY FOR EACH
MOLECULE TO BE FED DIRECTLY INTO
AN AUTOMATED SYNTHESIZER TO
PRODUCE THE MOLECULES GENERATED
WITH THE MACHINE LEARNING
APPROACH.
THE QUESTION AROSE THE
DIFFERENCE BETWEEN SCAFFOLD
TOPPING AND DE NOVO DESIGN
DESIGN AND YOU SEE THE FIRST DE
NOVO DESIGN DESIGN WITH A
MACHINE LEARNING DE NOVO
STRUCTURE AND AUTOMIZATION FROM
THE TEMPLATE MOLECULE AS
REFERENCE WHICH YOU WISH TO
MIMIC THE DE NOVO DESIGN TOOL
COMES UP WITH A SUGGESTION THAT
NEEDS TO BE SYNTHESIZES IN THE
LAB AND TESTED.
AND THIS IS FED BACK TO THE
LEARNING ALGORITHM FOR ITERATIVE
COMBINATORIAL OPTIMIZATION.
WHEN WE APPLY DE NOVO DESIGN
SUCH A FASHION IS AN INSTANCE OF
SCAFFOLD POPPING.
HERE'S ANOTHER EXAMPLE ONE CAN
USE DE NOVO DESIGN THE
RULE-BASED DE NOVO DESIGN TOOLS.
FOR EXAMPLE WHEN YOU WISH TO
LOOK AT A COMPLEX SCAFFOLD.
YOU SEE WHAT HAS BEEN
DE-ORPHANED AS A MENTHOL CHANNEL
BETA-BLOCKER BUT REQUIRES A
SYNTHESIS OF 11 LINEAR STEPS.
NOW, DE NOVO DESIGN HAS COME UP
WITH TWO NOVEL CHEMICAL
STRUCTURES.
THESE COULD ALL BE SYNTHESIZES
IN THREE STEPS AND HAVE DESIGN
ACTIVITY AS THE ORIGINAL
TEMPLATE.
AGAIN, DE NOVO DESIGN CAN HELP
US GENERATE NEW STRUCTURES THESE
ARE IP-FROM AN AND SYNTHESIZED
AND MIMIC IN THIS CASE COMPLEX
NATURAL PRODUCT TEMPLATE.
HERE'S A MORE RECENT EXAMPLE OF
THE APPLICATION OF RULE-BASED DE
NOVO DESIGN AND OTHER PRODUCT
WAS CONVERTED INTO THIS DE NOVO
STRUCTURE AND IT'S THE MOST COX
1 INHIBITOR TO DATE AND WE SE
SEE'S -- IT SHOWCASES THE TOOLS
OF THE STRUCTURES KNOWN IN THE
DOMAIN BUT WOULD HAVING SEEN THE
STRUCTURES AND LOOKING WHY THE
DE NOVO DESIGN DOESN'T ACT LIKE
IT BUT IS A SELECTIVE COX 1
INHIBITOR OF COURSE THEN
EXPERIMENTAL STRUCTURE
DETERMINATION IS OF THE ESSENCE
TO HAVE HARD FACTS TO LEAD
OPTIMIZA
OPTIMIZATION.
HERE'S A LINK BETWEEN DESIGN AND
FOLLOW-UP.
METHOD TWO, WE'RE MOVING ON IN
HISTORY MORE RECENTLY CHEMICAL
LANGUAGE MODELS HAVE BEEN
FASHIONABLE.
WE HAD A CLOSE LOOK AT CHEMICAL
LANGUAGE MODELS TO LEARN THE
SYNTAX OF DRUG LIKE MOLECULE
FROM THE REPRESENTATION.
THE FIRST STEP OF CHEMICAL
LANGUAGE MODELS IS TO DEFINE A
TRAINING DATA SET OF DATA
SUITABLE FOR THE PARTICULAR TASK
REPRESENT THESE MOLECULES AS IN
THIS CASE STREAMS AND THEN IN AN
AUTO REGRESSIVE FASHION HAVE THE
CLM TOOL UNDERSTAND, IF I MAY
SAY SO, THE SYNTAX OF SMALL
STREAMS TO REPRODUCE THE INPUT
MOLECULES OF THE TRAINING DATA.
ONCE DONE, WE CAN USE SUCH A
TOOL TO EMIT TO GENERATE NEW
MOLECULES BASED ON THE INTERNAL
PROBABILITY DISTRIBUTIONS
LEARNED.
NOW ALL THESE TOOLS, THESE DEEP
LEARNING TOOLS REQUIRE DATA TO
LEARN FROM.
WE TYPICALLY RELY ON CHEMICAL
DATABASES, SUPPLY CATALOGS,
PATTERNS OR AS WE HEARD FROM
ALEXANDRE VARNEK COMPOUND
SAMPLES.
COMPARED TO FACE RECOGNITION,
COMPUTATIONAL DRUG DESIGN,
SURFACE FROM DATA THEREFORE IT'S
ESSENTIAL WE DEFINE CONTEXT
SPECIFIC DATA SELECTIONS FOR
WHAT IS AT HAND AND REPRESENT
THE MOLECULES.
HUMAN INGENUITY AND KNOWLEDGE
PLAYS AN ESSENTIAL PART.
WE CANNOT EXPECT A DEEP LEARNING
TOOL FOR EXAMPLE A CHEMICAL
LANGUAGE MODEL TO LEARN
EVERYTHING THERE IS ABOUT
CHEMISTRY AND ABOUT THE RELEVANT
MOLECULAR PROPERTIES BY FEEDING
IN MORE AND MORE DATA.
THIS IS AN OBSERVATION OVER THE
PAST YEARS WORKING ONE THESE
TOOLS.
A NATURAL LANGUAGE TO LEARN FOR
SUCH A CHEMICAL LANGUAGE MODEL
IS THE AMINO ACID CODE.
HERE'S AN EXAMPLE OF AN
AUTOMATED DESIGN TEST PIPELINE
WHERE WE TRAINED CHEMICAL
LANGUAGE MODEL ON SEQUENCES.
WE WANTED TO GENERATE AND ASK
THE COMPUTER TO GENERATE NEW
ANTI-CANCER PEPTIDES.
THE SYSTEM WAS TRAINED WITH
KNOWN HELICAL SEQUENCES AND
PEPTIDES.
AND CANCER CELLS IT'S WHAT YOU
SEE HERE IS BREAST CANCER CELL
AND MCF7 CELL THAT CONTAINS
FLUORESCENT DYE AND WHEN THE
PEPTIDE IS APPLIED THE CELL
LEAKS AND DIES.
AND IT TURNED OUT TO BE A CANCER
CELL SELECTIVE PEPTIDE THAT
LEAVES HEALTHY CELLS INTACT.
WHEN WE TALK ABOUT FULL
AUTOMATION AND INTEGRATING THE
DESIGN THEY TEST PARTS OF SUCH
AN AUTO TON WE CAN BUILD
MACHINES AND THIS IS ON A DESK
TOP AND PORTABLE DE NOVO DESIGN
LABORATORY GUIDED BY A CHEMICAL
LANGUAGE MODEL WHICH TRAINED ON
VENDOR CATALOGS.
ON SYNTHESIZABLE MOLECULES WE
WANTED IT TO LEARN ABOUT STOCK
MARKET ABILITY OF THE DESIGNS.
OVER ALL IN THIS PARTICULAR
APPLICATION WE HAD MORE THAN 50%
SUCCESSFUL SYNTHESISES AND MORE
THAN 50% OF THOSE MOLECULES
SYNTHESIZED HAD THE DESIRED
BIOLOGICAL ACTIVITY.
THAT'S A TYPICAL OUTCOME FOR DE
NOVO DESIGN WITH CHEMICAL
LANGUAGE MODELS AS WELL AS WITH
RULE BASED TOOLS.
WE CAN EXPECT -- ONE CAN EXPECT
AT LEAST A 50% IT'S A
CONSERVATIVE ESTIMATE CHANCE OF
BEING SYNTHESIZABLE AND BIO
ACTIVE.
WHEN WE LOOK AT THE MOLECULE
SUGGESTED BY THE MODEL WE SEE
APPROXIMATELY 50% OF THESE
SUBJECTED DESIGNS ARE ACTUALLY
NEW AND THE OTHERS ARE KNOWN IN
ONE OR THE OTHER WAY.
I THINK THIS IS A HEALTHY MIX
BECAUSE WE DON'T WANT TO CREATE
JUST ANY ARBITRARY NEW MOLECULE
BUT SEE THESE NEW MOLECULES AS
SIMILAR TO WHAT IS KNOWN BECAUSE
THIS INCREASES THE CHANCE THEY
ARE SYNTHESIZABLE AND HAVE THE
DESIRED BIO ACTIVITY.
METHOD THREE.
HERE I PRODUCED THE THIRD
DIMENSION BY GRAPH PROCESSING.
THE CHEMICAL WORLD IS NOT FLAT
AND WE NEED TO CONSIDER STEREO
CENTERS AND SHAPE OF MOLECULES
AND OTHER 3-D RELEVANT
PROPERTIES.
IN ORDER TO CONSIDER TO BE ABLE
TO COMPUTE 3 DIMENSIONAL
CONFIRMATIONS WE USED A
GRAPH-BASED REPRESENTATION OF
MOLECULES WITH VERTIX AND TYPES
TO REPRESENT AND COLOR THIS 3
DIMENSIONAL GRAPH AND TO
CONSIDER LOCAL NEIGHBORHOODS,
MESSAGE PASSING TURNED OUT TO BE
ESSENTIAL WHEN WE WISH TO WORK
WITH 3-D GRAPHS OF MOLECULAR
STRUCTURES AND CAN USE DEEP
NEURAL NETWORKS AND CASCADED
STACK NETWORKS TO COMPUTE ALL
KINDS OF PROPERTIES BASED ON
THESE UPDATED MOLECULAR FEATURES
OF THE MOLECULAR GRAPH.
THE FIRST EXAMPLE TO SHOWCASE
WHAT'S POSSIBLE WITH THE TOOLS
IS LEAD OPTIMIZATION.
WE GO FROM THE HIT TO LEAD STAGE
AND FUNCTIONALIZATION.
THE IDEA IS TO TAKE A MOLECULE
AND EXTEND TO GROW THIS MOLECULE
IN SOME CHEMICALLY MEANINGFUL
WAY SO IT CAN BE FUNCTIONALIZED
WITHOUT HAVING TO SYNTHESIZE THE
COMPLETE MOLECULE FROM SCRATCH.
IN ORDER TO DO SO WE STARTED
WITH THE VERSATILE CHEMICAL
REACTION WHICH IS FASHIONABLE
AND USEFUL IN CHEMISTRY WITH OUR
PROJECT PARTNERSES WE GENERATED
TRAINING DATA FOR THIS GRAPH
NEURAL NETWORK AND APPROXIMATELY
2,200 REACTIONS WERE PERFORMED
AND THIS IS A SMALL DATA SET BUT
WAS SUFFICIENT AND THE REACTION
EXAMPLES WERE USED IT TRAIN AN
EFFORT I WILL SHOW ON THE NEXT
SLIDE TO PREDICT POTENTIAL
BORLYATION SITES AND IF IT TOOK
PLACE, WHERE IN THE MOLECULE AND
WHAT'S THE YIELD.
AND WE HAVE THE 3-D OR 2-D GRAPH
USED AS INPUT AND FROM THE TWO
IMPORTANT STEPS ONE CAN COMPUTE
THESE DESIRED OUTPUTS.
UNUP PARTICULAR -- AND WE ADDED
A INPUT AND CHEMISTS CAN START
TO OWN THE DEEP LEARNING TOOLS
BY MODULATING THEIR
FUNCTIONALITY USING WELL
ESTABLISHED CHEMICAL KNOWLEDGE,
FOR EXAMPLE, REACTION
CONDITIONS.
THE OUTCOME WAS VERY SURPRISING.
WE HAD EXTREMELY GOOD SUCCESS
RATES, HIGH SUCCESS RATES FOR
THE BINARY PREDICTION WILL THE
REACTION TAKE PLACE, YES OR NO?
IF SO AND THE PREDICTIVE
REACTION YIELD.
THE ARROW PREDICTED IN THE ORDER
OF THE EXPERIMENTAL ARROW.
HERE ARE A FEW EXAMPLES TAKEN
FROM RAUSCH ONGOING PROJECTS IN
THEIR PHARMA DISCOVERY PROJECTS.
ON THE LEFT SIDE THE INPUT
MOLECULE AND ON THE RIGHT THE
RELATION PRODUCT AND YOU'LL SEE
IN THE MAJORITY OF THE CASES THE
PREDICTIONS OF SUCH A GRAPH
NEURAL NETWORK WERE CORRECT.
I'D LIKE TO HIGHLIGHT ONE
EXAMPLE THAT IS A FAILURE ON THE
LOWER RIGHT.
HERE ARE THE ALGORITHM PREDICTED
NO ATOM WOULD BE A SITE OF
CORRELATION BUT WE OBSERVED THE
CORRELATION AND WHY IS THAT?
THERE WAS NOT ONE EXAMPLE OF AN
CARBON ATOM AS A SUBSTRATE FOR
THE BORYLATION IN THE TRAINING
DATA.
SO THE SYSTEM CANNOT KNOW WHAT
IT HASN'T SEEN.
THIS IS SOMETHING ALSO TO KEEP
IN MIND WHEN IT COMES TO
TRAINING DATA SELECTION THAT WE
HAVE A RATHER DIVERSE AS
POSSIBLE COLLECTION OF INPUT
DATA.
IT'S NOT SO MUCH THE AMOUNT THE
NUMBER OF DATA POINTS WE USE FOR
MODEL TRAINING BUT THE DIVERSITY
OF VARIOUS INSTANCES.
LET'S COMBINE SEQUENCE-BASED
LANGUAGE MODEL TO DEVELOP THE
HOPEFULLY UNIVERSAL TOOL FOR
STRUCTURE BASED DESIGN SEQUENCE
RELATIONSHIP LEARNING.
AND IN ORDER TO DO SO WE NEED TO
CHANGE PERSPECTIVE WHEN IT COMES
TO HOW WE TRAIN THESE DEEP
ARCHITECTURES.
TRADITIONALLY, WE HAVE USED A
LIGAND CENTRIC VIEW.
IT CAN MAKE THE SPACE AND EACH
SMALL DOT MOLECULE A LIGAND AND
WANTED TO PREDICT A LIGAND BASED
DE NOVO DESIGN AND WE TRAINED A
NEURAL NETWORK ON THE
INTERACTION NETWORK OF PROTEINS
AND SMALL MOLECULAR LIGANDS AND
WE CAN NOW USE A GRAPH
REPRESENTATION OF THE BINDING
SITE AS INPUT AND LANGUAGE MODEL
AS DECODER SO THE CHEMICAL
LANGUAGE MODEL SUGGESTS
POTENTIAL LIGANDS FOR THE INPUT
LIGAND BINDING SITE AND POCKET.
WITH THAT APPROACH WE WENT INTO
THE LAB AND THIS IS THE VERY
FIRST EXAMPLE WE PUBLISHED ON
THE TOPIC.
STARTING FROM THE LIGAND BINDING
POCKET THAT WAS NOT IN THE
TRAINING DATA THE DE NOVO DESIGN
STRUCTURE GENERATOR SUGGESTS
MOLECULES 1 AND 2 HAVE
SYNTHESIZABLE IN THE LAB AND LAD
THE DESIRED ACTIVITY AND
SELECTIVITY.
AND THEY'RE NOT SUBSTRATES AND
THEY'RE SELECTIVE TO PFAR GAMMA.
THE LEARNING APPROACH IMPLICITLY
CONSIDERS LIGAND SELECTIVITY
WITHOUT HAVING TO EXPLICITLY
PREDICT ACTIVITY AND WE HAVE TO
SEE AS A COMMUNITY WHETHER THIS
APPROACH ENABLES LIGAND DESIGN
FOR ALL STRUCTURES AND ALPHA
FOLD PREDICTED STRUCTURES AND
THAT'S AN ONGOING ACTIVITY.
LET ME SUMMARIZE, RULE BASED DE
NOVO DESIGN PERFORMS WELL AND
RELIABLE WHEN YOU HAVE LITTLE
LIMITED, SCARCE DATA AVAILABLE
AND DEEP A.I. MODELS HAVE PROVEN
TO DELIVER BIO SYNTHESIZABLE
MOLECULES AND THE MOST CRITICAL
ASPECT OF DEEP LEARNING IN MY
VIEW IS PROBLEM REPRESENTATION
NOT SO MUCH THE TOTAL AMOUNT OF
DATA AND NOT SO MUCH THE CHOICE
OF A GENERATIVE A.I. BUT
TRAINING DATA SHAPING.
MOST IMPORTANTLY THERE'S NO
LEARNING WITHOUT FEEDBACK AND
FOR FEEDBACK WE NEED CHEMISTRY
BECAUSE WE NEED TO MAKE
MOLECULES AS A COMMUNITY,
SYNTHESIZE, TEST THEM.
AS A COMMUNITY I WOULD SAY TEAM
UP CHEMISTS WITH COMPUTER
SCIENTISTS, WITH BIOLOGISTS AND
VICE VERSA BECAUSE WE NEED MUCH
MORE CHEMISTRY.
WITH EVERYTHING WE'RE DOING IN
THE FASCINATING FIELD AREA YOU
SHOULD NOT FORGET WE CANNOT
ESCAPE THE EDGE OF CHAOS AND THE
PARTIAL PREDICTABILITY AND CAN
PUSH THE ENVELOPE TOGETHER.
I THANK TONS OF MY CO-WORKERS
THAT CONTRIBUTED TO THE
DEVELOPMENTS OVER THE LAST 30
YEARS.
MY COLLABORATORS AND SPONSORS
AND YOU FOR LISTENING AND I'LL
BE HAPPY TO ANSWER ANY QUESTION
YOU MAY HAVE.
>> THANK YOU FOR A VERY
STRUCTURED TALK.
WE ARE NOT GOING TO HAVE TOO
MUCH TIME FOR QUESTIONS.
KINDLY ANSWER WHAT YOU SEE ON
THE SCREEN AFTER.
BUT A COUPLE QUESTIONS I'D LIKE
TO ANSWER QUICKLY, GIVEN THE
SCARCE DATA PROBLEM AND WITH
ALPHA FOLD FILLING IN THE GAP
WHAT KIND OF EXPERIMENTAL DATA
ARE NOW MOST CRITICALLY IN NEED?
>> IN MY VIEW IT'S STILL
ACTIVITY DATA FOR THE BROADER
DIVERSITY OF TARGETS.
>> OKAY.
OFF-TARGET ATTEMPT, HOW DO YOU
DESIGN?
>> OFF TARGETS?
WELL, FOR A LONG TIME WE'VE
TRIED TO AND HAVE DEVELOPED
INDIVIDUAL PREDICTION TOOLS FOR
EACH TARGET ONE ACTIVITY AFTER
THE OTHER AND MULTITASKING AND
TO PREDICT AS YES/NO DESIGN
DECISIONS OR BINDS OR DOESN'T
BINE AND WITH THE INTERACTON
LEARNING APPROACH WE TRIED TO
CAPTURE ACTIVITY AND SELECTIVELY
IMPLICITLY RATHER THAN
EXPLICITLY BY TRAINING THE
NEURAL NETWORK OR DEEP LEARNING
MODEL ON THE NETWORK OF
INTERACTIONS.
SO, ONLY RELEVANT INTERACTIONS
LIGANDED -- LIGANDS AND TARGETS
ARE SELECTED.
>> THANK YOU.
WE'LL HAVE TO CLOSE THE SESSION
AND KINDLY ANSWER QUESTIONS WE
DIDN'T HAVE TIME FOR.
LET'S MOVE ON WITH THE SESSION.
THE NEXT SPEAKER IS PROFESSOR
CON
CONNOR COLEY AT MIT IN THE
DEPARTMENT OF CHEMICAL
ENGINEERING AND DEPARTMENT OF
ELECTRICAL ENGINEERING AND
COMPUTER SCIENCE AND HIS GROUP
WORKS WITH MODELS TO UNDERSTAND
HOW MOLECULES BEHAVE, INTERACT
AND REACT AND USES THE
INFORMATION FOR DISCOVERY.
CONNOR HAS RECEIVED REMARKABLE
AWARDS.
WE LOOK FORWARD TO HEARING FROM
YOU.
>> THANK YOU FOR ORGANIZING THE
SESSION AND FOR THE INVITATION
TO SHARE SOME OF OUR WORK WE'RE
DOING HERE AT MIT.
I WANT TO START THE TALK TO
AGGRESSIVELY AGREE WITH MANY
THINGS WE JUST HEARD FROM
GISBERT SCHNEIDER ABOUT
MOLECULAR DESIGN AND THE
CONSIDERATION OF SYNTHESIS.
THAT WILL BE A THEME OF THIS
TALK FOR THE NEXT 25 MINUTES OR
SO.
IF WE WANT TO THINK ABOUT THE
GOAL OF MOLECULAR DESIGN
OPTIMIZATION WE CAN TRY TO MAP
IT INTO A CLASSICAL FORMULATION
TRYING TO FIND OR GIVEN FUNCTION
THAT RETURNS PERHAPS AN ACTIVITY
OR SOME MPO SCORE.
THE BEST DESIGN FROM THIS LARGE
SPACE OF ALL POSSIBLE MOLECULES
AND WE MIGHT THEN CONSIDER THE
OPTIMAL MOLECULE WHAT WE WOULD
ADVANCE FURTHER INTO TESTING.
IN REALITY OFTEN WE WANT TO
OPTIM
OPTIMIZE PROPERTIES DIFFICULT
TO MEASURE WITH A MEASUREMENT
THAT APPROXIMATES SOMETHING WE'D
LIKE TO OPTIMIZE FOR.
IT MATE LOOK LIKE BINDING VERSUS
IN IN VIVO EFFICACY.
BINDING BEING THE PROXY FOR THE
OTHER.
IN REALITY, PERHAPS IT'S NOT
TRULY THE SPACE OF ALL POSSIBLE
MOLECULES WE'RE TRYING TO
NAVIGATE BUT THE SPACE OF
SYNTHESISABLE MOLECULES BECAUSE
THESE ARE THE ONES WE CAN'T TAKE
IN THE LABORATORY TO PHYSICALLY
TEST AND USE FOR FEEDBACK.
SO I VISUALIZE THE LANDSCAPE
THAT CONNECTS STRUCTURE ON THE
HORIZONTAL PLANE HERE TO THE
PROPERTIES THAT EXHIBIT SHOWN AS
PEAKS AND TROUGHS.
THE GOAL OF ELECTED DISCOVERY IS
THE MOLECULES THAT EXIST AT
THESE LOCAL MAXIMA PEAKS.
WHILE WE'VE HAD THIS TESTING OF
MOLECULE FROM A VAST DESIGN
SPACE WE CAN USE TOPOGRAPH CAL
MODELS TO STEER US IN THE RIGHT
DIRECTION TO UNDERSTAND WHERE WE
SHOULD BE LOOKING FOR NEW
MOLECULE AND WAYS TO LOOK RAT
AGAIN -- GENERATIVE DESIGN OR
DE NOVO DESIGN AND USE
GENERATIVE MODELS TO HELP CLIMB
THE PEAKS AND GET TO THESE
MAXIMA MER QUICKLY.
WE'LL BE THINKING ABOUT THE
DESIGN ASPECT IN THE REST OF THE
TALK AND MEANT TO FEED INTO
SYNTHESIS AND TESTING.
WE WON'T EVER HAVE THE FIDELITY
TO PREDICT IN SILICO FOR SOME
TIME SO WE'RE RELIANT ON THE
EXPERIMENTAL SPEED BACKTO
RESTRREVISE
OR MODELS AND DESIGNS OF FOR THE
NEXT ITERATION.
ALL THE MODELS ARE FRAMED IN
THIS ITERATIVE DRUG DISCOVERY
VIEW.
SO WHAT ARE THE CORNERSTONES OF
DISCOVERY CHEMISTRY SETTINGS HAS
TO BE PARALLEL PLATE-BASED
CHEMISTRY.
GIVEN SOME COMMON BUILDING
BLOCKS OR MOTIFS ONE CAN
CONSTRUCT RAPIDLY 96 OR 384 OR
SOME NUMBER OF DERIVATIVES.
IN THE CASE WE SAW FROM
PROFESSOR SCHNEIDER WAS IT WAS
CONCEPTUAL FOR LATE STAGE AND
OTHERS FOR COUPLING OF DIFFERENT
BUILDING BLOCKS IN THIS CASE
THROUGH A COUPLING.
AND SO THIS IS SUMMARIZING
COLLABORATIVE WORK DONE WITH
ABBVIE WHERE WE LOOKED AT THE
HISTORICAL DATA ON COUPLINGS
FROM THE MEDICINAL CHEMISTRY
SETTINGS TO TRAIN REACTION
YIELDS AND UNDERSTAND THE
LIKELIHOODS OF SUCCESSI AND TAK
THE DESIGNING LIBRARIES COUPLING
ONE WITH MANY ACIDS AND DECIDE
WELL, A CHEMIST DESIGN THIS
LIBRARY WITH A CERTAIN SELECTION
OF 46 BRONNIC ACIDS AND IF WE
FOLLOWED THOSE SELECTIONS
EXACTLY, OF THE 46 DESIGNED BY
HUMAN CHEMISTS, 9 6 SUCCEEDED TO
BE TESTED FOR BIOLOGICAL
ACTIVITY.
BY INSERTING THE MODEL THAT
THINK OF CHEMICAL YIELDS WE WERE
ABLE TO DETECT SOME OF THOSE
WOULD BE UNSUITABLE AS A
COUPLING MODEL FOR THIS MOTIF
AND COULD REPLACE SOME OF THOSE
DECISIONS AND END UP INCREASING
MODESTLY THE SUCCESS RATE.
AND FIVE ADDITIONAL COMPOUNDS
WERE ABLE TO BE MADE AND CARRIED
FORWARD TO PURIFICATION.
THIS IS A SINGLE STEP LIBRARY
AND WE'RE OFTEN INTERESTED IN
BUILDING UP LIBRARIES FROM
MULTIPLE SYNTHETIC STEPS AND
GOES TO REACTION TEMPLATE OR
STEP CONSTRAIN DESIGN OF NEW
MOLECULES.
ONE OF THE STRATEGIES WE'VE
TAKEN TO DEVELOPING LIBRARIES OF
MOLECULES CENTERED AROUND
PERHAPS KNOWN SEEDS OR HIP
COMPOUNDS WE'D LIKE TO EXPAND
AND TAKE A MOLECULE THROUGH
SYNTHESIS AND GIVEN THE PATHWAY
SPREAD A MAP OUT ALL THE ANALYZE
WE CAN BELIEVE WE CAN ACCESS BY
SWAPPING BUILDING BLOCKS FOR
EACH OTHER.
AND WE SAW WHAT KINDS OF
REACTIONS WE'LL USE TO BRING
TOGETHER THE COMPONENTS TO
PRODUCE OUR PRODUCTS AND TRYING
TO SIMPLY EXCHANGE ONE MOLECULE
FOR ANOTHER BUT STILL FOLLOW THE
SAME SYNTHETIC STRATEGY.
GIVEN THE PATHWAY WE RELY ON THE
TRANSFORMATION RULES AND WITH
SIMPLE INFORMATICS WE CAN
QUICKLY MAP OUT SOMETIMES
HUNDREDS OF ANALOGS, SOMETIMES
MILLIONS OF CERTAIN MOLECULAR
STRUCTURES.
WE'RE NOT TRYING TO BE CREATIVE
IN OUR APPROACH BUT TRYING TO
REPLICATE THE SYNTHETIC STRATEGY
TO CHANGE THE MOLECULES.
WHEN WE APPLIED THIS IN PRACTICE
FOCUSSING ON A COMPUTATIONAL
EVALUATION, WE'VE SHOWN YOU CAN
FIRST DO A VIRTUAL SCREEN FROM A
FIXED ENUMERATED LIBRARY LIKE
250,000 MOLECULES FROM ZINC HAS
BECOME A COMMON SUBSET TO USE
FOR APPROACHES AND WE FOUND ITS
PROPERTY SCORE WAS JUST UNDER
0.7.
THIS IS A NORMALIZED PROPERTY
SCORE WE'RE TRYING TO MAXIMIZE.
AND THROUGH THIS ENUMERATION
APPROACH WE CAN EXPAND OUT A
SPACE OF SYNTHESIZABLE ANALOGS
THAT ENDS UP INCLUDING THOUSANDS
OF NOVEL STRUCTURES WITH
PROPERTIES THAT EXCEED THE
ORIGINAL HIT.
WE CAN TAKE A STARTING POINT OR
IDEA FOR A MOLECULE AND RAPIDLY
EXPAND IT WITHOUT TRYING TO BE
CREATIVE ABOUT ITS CHEMICAL
SYNTHESIS AND STILL FIND NEW
STRUCTURES THAT MAY EXCEED ITS
PROPERTIES.
THIS STRATEGY IS FOLLOWING A
SINGLE PATHWAY AND IS RELYING
SOLELY ON THE IDEAS OF
ENUMERATION GROUNDED IN
INFORMATICS.
WE'RE ALSO INTERESTED IN THE USE
OF GENERATIVE OR DE NOVO DESIGN
MODELS TO INVENT NEW STRUCTURES.
THEY CAN BE VERY CREATIVE AND
COME UP WITH NEW DESIGNS WE
DIDN'T THINK OF AND PERHAPS
UNLOCK NEW CHEMICAL SPACE THAT
GO BEYOND 42 BILLIONS OF
STRUCTURES WE FIND IN LIBRARIES.
THE DOWN SIDE OF USING THESE
GENERATIVE TECHNIQUES AT TIMES
THEY CAN END UP BEING CREATIVE
TO A FAULT.
THE CREATIVITY REFLECTED ON THE
SLIDE WITH SOME STRUCTURES
MANIFESTS IN IDEAS THAT ARE VERY
NONSENSICAL TO PUT IT LIGHTLY,
BECAUSE THEY'RE EITHER NOT
STABLE OR NOT SYNTHESIZABLE AND
NOT REASONABLE STRUCTURES YOU'D
WANT TO TAKE IN THE LABORATORY
OR SUBMIT TO A CONTRACT
ORGANIZATION FOR SYNTHESIS OR
SUBMIT TO YOUR ROBOTIC PLATFORM
FOR SYNTHESIS IF THAT'S THE WAY
YOU OPERATE.
SO WE'RE INTERESTED IN
GENERATIVE DESIGN BUT WANT TO
AVOID THIS FAIRLY CATASTROPHIC
FAILURE MODE.
SO WHAT IF WE WANT TO USE
GENERATIVE DESIGN BUT STILL WANT
TO RESPECT SYNTHESIS?
THIS IS WHAT TAKES US BACK FROM
THE IDEATION THAT WE CAN FOLLOW
TO THIS STEP BY STEP REACTION
CONSTRAINED APPROACH.
SO IT'S MOST COMMON TO USE
GENERATIVE DESIGN FOR MOLECULAR
STRUCTURES IN WAYS THAT BUILD UP
NEW MOLECULES LETTER BY LETTER
AND WITH GRAPHS AND FRAGMENT OR
FRAGMENT OR ATOM BY ATOM WITH 3
DEM DIMENSIONAL MOLECULES BUT
YOU CAN END UP WITH DESIGNS THAT
ARE OKAY ACCORDING TO TOOLS BUT
NOT REALISTIC OR SYNTHETIC
TRACTABLE AND LOOKING THROUGH
THE LENS OF DEEP LEARNING AND
MODELS TO DESIGN STUCK TOURS NOT
AS LETTERS OR ATOMS BUT BUILDING
UP A REACTION PATHWAY.
AN EARLY CONTRIBUTION WAS A
MODEL TRAINING A NEURAL NETWORK
POLICY THAT COULD SELECT
COMMERCIALLY AVAILABLE BUILDING
BLOCKS AND TEMPLATES TO COMPOSE
A SYNTHETIC TREE FROM THE BOTTOM
UP.
WE CAN GIVE IT TO ACCESS OF
BUILDING BLOCKS AND 100 EXPERT
DEFINED REACTION TEMPLATES.
AND IT WILL COMPOSE WHATEVER
PATHWAYS IT WANTS WITH THE GOAL
OF REACHING A MOLECULE THAT
ACHIEVE A PROPERTY TARGET.
WE CAN TRAIN THE POLICY FOR
MODELS WE DO KNOW HOW TO MAKE
AND THEN OPTIMIZE WITH THOSE
WE'RE TRYING TO OPTIMIZE.
WE CAN CHANGE CONDITIONAL INPUT
TO THE MODEL THAT WILL GIVE US A
DIFFERENT SYNTHETIC PRIMARY
STRUCTURE AND THIS LOOP IS THEN
CONDUCIVE TO OUR OPTIMIZATION OR
ANY MODEL-FREE OPTIMIZATION
APPROACH WE WOULD LIKE.
AS A CONSEQUENCE OF TRAINING THE
GENERATIVE MODELS THAT THINK IN
TERMS OF SYNTHESIS RATHER THAN
ATOMS OR FRAGMENTS WE ENDS UP
FINDING SOLUTIONS WHERE THE
STRUCTURES ARE FAR SIM POLICE
OFFICER AND MORE SYNTHETICALLY
TRA
TRACTABLE AND DIFFICULT TO BRING
TO THE LABORATORY TO TEST.
USING THE COMMERCIAL BLOCKS AND
REACTION TEMPLATES THAT ENCODE
ROBUST TRANSFORMATIONS SOUNDS
FAMILIAR, FOR THOSE FAMILIAR
WITH THE LIBRARIES THIS IS
EXACTLY HOW WE CONSTRUCT THE
LIBRARIES.
WE USE THESE RULES TO ENUMERATE
CHEMICAL REACTIONS TO DEFINE THE
SPACE IN MILLIONS OR TRILLIONS
OR MORE MOLECULAR STRUCTURES
SYNTHETICALLY ACCESSIBLE.
THE USE OF DEEP LEARNING IS
RATHER THAN WORRYING ENUMERATION
WE'RE TEACHING MODELS TO
NAVIGATE THE SPACE AND WHATEVER
GOAL WE'RE TRYING TO OPTIMIZE
FOR THEY KNOW HOW TO NAVIGATE
THE LARGE SPACES WITHOUT EVER
WORRYING HOW MUCH MOLECULES ARE
IN SIDE THE SPACE AND LEARN HOW
TO DO THE SELECTION PROCESS AND
ON THE FLY GENERATE THE
REACTIONS AND MOLECULES IT
THINKS IT NICE TO TO FIND AN
OPTIMUM.
WE HAVE TAKEN THE IDEA IN THE
APPROACHES AND APPLIED TO DEEP
LEARNING APPROACHES.
WE'RE TAKING IN A GRAPH
STRUCTURE OF A MOLECULE WE'D
LIKE TO MAKE AND DECODING A
PATHWAY THAT WE BELIEVE WE CAN
USE TO MAKE IT.
TRE TRANSFORMER MODELS ARE USED
TO PREDICTING SEQUENCES AND
LETTERS OR TOKENS CONSTRUCTING A
SENTENCE.
WE CAN TAKE OUR SYNTHETIC
PATHWAYS WHICH IN GENERAL ARE
TREES BECAUSE YOU CAN HAVE
BRANCHES FOR CONVERTED
SYNTHESIZES, ETCETERA AND
LINEARIZE IT OR REPRESENT IT AS
THIS SEQUENCE AND NOW WE HAVE A
MODEL ARCHITECTURE WE CAN TRAIN
OF LARGE SCALE TO TAKE IN
MOLECULAR STRUCTURES AS TARGETS
AND OUTPUT THE PATHWAYS THAT
APPROXIMATELY OR EXACTLY
RECONSTRUCT THE OUTPUT AND WE
CAN TAKE IN DESIGNS FOR OTHER
GENERATIVE MODELS AND PUT THEM
THROUGH OUR MODEL TO PROPOSE NEW
STRUCTURES THAT ARE GUARANTEED
WITHIN THE LIMITS OF OUR
TEMPLATES TO BE SYNTHETICALLY
ACCESSIBLE.
I'M SHOWING A GENERATIVE DESIGN
FROM AN UNCONSTRAINED MODEL WITH
A DOCKING SCORE APPROXIMATING A
BINDING INTERACTION AND PASS
THIS MOLECULE THROUGH THE
PROJECTOR MODEL TO RETURN A
NOVEL STRUCTURE WHERE IT'S
ASSOCIATED ALREADY WITH A
SYNTHETIC PATHWAY IN THIS STEP
ONE STEP COUPLING AND THIS
APPROACH HOLDS TRUE FOR OTHER
PROTEINS AND IDEAS.
WE CAN TAKE THE RECOMMENDATIONS
FROM UNCONSTRAINED MODELS AND
QUICKLY PROPOSE ROUTES TO MAKE
THEM USING COMMERCIALLY
AVAILABLE BUILDING BLOCKS.
SO, AGAIN WE'VE BEEN FOLLOWING
UP THE LINE OF WORK BECAUSE
REALLY THE NOTION OF GROUNDING
THE PREDICTIONS OF DEEP LEARNING
MOLECULAR DESIGN TOOLS IN
CHEMICAL SYNTHESIS IS ESSENTIAL
FOR MINIMIZING THE GAP BETWEEN
THE WHITE BOARD IDEAS OR IDEAS
WE CAN DRAW UPON THE SCREEN AND
THE PHYSICAL WORLD.
A LOT OF TEXT OR IMAGE
GENERATION PUBLICATIONS OF
GENERATIVE DESIGN LIVE AND DIE
ON THE SCREEN.
YOU DON'T NEED TO PHYSICALLY
SUBSTANTIATE AN OBJECT FROM
CLASS GPT OR STABLE DIFFUSION
BUT WE HAVE TO PHYSICALLY
PRODUCE THEM TO GET ANSWERS ON
THE PROPERTIES.
THE LATEST MODEL IS CALLED SIP
FORMER AND CONTINUED THE IDEA OF
THE ARCHITECTURE WHERE WE TAKE
IN REFERENCE MOLECULE AS INPUTS
AND KEY CODE THE PATHWAY IN OUR
LINEARIZED POST-FIXED NOTATION
CORRESPONDING 1:1 TO THE GRAPH
AND HAVE THE DECODER MODEL WHERE
WE DON'T HAVE TO CONDITION ON A
SPECIFIC REFERENCE.
WE CAN START GENERATING NEW
STRUCTURES FROM SCRATCH.
THE WAY WE'VE BEEN APPLYING THIS
IS TO DO THIS ANALOG GENERATION
TAKING ANY DESIGN STRUCTURE AND
MAP IT BACK TO SYNTHETICALLY
ACCESSIBLE CHEMICAL SPACE AND
THIS BORONS IN STRANGE PLACE AND
WHAT YOU WOULDN'T EXPECT TO FIND
HERE AND TAKING FROM A
GENERATIVE MODEL AND AS YOU PASS
IT THROUGH IT SANITIZES IT AND
CLEANS IT UP INTO A NEW
STRUCTURE WHERE WE KNOW HOW TO
MAKE THE MOLECULE AND PRESERVED
THE PREDICTED PROPERTY SCORE.
SO HOW GOOD IS THE STRUCTURE WE
PRESERVED THROUGH THE ANALOGING
APPROACH.
LIKEWISE WE CAN TAKE THE
UNSATURATED PRODUCT WITH THE
TEST RESULT WE KNOW HOW TO MAKE
AND CORRESPONDS TO I A
COMMERCIALLY AVAILABLE BUILDING
BLOCK AND USE DOCKING SCORES AS
OUR OBJECTIVE FUNCTION TO
OPTIMIZE.
SOMETIMES WE'LL GET A SLIGHTLY
WORSE SCORE, SOMETIMES BETTER
SCORE BUT THE POINT IS WE CAN
TAKE THE IDEAS FROM ANY SOURCE
WE WANT AND CLEAN THEM UP INTO A
MOLECULE WE KNOW HOW TO MAKE.
SO THIS NOTION OF SYNTHESIZABLE
HIT SYNTHESIS CONTINUES IN HIT
EXPANSION STAGES, HIT TO LEAD
AND LEAD OPTIMIZATION TO QUICKLY
TAKE WAY MOLECULE AS A REFERENCE
POINT AND EXPAND OUT THE
CHEMICAL SPACE AROUND IT WITH A
LARGE NUMBER OF SYNTHETICALLY
TRACTABLE AND SIMILAR
STRUCTURES.
SO, SYNTHESIS AGAIN TO EMPHASIZE
THIS POINT, IT INFLUENCES THE
KIND OF MOLECULES WE WANT TO
DESIGN BECAUSE I DON'T WANT TO
WASTE OUR TIME DESIGNING
STRUCTURES THAT CAN'T EVER BE
BROUGHT TO THE LAB AND TESTED.
SO IN THE VIRTUAL LIBRARY OR
SCREENING SETTING THAT'S WHY
WITH WE HAVE THE LIBRARIES
BECAUSE THEY'RE CONSTRUCTED
THROUGH REACTIONS WE BELIEVE WE
CAN RUN.
WHEN WE LOOKED AT THE GENERATIVE
MODELS THEY HAVE THE FAILURE
MODE WHERE IF YOU DON'T TEACH
GENERATIVE MODELS ABOUT
SYNTHESIS THEY CAN SOMETIMES GO
OFF THE RAILS AND PROPOSE
STRANGE STRUCTURES THAT PASS THE
MOST BASIC FILTERS OF CHEMICAL
VALIDITY BUT REALLY DON'T MAKE
SENSE.
NOW, SYNTHESIS IS NOT TRULY A
BINARY PROPERTY OF A MOLECULE OR
WE CAN VERSUS NOT MAKE THIS IT
DOESN'T INFLUENCE ENTIRELY THE
COST OF THE STRUCTURES.
WE WANT MORE NUANCE WHEN WE
THINK ABOUT THE SYNTHETIC
ACCESSIBILITY OF DESIGN
MOLECULES IN SILICO.
THAT BRINGS US TO THE FINAL
VIGNETTE STORY I'D LIKE TO SHARE
IN THE LAST FEW MINUTES.
IT'S A TOOL DESIGN TO TAKE IN
SUCTIONS FROM VIRTUALLY ANY
SOURCE.
THESE DAYS WE HAVE OUR VIRTUAL
LIBRARIES, WE HAVE DE NOVO
DESIGN TOOLS AND EXPERTS WITH
IDEAS AND CAN ENUMERATE
CANDIDATE STRUCTURES.
IF WE'RE TRYING TO UNDERSTAND
FROM THE IDEAS WHICH ARE
VIRTUALLY INFINITE IN NUMBER,
WE'RE TRYING TO UNDERSTAND WHICH
ONES SHOULD WE PRIORITIZE FOR
SYNTHESIS AND EVALUATION.
WE SHOULD DO IT ENTIRELY ON
PREDICTIVE PROPERTIES BECAUSE IT
COULD BE ONE MOLECULE WE CAN
PURCHASE QUICKLY AND ANOTHER
MOLECULE THAT WOULD TAKE A Ph.D.
AND ORGANIC CHEMISTRY LAB AND
WANT TO UNDERSTAND THIS AS MORE
OF THE FLUID VALUE AND
CONTINUOUS VALUE.
SO THIS FRAMEWORK WE CALL
SPARROW TRIES TO TAKE ALL THE
CON -- CANDIDATES AND TO A SET
THAT STRIKES A BALANCE BETWEEN
THE REWARD OR BENEFIT OF TESTING
THE MOLECULES ACCORDING TO
PROPERTY PREDICTION MODELS AND
THEIR SNBT COST.
-- SYNTHETIC COST.
AND WHAT IS INTERESTING IS WE
CONSIDER THINGS LIKE SHARED
INTERMEDIATES AND USING PARALLEL
CHEMISTRY.
NOT ALL MOLECULES COST THE SAME.
NOT ALL REACTION PATHWAYS THAT
HAVE THE SAME NUMBER OF STEPS
ARE THE SAME IN PRACTICE.
SO SPARROW'S TRYING TO MAKE THE
DECISIONS IN THE TRADE-OFFS IN A
QUANTITATIVE WAY.
BECAUSE WE WANTED TO BE A
QUANTITATIVE OPTIMIZATION IT
REQUIRES US TO DEFINE VARIABLES
IN THE OPTIMIZATION.
WE'RE GOING TO SIMPLIFY THINGS
AND FOCUS ON THE INTERWIGS --
INTUITION IN THE SELECTION
PROCESS IT'S TRYING TO MAXIMIZE
THE BENEFIT OR PROPERTIES WE SEE
IN THE STRUCTURES AND TRYING TO
MINIMIZE THE COST IN TERMS OF
BUILDING BLOCK COSTS OR CHEMICAL
COSTS AND FOR MOLECULES THAT
REQUIRE SYNTHESIS, WE'RE
PREDICTING THE LIKELIHOOD OF
SUCCESS AND PATHWAYS USED AND
TRYING TO MINIMIZE THE NUMBER OF
REACTIONS WE'RE RUNNING AND WHEN
WE HAVE DO RUN REACTIONS TRYING
TO MAXIMIZE THE LIKELIHOOD OF
SUCCESS PREDICTED FROM OUR OTHER
CHEMISTRY TOOLS.
SO IN THE PAPER WE DESCRIBED
CASE STUDIES AND FOCUSSING ON
ONE HERE WE LOOKED AT A DESIGN
CYCLE WITH 121 STRUCTURES UNDER
CONSIDERATION.
THIS IS AN AUTONOMOUS DISCOVERY
OF DYE MOLECULES AND COULDN'T
MAKE 121 IN RAY -- A SINGLE
ITERATION.
IN THIS EXAMPLE FROM 121
CANDID
CANDIDATES, SPARROW SAID I THINK
YOU SHOULD TEST THESE 15 BASED
ON YOUR INFORMATION.
SIX SPARROW DETERMINED COULD BE
PURCHASED AND TESTED.
THERE WERE NINE ADDITIONAL
MOLECULE FROM THE LARGE SET
SPARROW SUGGESTED WE MAKE.
MAKING THE NINE REQUIRED SIX
BUILDING BLOCKS AND 10 REACTION
STEPS.
IF YOU LOOK AT THE PATHWAYS AND
PINK STRUCTURES, YOU MIGHT
RECOGNIZE THAT WE'RE USING
SIMILAR STARTING MATERIALS FROM
MULTIPLE CANDIDATES.
WE'RE TESTING INTERMEDIATE ON
THE WAY TO FINAL PRODUCT AND
GETTING COLLOQUIALLY BANG FOR
OUR BUCK AND TESTING A LOT OF
STRUCTURES WITH A SMALL NUMBER
OF SYNTHETIC EFFORT WHILE ABLE
TO ACCESS THIS INTERESTING SET
OF 15 CANDIDATES IN THE DESIGN
CYCLE.
SUMMARIZING THE STORIES WE THINK
ABOUT DIFFERENT WAYS OF
PROPOSING NEW MOLECULAR
STRUCTURES TO DRIVE DESIGN
CYCLES AND IN DO SO WE DON'T
WANT TO END UP WITH
UNSYNTHESIZABLE OR INACCESSIBLE
STRUCTURES AND USING TOOLS TO
PREDICT THE SUCCESSION OF
EXPERIMENTAL VALIDATION WE THINK
OF BIASSING OR CONSTRAINING
GENERATIVE A.I. TO HAVE TO THINK
ABOUT SYNTHETIC PATHWAYS NOT
JUST THINK ABOUT STRUCTURES.
AND WE ALSO THINK ABOUT THE
OPTIMAL DESIGN FRAMEWORKS THAT
LET US BALANCE INFORMATION GAIN
VERSUS STOCK
VER
VERSUS SYNTHETIC COSTS.
I'LL ACKNOWLEDGE ALL THE
FANTASTIC CONTRIBUTIONS FROM THE
GROUP THAT MADE THIS POSSIBLE
AND GENEROUS FUNDING SUPPORT AND
FOR QUESTIONS I'LL FLIP BACK SO
THANK YOU FOR THE INVITATION AND
TIME.
>> THANK YOU FOR AN EXTREMELY
CLEAR TALK.
LET ME START WITH COMMENTS TO
SHARE.
THANK YOU FOR SHARING GENEROUSLY
THE TOOLS YOU DEVELOPED.
SOMEONE WAS SAYING IN THE Q&A
IT'S EXTREMELY EDUCATIONAL FROM
THE FIELD.
THANK YOU FOR SHARING.
A FEW QUESTIONS.
>> SO THE CONNECTION BETWEEN
WHAT WE'RE DOING AND THE WAY
THEY'RE CONSTRUCTED.
THE PHILOSOPHY AND CONSTRAINTS
ARE IDENTICAL.
THE IDEA IS AS THE FIELD
CONVERGES ON UNDERSTANDING OF
WHAT TRANSFORMATIONS MAY BE
POSSIBLE AND WHAT BUILDING
BLOCKS ARE COLLECTIVELY
AVAILABLE, OUR INTENTION IS NOT
TO NECESSARILY REDEFINE THE
IDEAS AND MAKE DIFFERENT
DECISIONS BUT CHANGE HOW WE'RE
NAVIGATING THE SPACES.
AND I'VE ARGUED WITH DEEP
GENERATIVE APPROACHES ONE
DOESN'T HAVE TO DO ENUMERATION
TO BENEFIT FROM THE SPACE THEY
DESIGN.
AS THEY GET BIGGER AND BIGGER
WE'RE NOT GOING TO WANT TO STORE
THEM IN A NUMERATIVE FASHION.
>> HOW YOU NAVIGATE.
SO MORE ON THE PRACTICAL SIDE.
MOST YOUR TALK HAS BEEN
METHODOLOGICAL.
DO YOU HAVE SUCCESS WITH DESIGN
AND MORE ACTIVE COMPOUNDS THAT
MAY BE OUTSIDE OF THE
PRESENTATION.
>> WE HAVE SEVERAL ONGOING
COLLABORATIONS AND VALIDATION
CAMPAIGNS TO SEE HOW THESE
VALIDATES.
I'D SAY THERE'S A CONVENIENCE
FACTOR CERTAINLY IN DOING THE
INITIAL METHOD DEVELOPMENT WITH
COMPUTATIONAL EVALUATION BUT
THAT'S THE INTEREST MOVING
FORWARD.
WE HAVE COLLABORATIONS TO TALK
ABOUT BUT THAT'S THE HOPE.
AND PERHAPS IF I GIVE A SIMILAR
TALK IN A COUPLE YEARS YOU'LL
SEE A FEW MORE IC50 VALUES ON MY
SLIDE AND WILL HAVE MORE SUCCESS
VALUES TO SHARE.
>> A COUPLE QUICK QUESTIONS FROM
ME.
ANY COMMENTARY ON PREDICTIVE
YIELD?
THAT'S SOMETHING WE RARELY TALK
ABOUT.
THAT'S THE FIRST ONE.
A LOT OF THOUGHTS ABOUT
PREDICTING YIELD.
FEW WILL FIT IN THE NEXT 30
SECONDS OR ONE MINUTE.
THERE'S CONFOUNDING VARIABLES
WHEN YOU LOOK AT LITERATURE DATA
SETS AND HOLD CONCENTRATIONS.
YOU CAN HAVE COURSE MODELS OR
HIGH PERFORMING MODELS WITH
NARROW APPLICABILITY AND IT'S
DIFFICULT TO BRIDGE THE TWO AT
THE MOMENT.
>> WE HAVE JOHN CHODERA.
HE'S A MEMBER OF THE
COMPUTATIONAL PROGRAM AT
SLOAN-KETTERING CANCER CENTER IN
NEW YORK CITY AND LOOKING AT
DRUG DISCOVERY POUFRD BY THE
NEXT GENERATION HYBRID MODELS
AND USED TO PREDICT RESISTANCE
IN A DATA MANNER SOMETHING I
WANT TO ADMIRE AND HIGHLIGHT IS
HE'S BEEN ONE OF THE BIGGEST
COMPONENTS AND CONTRIBUTORS TO
OPEN SCIENCE DEVELOPING OPEN NM
AND LOOKED AT IS THE MOON SHOT
AND THE ANTIVIRAL PROGRAMS.
HE'S AN INVESTIGATOR IN OTHER
WORKS AND LOOK FORWARD TO THE
TALK.
>> THANKS FOR THE INVITATION.
I'M HOPING TO GIVE PRACTICAL
ADVICE THAT MAY BE A GOOD
FOUNDATION FOR YOUR EXPLORATIONS
IN THE WORKSHOP AS YOU GO OFF
AND DO MORE COOL STUFF WITH A.I.
AND ML.
JUST TO START, I'D LIKE TO
HIGHLIGHT THE CHALLENGE.
IT'S SLOW AND COSTLY AND THERE'S
AN EXTREMELY HIGH FAILURE RATE.
I TRIED TO LOOK AT THE
STATISTICS AND YOU CAN FIND A
COPY HERE AND FOLLOW ALONG.
I TRIED TO SYNTHESIZE THE
SUCCESS RATES AND COSTS NOT
INCLUDING FAILURES AT EACH STAGE
AND TALK ABOUT HOW TO USE THE
MODELS TO TEST IN THE DISCOVERY
PART BUT THE FAILURE RATES AND
COSTS ARE HIGH FOR THE STAGES.
SO ANYTHING WE CAN DO IN
DISCOVERY TO HAVE MORE SHOTS ON
GOAL OR HIGHER RATES WILL HELP
IMPROVE THE STATE OF DRUG
DISCOVERY.
I SPEAK FROM A LITTLE BIT MUCH
EXPERIENCE.
I COLLABORATE WITH COMPANIES AND
INDUSTRY AND I HAD TO SUPPORT
THE PROGRAM AS PART OF THIS OPEN
SCIENCE EXAMPLE CALLED THE COVID
MOON SHOT UNIQUE AMONG DISCOVERY
PROJECTS PUTTING ALL THE DATA
ONLINE.
YOU CAN GO TO THIS WEBSITE AND
FIND ALL THE DATA IN A DRUG
DISCOVERY PROGRAM AND WANT TO
LOOK AT THE NITTY-GRITTY OF WHAT
HAPPENS IN THE PROGRAMS I'D
ENCOURAGE YOU TO BUT WE STARTED
FROM AN X-RAY FRAGMENT SCREEN
AND THROUGH A SERIES OF RAPID
HIT TO LEAD ASSISTED BY TEAMS AS
WELL AS COMPUTATION WE ABLE TO
LOOK AT OTHER ABILITIES THAN
OTHER DRUGS YOU PLAY BE FAMILIAR
WITH AND MAY HAVE TAKEN.
SO I'LL USE THIS AS AN EXAMPLE
AS I TALK ABOUT THE CHALLENGES
WE'RE FACING.
IN ANY DRUG DISCOVERY PROGRAM
YOU HAVE TO HAVE A DESIGN
OBJECTIVE AND FOR SMALL MOLECULE
THE TARGET CANDIDATE PROFILE
THAT SPECIFIES WHAT WE'RE TRYING
TO ACHIEVE.
YOU MIGHT WANT TO HIT A
PARTICULAR TARGET WITH THE SARS
COV2 PROTEASE AND HAVE TO HIT A
BETTER AFFINITY THAN 10 NANO
MOLAR ACTIVITY AND THERE'S OTHER
PROPERTIES THERE TO ADMINISTER
IT RATHER THAN OTHER ROUTES IT
NEEDS TO BE SOLUBLE AND HALF
LIFE OTHERWISE IT BECOMES
DIFFICULT TO MANAGE.
THERE'S A HUGE PANEL OF TOXICITY
CONCERNS YOU HEARD OF ALREADY
MAKING IT SAFE ENOUGH TO TAKE
FOR FIVE OR SEVER EN DAYS.
-- SEVEN DAYS.
WE CAN'T RELY ON GENERATIVE
MODELS THOUGH THERE'S EXCITEMENT
HOW THEY CAN BE USEFUL.
WE LOOKED AT THE NUMBER
ENORMOUS, 21 MILLION
MEASUREMENTS COMPARED TO THE
NUMBER OF STRUCTURES IN THE
PROTEIN DATA BANK WHICH IS
220,000 AND SIX ORDERS OF
MAGNITUDES FROM THE TOKENS OF
TRAINING GPT 4 AND PRODUCING
USEFUL TEXT IN SUBDOMAINS.
TO MAKE MORE DATA IN DRUG
DISCOVERY USING TRADITIONAL
METHODS WOULD COST AROUND $6
TRILLION AND UNLIKELY WE'LL GET
TO THAT SCALE SO INTEREST TO BE
MORE CLEVER.
WE ORGANIZED INTO AN ASSAY
CASCADE.
WE HAVE ALL THE CHEAP AND EASY
ASSAYS THAT COME RIGHT AFTER
SYNTHESIS IN THE FIRST TIER.
WE STILL HAVE TO MAKE THE
COMPOUND WHICH IS USUALLY
DIFFICULT.
IT COSTS MONEY AND TIME AND THIN
HAVE TO DO LOGISTICS AND IF IT
DOES WELL WE PUT IT THROUGH MORE
EXTENSIVE ASSAYS THAT TELL US
ABOUT THE PHARMACO KINETICS AND
IT'S SOMETHING WE WANT TO DO
WHEN WE HAVE TO.
SO YOU CAN FIND MORE EXAMPLES OF
THESE ASSAY CASCADES FROM OUR
OPEN SCIENCE ANTIVIRAL DRUG
DISCOVERY PROGRAM AND YOU CAN
SEE ASSAY CASCADES FOR ACTIVE
PROGRAMS IF YOU WANT TO EXPLORE
MORE WHAT THE PROGRAMS LOOK LIKE
FOR A DIVERSE SET OF PROGRAMS.
A LOT OF THE TIME YOU'RE
SPENDING TIME THROUGH THE CYCLES
TRYING TO MAKE DESIGN IDEAS,
PRIORITIZING WHICH ONES WILL BE
SUCCESSFUL AND CUEING THEM FOR
SYNTHESIS AND GEKT DOWN TO THE
THINGS TO PUT INTO A PROGRAM TO
CONVINCE A REGULATORY AGENCY
LIKE THE FDA YOU HAVE SOMETHING
SAFE AND EFFICACIOUS ENOUGH TO
PUT INTO HUMANS.
THERE'S A LOT OF OPPORTUNITIES
FOR MODELLING TO IMPACT ALL THE
STAGES.
WE'D LIKE TO HAVE MODELS PRE
PREDICTIVE AND LOOKING AT LIGAND
PROPERTIES AND SOLUBLE ABILITIES
AND LOOKING AT THE TARGET AND WE
PLAY HAVE TO RELY ON SOMETHING
LIKE A PHYSICAL MODEL AND
REQUIRES LIGAND BASED TARGET
WHICH WE PLAY NOT HAVE.
AND THE INDUSTRY STANDARD WAY TO
ADVANCE DRUG DISCOVERY BY MAKING
CHOICES ON WHICH MOLECULES TO
MAKE.
THEY'RE ENABLED BY USING
STRUCTURAL INFORMATION AS SAID
EARLIER TODAY THAT TELL US HOW
THEY MAY INTERACT AND IT'S MORE
THAN DOUBLED IN SIZE FROM
DEPOSITIONS OF STRUCTURES IN
PURSUIT OF OTHER SCIENCE GOALS.
WE'VE SEEN OVER 1,000 NEW
STRUCTURES IN THE LAST YEARS
WHICH REMARKABLE AND THERE'S
IMPORTANT GAPS THAT REMAIN IN
DIFFERENT THINGS LIKE RELATED
PROTEINS TO UNDERSTAND WHAT THEY
LOOK LIKE, FOR EXAMPLE.
GIVEN THINGS LINING ALPHA FOLD
WE'VE BEEN ABLE TO DENSELY MODEL
A LOT OF INTERESTING TARGETS OF
INTEREST.
IT'S BECOMING MORE AND MORE OF
OF A USEFUL RESOURCE WHERE TO
USE THE MODELLING TECHNIQUES TO
ANSWER QUESTIONS.
WHAT THE DESIGN AND CYCLES HAVE
IS TRYING TO IMPROVE POTENCY.
THIS IS A WEAK MOLECULE WE
STARTED WITH OR MAYBE A HIT IN
THE COVID MOONSHOT AND TRYING TO
IMPROVE THE POTENCY OR LATER AT
OTHER ISSUES LIKE METABOLISM
TRYING TO MAINTAIN POTENCY
WORKING OUT CHANGES TO CHANGE
THE PROPERTIES.
YOU MIGHT START WITH THIS AND
IDENTIFIED THE ROUTE WHERE
CHEMISTS HAVE TO OPTIMIZE THE
ROUTE WHICH SOMETIMES TAKES
WEEKS AND MAY NOT BE POSSIBLE
AND MAY COST A LOT OF FOR EACH
ONE YOU WANT TO SYNTHESIZE AND
WHAT YOU WANT TO DO IS USE THE
DIFFERENT STARTING MATERIALS IN
THE LAST ONE CONNOR SUGGESTED
HAVING A CHEMIST MAKE A DOZEN
ANALOGS PER WEEK AND SOME WILL
MAKE JUST SMALL CHANGES AS
YOU'LL SEE HERE UP TO ONE PART
OF THE MOLECULE TO FEEL OUT THE
STRUCTURE ACTIVITY RELATIONSHIP.
YOU PLAY HAVE MANY COMPOUNDS
COMPATIBLE WITH THE LAST
REACTION THE COUPLING.
THERE WERE 15,000 POTENTIAL
GROUPS THAT COULD BE INSTALLED
AT THE R3 SITE.
YOU CAN MODEL ALL AND WHAT THEY
LOOK LIKE IN THE BINDING SITE IN
A STATIC STRUCTURE BUT WOULD
LIKE TO PREDICT IN AN ACCURATE
WAY TO SELECT WHICH COMPOUNDS
ARE LIKELY TO BE MOST SUCCESSFUL
AT MAINTAINING POTENCY AND YOU
MAY NOT HAVE A LOT OF DATA FOR
THIS PARTICULAR TARGET.
ONE OF THE BEST PHYSICAL
MODELLING APPROACHES IS A
CALCULATION.
I'LL TELL YOU ABOUT IT BUT
YOU'RE TRYING TO ASK WHAT IS THE
DIFFERENCE IN BINDING AFFINITY
BETWEEN LIGAND A AND B WHERE
SOME PERTURBATION TO THE
STRUCTURE AND INSTEAD OF RUNNING
A SIMULATION I CAN USE THIS AND
THE CYCLE ENDS UP BEING ZERO
INSTEAD DO TRANSFORMATION OF THE
BIND BEING SITE AND SOLVENT.
AND YOU CAN LOOK AT THE BEST
PRACTICES FOR THE LIVING JOURNAL
OF COMPUTATIONAL SCIENCES.
THE BEST PRACTICES REVIEW THAT
CAPTURES A SNAP SHOT OF WHAT THE
FIELD BELIEVES IS IMPORTANT.
IF YOU WANT TO LEARN MORE ABOUT
HOW WE DO THIS.
THE SAME ENVIRONMENT AND
SUBTRACTING THEM AND I'VE GIVEN
EXAMPLES OF THE CATEGORIES AND
CAN ALSO DRIVE ACTIVITY IF
YOU'RE LOOKING AT RELATED ACTIVE
SITES AND KINASES THAT SHARE
HEMOLOGY OR PREDICTING THE
IMPACT OF MUTATION OF TARGET.
WE'LL SEE PATIENT INDIVIDUALIZED
MUTATIONS THAT MAY CHANGE DRUG
RESPONSE OR SENSITIZE THE TARGET
TO PARTICULAR DRUGS.
OTHERS HAVE USED ITS FOR
BIOLOGICS OPTIMIZING STABILITY
BY INCREASING THE MELTING
TEMPERATURE OF A PARTICULAR TAR
FET.
THERE'S OTHER THINGS LESS TARGET
CENTRIC AND MAYBE ANTI-TARGET
BASED AND YOU AVOID CAUSING
CARDIO TOXICITY.
WE'RE USING THE CALCULATIONS TO
ADDRESS QUESTIONS OF ANTI-TARGET
BINDING AND OTHER PROPERTIES.
AND OTHER PHYSICAL PROPERTIES
LIKE SOLUBILITY AND THE SAME
TECHNOLOGY CAN BE USED TO
ADDRESS A LOT OF THESE
QUESTIONS.
THE WAY THE CALCULATIONS YOU
PERTURB ONE LIGAND TO ANOTHER IN
A COMPLEX WAY AND RUNNING
SIMULATIONS AND ABLE TO GET A
GOOD ESTIMATE OF WHAT THE FREE
ENERGY DIFFERENCE BINDING ARE.
THERE'S OTHER VARIANTS FOR FREE
ENERGIES MORE EXTENSIVE BUT ALL
USE SOME WAY OF SAMPLING THE
CHEMICAL STATES IN SOME WAY.
THERE'S DIFFERENT METHODOLOGIES
AND THIS IS A SNAPSHOT AND SOME
VIEWED AS THE BEST GOLD STANDARD
BUT IT'S A LITTLE BIT TRICKY TO
IMPLEMENT.
THERE'S OTHER VARIANTS WITH COOL
RECENT STATISTICAL MECHANIC
RECOVERIES THAT ALLOWS TO
PARALYZE THE CALCULATIONS OVER
MANY
GPUs AND FOLKS EFFECTIVELY USING
THEM NOW.
THEY ARE DON'T HAVE TO BE
TREMENDOUSLY ACCURATE.
WE'RE MAKING MODIFICATIONS AND
THEY'RE MODELLED IN TERMS OF
BINDING FREE ENERGY CHANGES.
IF YOU LOOK AT THE SHADED REGION
IT'S THE PROBABILITY OF
IMPROVING BY A FACTOR BY 10 AND
DECREASE THE COMPOUNDS TO HIT
THE GOAL BY A FACTOR OF 3.
IF YOU CAN GET TO HERE YOU ARE
SPEEDING THINGS REDUCING THE
DESIGN CYCLES BY AN ORDER OF
MAGNITUDE.
THERE'S EFFORTS THAT GO THROUGH
AND USING TOOL TO COMPUTE
RETROSPECTIVE BENCH MARKS AND
CURATED AND CAN ASSESS THE
ACCURACY ON TARGET CLASSES AN
SCAFF
SCAFFOLDS.
SOMETIMES IT WORKS WELL AND
SOMETIMES NOT AS WELL.
OVER ALL THE ACCURACY IS PRETTY
GOOD.
IT'S EXCEPTIONALLY USEFUL IN
PRIORITIZING WHICH COMPOUNDS YOU
MAKE AND GETTING RID OF BAND
DESIGNS AND ADDRESS THE
QUESTIONS WE'RE ANSWERING OR
ASKING ABOUT BINDING AFFINITY IN
THE PUBLICATION.
WE'VE USED THIS AS PART OF THE
COVID MOON SHOT SUCCESS.
THERE'S OPPORTUNITIES TO MAKE
IMPROVEMENTS IN VARIOUS SCAF
SCAFFOLDS AND THIS IS REALLY
WORKING CLOSELY WITH CHEMISTS
DRIVING THE IDEATION AND
DECISION MAKING IN EACH STAGE
AND IT TAKES A TEAM TO MAKE ALL
THIS HAPPEN.
YOU CAN FIND ALL THE DATA AND
READ ABOUT THE STORY ALL OPEN
SCIENCE.
SOMETIMES HOWEVER, THINGS DID
NOT GO SO WELL.
WE FREQUENTLY FOUND WE HAD
MISMODELLED THE ACTIVE SITE AND
FOUND SOME COMPOUNDS AGREE WELL
AND OTHERS HAVE NO CORRELATION
BETWEEN EXPERIMENT CALCULATION.
THIS IS A MORE GENERAL
PHENOMENA.
THERE'S A PAPER I ENCOURAGE YOU
TO CHECK OUT ABOUT THE KINDS OF
SUCCESSES AND FAILURES FOUND IN
TRYING TO APPLY FREE ENERGY
CALCULATIONS TO PROGRAMS
INTERNALLY AT MERCK AND
CATEGORIZED THE AREAS AND
CLASSES OF FAILURES OBSERVED AND
WAS ABLE TO PIN DOWN.
IN TOTAL HALF THE PROGRAMS
WORKED WELL.
HALF WERE FAILURES FOR DIFFERENT
REASONS.
ONE OTHER IMPORTANT REASON IS
THE CURRENT LEVEL OF ACCURACY IS
AN OPPORTUNITY TO IMPROVE.
WITH CAN'T RUN HUNDREDS OF
PROGRAMS OVER AND OVER AND SEE
WHAT THE IMPACT OF MAKING A TOOL
IS ON THE DISTRIBUTION FUNCTION
BUT CAN DO THIS SYNTHETICALLY
AND MODEL PERHAPS IF WE USE
TOOLS OF DIFFERENT ACCURACY AND
HOW IT SHIFTS COSTS IN A MODEL
PHASE OF THE REALISTIC PROGRAM.
YOU SEE THE AMOUNT OF MONEY IT'S
SAVED GETTING FASTER AND CHEAPER
CAN BE ENORMOUS IN REDUCING THE
MODELLING ERROR.
THERE'S INTEREST IN MAKING THEM
MORE ACCURATE BY IMPROVING ON
THEIR PERFORMANCE.
THERE'S THREE CRITICAL AREAS.
ONE IS THE ACCURACY OR
SIMULATION MELD AND THE ITEMS IN
THE SYSTEM.
WE'VE USED THE FORCE FIELD FORMS
ABOUT 4R5 YEARS OLD NOW.
THERE'S AMPLE OPPORTUNITY TO
MORE TO MORE FASTER HARDWARE AND
LOOKING AT THE WAY TO IMPROVE
AND NEGLECT EFFECTS THAT CAN BE
IMPORTANT IN PARTICULAR
INTERACTION AND INTEREST IN
MODELLING CONFIRMATIONS IN
PROTEIN RELEVANT AND PROTEIN
LIGANDS THAT MAY BE RELEVANT AND
OFTEN THERE'S MILLISECOND SCALE
AND THE MOTIONS ARE SHORTER AT
NANOSECONDS.
ALL RIPE FOR A.I., M.L. TO
ACCELERATE PROGRESS TO MAKE IT
BETTER.
FIRST YOU START WITH A GOOD OPEN
FOUNDATION AS A BASELINE AND
INTEGRATIVE TECHNOLOGIES AND
ASSESSING THEM IN DIFFERENT
WAYS.
THE SOFTWARE FOUNDATION IS A
NON-PROFIT BUILDING THE WHOLE
ECO SYSTEM OF OPEN SOURCE TOOLS
FOR ACCELERATING DRUG DISCOVERY.
IT'S FROM THE OPEN FORCE FIELDS
ACCORDING TO BEST PRACTICES AND
OPEN FREE ENERGY AND PRODUCING
MODELS AN AIMING TO PREDICT
PROPERTIES USING NEW DATA FUNDED
BY AN ARPA-H GRANT.
THERE'S OPEN SOURCE TOOLS AND
DATA SETS AS WELL AS OPEN
ITERATIONS OF FORCE FOLDS THAT
CONFORM TO BEST PRACTICES.
YOU CAN FIND MORE BUT I WANT TO
HIGHLIGHT THE OPEN ENERGY
CONSORTIUM AND EXPERIMENT HOW TO
DO BETTER USING OTHER M.L.
METHODS.
THERE'S A STAFF OF PROFESSIONAL
DEVELOPERS CONTRIBUTING AND
COMMITTEE OF AM DEMOCRATIC
MEMBERS AND GOVERNANCE AND
GUIDANCE AND FUNDING TO MAKE THE
EFFORT SUSTAINABLE.
THEY'VE BEEN WORKING ON BEST
PRACTICES CAPTURED FROM A BROAD
INDUSTRY CROSS SECTION IN THE
PAPERS AND WHAT'S COOL IS YOU
CAN TRY THIS IN A GOOGLE
NOTEBOOK AND PLAY WITH THE
METHODS TODAY.
THERE'S TOOLS AS PART OF THIS
INFRASTRUCTURE.
THERE'S TOOLS FOR PLANNING
CAMPAIGNS OF TRANSFORMATION
NETWORKS WHERE YOU MAY HAVE
RELATED DESIGNS AND ITERATING
TRANSFORMATIONS AND THERE'S AN
ALSO OPEN SOURCE TOOLS THAT LOU
TO YOU EXECUTE THE CALCULATIONS
RAPIDLY AND CURRENTLY TAKE 12
HOURS FOR TRANSFORMATION SO
THEY'RE QUITE EXTENSIVE COMPARED
TO M.L. APPROACHES.
THERE'S DIFFERENT ALGORITHMS
THAT ALLOW US TO BE RAPIDLY
EXTENDED AND YOU CAN IMPROVE AND
SWAP OUT A MODULE AND DO BETTER
AS WELL AS USING BEST PRACTICES
AND ANALYZING THE CALCULATION
AND REPRESENTING HOW YOU ASSESS
THE CALCULATIONS IN A BEST
PRACTICES MANNER TO MAKE SURE
YOU'RE NOT BEING DECEIVED BY
STATISTICS.
THERE'S TOOLS AVAILABLE FOR
ORCHESTRATING LARGE CAMPAIGNS OF
THESE ON DISTRIBUTED OR CLOUD
COMPUTING RESOURCES.
WE MANAGED TO USE THESE BETWEEN
THE ANTIVIRAL DISCOVERY CENTER
SAP AND TWO PROJECTS IN THE LAST
YEAR ALONE.
THESE ARE GETTING TO BE ROBUST
AND SCALABLE.
THE AMAZING THING IS THEY'RE
ABLE TO DO CONTINUOUS
BENCHMARKING USING RESOURCES TO
MAKE SURE THEY'RE ALWAYS
IMPROVING AS THEY ADD NEW
FEATURES OR CHANGE THE ECO
SYSTEM AND MAKE SURE THEY
HAVEN'T BROKEN SOMETHING AND
THERE'S EFFORTS DELIVERING THE
BEST PRACTICES TO YOU AS PART OF
WHAT'S GOING ON IN THE TOOLS.
YOU CAN BUILD PROFESSIONAL
QUALITY DRUG DISCOVERY ECO
SYSTEMS.
JOSH HORTON AND HUGO AS PART OF
THE ANTIVIRAL DRUG DISCOVERY
CENTER WE LEAD THE CENTERS
WORKING ON PANDEMIC
PREPAREDNESS.
THIS IS THE ENTIRE WORK FLOW YOU
CAN DOWNLOAD AND ADAPT TO YOUR
OWN DRUG DISCOVERY NEEDS BY
GOING TO GITHUB.
EVERYTHING IS OPEN SOURCE.
INDUSTRY IS REPORTING SUCCESSFUL
USE OF THESE OPEN SOURCE
PIPELINES WITH REASONABLE
ACCURACY.
WE'VE.
USING THIS IN OUR NEW ANTIVIRAL
DISCOVERY PROGRAMS BUILDING
SECOND AND THIRD GENERATION
INTERERS FOR SARS COV2 AND
BEYOND AND WE LOOK AT
SUSTAINABLE FUNDING.
IT'S A GREAT FEATURE OF THE ECO
SYSTEM AND THERE'S A HUGE NUMBER
OF FOLKS THAT CONTRIBUTED
SCIENTIFICALLY AND OTHERWISE.
WITH THAT I'LL REMIND YOU MY
SLIDES ARE AVAILABLE HERE IF YOU
WANT TO LOOK AT ANY AND I'M
HAPPY TO TAKE ANY QUESTIONS.
I HOPE I KEPT US ON TIME.
>> YOU KEPT AHEAD OF TIME.
>> EXCELLENT.
>> THANK YOU VERY MUCH.
YOU SPOKE FAST.
A FEW QUESTIONS FROM THE Q&A.
YOU HAVE ALLUDED TO USING
MACHINE LEARNING AND MAYBE GIVEN
WE HAVE TIME HOW YOU SEE THE
METHOD GOING FORWARD.
THERE'S SEVERAL LEVELS.
THE FIRST IS THESE CAMPAIGNS ARE
MORE SUCCESSFUL THE MORE
COMPOUNDS YOU SCORE.
ONE OF THE SIMPLE AREAS IS TO
AVOID THE COST OF 12GPU HOURS
PER TRANSFORMATION CAN WE SCORE
LARGER SETS FOR A NEW TARGET
WITHOUT EVERY LIGAND
TRANSFORMATION AND THERE'S 3-D
AND 2-D METHODS.
THIS WILL SAVE HUGE AMOUNTS OF
CLOUD COMPUTING TIME.
THAT'S ONE OPPORTUNITY.
ANOTHER IS TO LOOK AT EACH AREA
WHERE THERE'S NEED TO IMPROVE.
FOLKS ARE, WOULD GO TO TURN THIS
PROBLEM INTO A MACHINE LEARNING
PROBLEM TO LEARN FROM DATA AND
BE TRAINED ON LARGE DATA SETS.
THERE'S A MOVEMENT TOWARDS
BUILDING FOUNDATION MODELS USING
MORE SFOPHISTICATED FORMS LIKE
NEURAL NETWORKS AND HOW TO DO A
BETTER INTEGRATION OF
STATISTICAL MECHANICS WITH
MACHINE LEARNING APPROACHES AND
THERE'S INTEREST IN ALL THE
VARIANTS THAT WILL PREDICT
MULTIPLE PROTEIN CONFIRMATIONS
AND IDEALLY LIGANDS INTO WAYS TO
SAMPLE FROM THE RIGHT DENSITY IS
A HUGE OPEN OPPORTUNITY TO
ACCELERATE THE ACCURACY AND
RELIABILITY OF THE CAL CLAYINGS.
THERE'S AMPLE AREA IN MANY
PLACES FOR MACHINE LEARNING TO
SOLVE THE PROBLEMS.
>> GREAT USE OF MACHINE LEARNING
IN THE AREA THAT HAVE
HISTORICALLY BE ANYONE UNDER
RESOURCED.
>> IT SEEMS LIKE A MISSED
OPPORTUNITY.
THERE'S SYSTEMS WHERE IT'S NOT
AVAILABLE.
>> THERE'S SOME WORK OUT THERE.
YOU'LL SEE IN RECENT PAPERS
TRYING TO USE ALPHA FOLD AND
THERE'S PROBLEMS MAKING IT FIT
AND YOU SOMETIMES HAVE TO USE
DOCKING AND THERE'S METHODS THAT
PREDICT THE STRUCTURE.
THIS IS WHERE MOST ENTHUSIASM IS
THEN YOU'RE OFF TO THE RACES
WITH THE FREE ENERGY
CALCULATIONS.
THE ANSWER IS WE DON'T KNOW YET.
NOT EXHAUSTIVELY BUT THERE'S
DOCKING TO ALPHA FOLD PREDICTIVE
STRUCTURES WHERE THERE'S BEEN
SIGNIFICANT SUCCESS.
>> HERE'S A LOADED QUESTION.
THE SHORT VERSION IS DOES IT
SEEM NO COMMERCIAL TOOLS ARE
NEEDED FOR DRUG DISCOVERY.
MY EXTENSION TO THE QUESTION IS
HOW DO WE PLAY WITH INDUSTRY AND
OPEN WORK, WHO FUNDS IT AND HOW
DO WE MAKE THE TOOLS VALIDATIVE,
USEFUL AND FUNDED?
>> GREAT QUESTION.
THE HOOK IS INDUSTRY WILL HAVE
FIRST THERE'S A LOT OF UTILITY
FOR INDUSTRY PRODUCED SOFTWARE
IN HOUSE AND THROUGH LARGE
COMMERCIAL SOFTWARE VENDORS BUT
NOT EVERYONE NEEDS TO INVENT
EVERYTHING FROM SCRATCH.
THERE'S NO NEED TO HAVE AN
ENTIRE ECO SYSTEM FROM SCRATCH.
THE HOOK IS TO HAVE A GOOD SOLID
FOUNDATION WHICH WE THINK OF AS
THE PARTS OF THE OPEN SOURCE ECO
SYSTEM AND START WITH BEST
PRACTICE AND INNOVATE WHERE YOU
WANT TO HAVE THE PEST IDEAS OR
HAVE THE HIGHEST OR BEST
CONTRIBUTIONS OR WANT TO FOCUS
YOUR ENERGY.
AND EVERYTHING IS MODULAR.
YOU CAN TAKE THE FREE ENERGY
TOOL AND REPLACE THE PROTOCOL
OBJECT WITH YOUR FAVORITE FREE
ENERGY METHOD AND DROP INTO THE
ECHO SYSTEM WITH OTHER BEST
PRACTICES METHODS.
THERE'S OPPORTUNITY FOR SYNERGY
AND FOR SAVING INDUSTRY A LOT OF
TIME IN AREAS WHERE IT DOESN'T
NEED TO ENGINEER EVERYTHING.
THE CURRENT MODEL WE FOCUSSED ON
RIGHT NOW IS WHERE WE HAVE
INDUSTRY FUNDED CONSORTIA WITH
ACADEMIC-LED FEDERAL FUNDING OR
NONPROFIT FUNDING SUPPORTING
THESE PROJECTS JOINTLY.
THAT MODEL HAS BEEN SUCCESSFUL
FOR THE OPEN FORCE FIELD
INITIATIVE WHICH HAS GROWN UNDER
THAT FUNDING AND SCIENTIFIC
INPUT AND GOVERNANCE MODEL
HAVING ACADEMICS AND INDUSTRY
ARE MAKING TOOLS PEOPLE CAN USE.
>> I THINK YOU ANSWERED.
>> EVERYONE SHOULD FUND OPEN
SCIENCE PROJECTS, ALEX.
>> GIVING BACK USEFUL TOOLS.
THAT'S KIND OF WHAT YOUR ANSWER
IS.
>> I AGREE.
WHAT LESSONS WERE LEARNED FROM
THE MOON SHOT PROJECT?
>> WE'VE DONE THINGS DIFFERENT
AS PART OF THE SAP I LEAD.
GO TO SAP DISCOVERY AND LEARN
ABOUT THE CHOICES WE MADE THAT
ARE DIFFERENT.
ONE IS WE GOT INVOLVED IN
CROWDFUNDING BOUGHT IT'S A LOT
OF EFFORT AND YOU KNOW YOU HAVE
JOBS TO GO BACK TO.
WE TRIED TO AUTOMATE AS MUCH AS
WE CAN WITH THESE A.I./M.L.
METHODS AND CHECK OUT THE PAPERS
FROM ALPHA LEAD.
THE OTHER PART IS WE WENT
WITHOUT A PATENT AND THERE'S
OPPORTUNITIES FOR HAVING ENOUGH
GOVERNMENTAL FUNDING FOR THE
MODEL TO BE SUCCESSFUL BUT AS
WE'VE SEEN RECENTLY, GOVERNMENT
FUNDING FOR ANY PANDEMIC
PREPAREDNESS GLOBALLY HAS DRIED
UP.
THE NIH ENDED OUR ANTIVIRAL DRUG
DISCOVERY PROGRAMS TWO YEARS
EARLIER THAN EXPECTED SO IT'S
DIFFICULT TO PURSUE.
WE CAN TRY TO KEEP AFFORDABLE
ACCESS AND HAVE A NEW IP MODEL
TO MAKE SURE THE PATENT WILL
HAVE A SINGLE MOLECULE AND
LICENSED FOR NO COST AS LONG AS
MANUFACTURERS AGREE TO WAVE
THEIR EXCLUSIVITY AND THERE'S
MINOR TWEAKS WE WROTE ABOUT.
>> I I'D LIKE TO END THE
DISCUSSION.
THANK YOU FOR YOUR EXCELLENT
TALK AND WE FINISHED ON TIME.
SARINE IF YOU HAVE CONCLUDING
REMARKS, PLEASE PROCEED.
>> THANK YOU SO MUCH.
>> WE APPRECIATE YOU ALL.
I'M GOING TO BRING BACK THE
AGENDA SO EVERYONE KNOWS WHERE
WE'RE AT.
WE HAVE A SHORT BREAK ABOUT 10
MINUTES AND WE'LL BE BACK AT
4:15 TO START SESSION 3 ON
APPLICATIONS ON IN SILICO
METHODOLOGIES IN DRUG DISCOVERY.
COME BACK AND TAKE A SHORT BREAK
AND COME BACK AT 4:15.
WE'LL START SESSION 3.
>> WELCOME BACK TO THE LAST
SESSION OF THE DAY.
THIS SESSION IS GOING TO BE ON
APPLICATIONS OF IN SILICO
METHODOLOGIES AND PLEASED TO
INTRODUCE DR. RELE PROGRAM
OFFICER AND SENIOR EXPERT IN
VACCINE AND THERAPEUTICS PRODUCT
DEVELOPMENT AND PLATFORM AND
TECHNOLOGIES AND DEVELOPING
ADVENTURES AT BARDA.
HE HAS 23 PLUS YEARS OF
EXPERIENCE AT THE INTERFACE OF
BIO TECH MANUFACTURING AND
OPERATIONS AND NONPROFIT
ACADEMIA AND MANAGEMENT
CONSULTING.
THE FLOOR IS YOURS.
>> THE HOST HAS STOPPED THE
VIDEO.
>> GIVE US A SECOND.
THERE YOU GO.
GREAT.
THE FLOOR IS YOURS.
>> THANK YOU SO MUCH FOR THE
KIND INTRODUCTION.
I'M TALK ABOUT IN SILICO
METHODOLOGIES IN DRUG DISCOVERY
AND HOW TO FOR THE WORKSHOP AND
EXPERTS AND ACADEMIA AND
INDUSTRY.
I WANTED TO THANK ALL FOR BEING
APART OF THIS WORKSHOP.
LISTENING TO ALL THE TALKS YOU
WAS DOING THE SYNTHESIS THE OLD
SCHOOL WAY.
GREAT TO SEE THE WORK BEING
DONE.
RE-EMPHASIZING WHAT'S BEEN SAID,
BIOLOGY SYSTEMS ARE COMPLEX AND
ANY TOOLS THAT CAN SIMULATE
THEIR BEHAVIOR AND THEREFORE
CONTINUING THE DESIGN AND
COMPUTATIONAL MODELS AND
ALGORITHMS AS TOOLS TO PREDICT
THE MOLECULES AND THE LIGANDS
AND RECEPTORS PROVIDING INSIGHT
INTO THE SYNTHESIS AND
POLYMORPHISM AND PATHWAYS FOR
NEXT GENERATION TREATMENT
MODALITIES ARE KEY.
THEREFORE THE MOLECULAR DESIGN
AND TESTING AND SCALE UP OF THE
MOLECULES ARE CRITICAL FOR THE
DRUG DISCOVERY.
THIS IS GOING TO BE A
STIMULATION SESSION ON DRUG
DISCOVERY.
LET ME INTRODUCE OUR FIRST
SPEAKER, SEAN EKINS AND USING
MACHINE LEARNING AND A.I.
APPROACHING AFTER HIS POSTDOC AT
ELI LILLY HE WAS WITH PFIZER AND
THEN LATER WITH START-UP PHARMA
AND COMPANIES HE'S ALSO A SOUGHT
OUT RARE DISEASE ADVISER.
HIS TOPIC OF PRESENTATION IS
OUTLINED IN SMALL DRUG
DISCOVERY.
THE FLOOR IS YOURS.
YOU HAVE 25 MINUTES AND IN 22
MINUTES I'LL INFORM YOU TO WRAP
UP.
>> THANK YOU.
IT'S GREAT TO BE HERE.
GREAT HONOR TO BE INVITED AND
WOULD LIKE TO THANK THE
ORGANIZERS.
IT'S BEEN STIMULATING LISTENING
TO THE TALKS.
I'LL GIVE AN OVERVIEW SINCE
FINDING THE SMALL COMPANY.
IT WILL BE QUITE DIFFERENT TO
MOST THE TALKS WHICH HAVE BEEN
PRIMARILY ACADEMIC.
WE'RE A SMALL COMPANY THE FOLKS
ON THE SLIDE ARE ESSENTIALLY
RESPONSIBLE FOR THE WORK I'LL
PRESENT.
AND WE DEVELOPED VARIOUS
TECHNOLOGIES AND SOFTWARE FOR
DRUG DISCOVERY AND APPLICATIONS.
WHAT'S UNIQUE ABOUT US IS WE
HAVEN'T BEEN FUNDED BY VENTURE
CAPITAL OR ANGEL FUNDING AND
RELIED ON FUNDING FROM NIH AND
DEPARTMENT OF DEFENSE.
LOTS OF GRANT WRITING.
THAT'S ENABLED US TO BRING IN
ABOUT $23.5 MILLION SO FAR WITH
COLLABORATORS ON VARIOUS PRO
PROJECTS SINCE 2016.
A UNIQUE WAY TO FUND A COMPANY
AND HAVE LABS ON THE CAMPUS OF
NC STATE UNIVERSITY.
WE'RE NOT AFFILIATED WITH THE
UNIVERSITY BUT TAKE ADVANTAGE OF
GREAT COLLABORATIONS AND ACCESS
TO AMAZING STUDENTS AND FACULTY.
OVER THE YEARS WE'VE BEEN ABLE
TO PRODUCE A NUMBER OF PATENTS
AND PUBLISH QUITE WIDELY.
I CAN'T TALK ABOUT EVERY SINGLE
THING WE'VE DONE SINCE FOUNDING
THE COMPANY BUT I WANT TO GIVE
IDEAS OF WHAT'S POSSIBLE USING
THE TECHNOLOGIES.
ALSO POSSIBLY INSPIRE OTHERS TO
START THEIR OWN COMPANY.
WE'RE AN STREAMING ORGANIZATION.
WE BUILD OUR OWN SOFTWARE AND
LICENSE FOR PREDICTING
PROPERTIES AND TOXICITY IS A BIG
ONE I'LL TOUCH ON LATER.
WE ALSO USE THESE TECHNOLOGIES
TO HELP DRIVE OUR PIPELINE AND
FIND MOLECULES AND OPTIMIZE
MOLECULES AND THIRDLY, WE'RE A
CONTRACT SERVICES ORGANIZATION.
PHARMACEUTICAL COMPANIES COME
WITH US WITH CHALLENGING
PROBLEMS AND WORK ON SOLVING
THEM USING OUR TECHNOLOGIES.
THAT'S SO FAR ENABLED US TO BOOT
STRAP THE COMPANY AND STARTING
WITH TRADITIONAL A.I.
APPROACHES.
THESE ARE TOOLS YOU'VE SEEN
QUITE A BIT OF TODAY ALREADY
TOOLS THAT TAKE THE DATA SET
WHETHER BY ACTIVITY OR PHYSICAL
CHEMICAL PROPERTY FOR EXAMPLE
AND ALLOW YOU TO BUILD A
PREDICTIVE MODEL AND THEN
OBVIOUSLY GENERATE PREDICTIONS
FOR MOLECULES WHETHER FROM A
VIRTUAL LIBRARY OR AVAILABLE
LIBRARY OF MOLECULES.
THEY DON'T NECESSARILY ALLOW TO
YOU CREATE ANYTHING NEW BUT MINE
THE DATA AND FIND NEW MOLECULES
WITH NEW USES.
I'LL TRY TO SHOW SOME OF THOSE
EXAMPLES AFTER THE SLIDE.
THE SECOND APPROACH IS MORE
GENERATIVE A.I. WHERE YOU CAN
LARGE LANGUAGE MODELS OR
APPROACHES AND ENABLE MAKING
DECISIONS GOING BEYOND THE DATA
TO PROPOSE NEW NOVEL MOLECULES
AND AS A SMALL COMPANY WE'RE AT
THE CUTTING EDGE OF USING THE
TOOLS.
WHEN WE SEE WHAT BIG PHARMA ARE
DOING WITH WAY MORE INVESTMENT
THEN THE $23.5 MILLION WE
BROUGHT IN FROM NIH AND DOD IT
MAKES YOU REALIZE BIG PHARMA HAS
MASSIVELY UNDER INVESTED IN A.I.
TO DATE.
TO SOME PEOPLE IT MAY SEEM
CONTROVERSIAL BUT MANY HAVE TOLD
US THEY'RE A DECADE-PLUS BEHIND.
THERE'S STILL A LOT OF
OPPORTUNITY FOR THESE TYPES OF
COMPANIES TO INVEST IN THIS
SPACE.
THE TYPES OF TOOLS WE INITIALLY
USED STARTED WITH BAYESIAN
ALGORITHMS AND RANDOM FOREST,
ETCETERA.
OVER OF THE YEARS WE EXPANDED TO
LOOK AT MANY ALGORITHMS IN
PARALLEL THE CLASSIFICATION AND
REGRESSION AND MORE
SOPHISTICATED TOOLS LIKE
DIFFERENT APPROACHES.
A WHOLE MIX OF ALGORITHMS.
AS I'LL TOUCH ON LATER, PICKING
WHICH ALGORITHMS TO USE FOR A
PARTICULAR DATA SET IS IN ITSELF
A CHALLENGE.
INITIALLY, WE WERE TRYING TO
FOCUS ON MOLECULES AND TRY TO
DESIGN, BUY, TEST, ANALYZE.
WE WEREN'T THINKING OF MAKING
MOLECULES BECAUSE THEY WERE
BEYOND WHAT WE WERE ABLE TO DO.
INITIALLY AS A SMALL COMPANY WE
DIDN'T HAVE A LAB.
WE WERE USING TECHNOLOGIES AN
BUILDING TECHNOLOGIES SO WE
COULD SCREEN THROUGH FDA
APPROVED DRUGS, ETCETERA AND
SEND THEM OFF TO COLLABORATORS.
ONE TARGET AT A TIME OR ONE
PHENOTYPIC ASSAY AT A TIME.
THIS LED US TO PUT TOGETHER TEST
CASES TO SAY LET'S DEMONSTRATE
THIS AND PRODUCE SOFTWARE AND
BUNDLE DIFFERENT ALGORITHMS AND
USE THEM FOR PREDICTIONS.
WHAT YOU'LL SEE ARE THINGS WE'VE
DONE DEVELOPING THE
TECHNOLOGIES.
AND THEY OBVIOUSLY LED US TO
DEVELOP TOOLS THAT WE HAVE NOW
BEEN ABLE TO LICENSE TO
COMPANIES.
ONE OF THE EARLY EXAMPLES WE
PUBLISHED BACK IN 2015 WAS
AROUND THE EBOLA OUTBREAK OF
2014.
NOWADAYS WE THINK OF OUTBREAKS
LIKE SARS COVID AND THERE WAS A
BIG EBOLA OUTBREAK.
I WAS INTERESTED IN TRYING TO
ACCESS BUILD MACHINE LEARNING
MODEL TO TRY TO IDENTIFY NEW
ANTIVIRALS.
A COLLABORATOR AT THE TIME BUILD
A MODEL WE WERE ABLE TO USE TO
VIRTUALLY SCREEN A LIBRARY OF
ABOUT 2500 MOLECULES IN THE
LIBRARY.
THAT THEN ENABLED US TO RANK THE
MOLECULE AND SELECTED THREE OF
THEM.
TO TEST WITH A COLLABORATOR IN
VITRO.
WE WERE ABLE TO FIND ALL THREE
MOLECULES THAT HAVEN'T BEEN
TESTED PREVIOUSLY WERE NANOMOLE
ACTIVES IN THIS IN VITRO ASSAY.
THAT WAS SURPRISING FOR A NUMBER
OF REASONS.
ONE WAS AN ANTI-MARLARIAL
APPROVED SEVERAL YEARS AFTER THE
PUBLICATION.
AND WE WERE ABLE TO USE THE ONE
GRAPH TO GET AN R21 FROM NCATS.
SO WE'RE VERY GRATEFUL TO THEM.
THAT KICKED OFF WORK WITHIN OUR
COMPANY IN TERMS OF EVALUATING
THE MOLECULE NOT ONLY FOR EBOLA
BUT ALSO AGAINST OTHER VIRUSES.
OVER SUBSEQUENT YEARS WE'VE BEEN
ABLE TO USE THIS MOLECULE
IDENTIFIED THROUGH MACHINE
LEARNING AND CHARACTERIZE IT AND
SHOWED IT ACCUMULATES IN THE
LYSOSOMES AND CHANGING PH AND AN
INHIBITOR AND MOLAR AND
PERFORMED DOCKI IING TO EVALUA
TARGETS AND FOLLOWED THIS UP
WITH DOING BINDING AND SHOWED
IT'S A PRETTY POTENT LOW MICRO
MOLAR BINDER TO THE TARGET AND
LOOKED AT HOW IT MAY INTERFERE
WITH ACCESS OF THE VIRUS INTO
CELLS.
WE SHOWED WITH A PSEUDO VIRUS
THE MOLECULE WAS ABLE TO BLOCK
UPTAKE.
SO REALLY INTERESTING OVER THE
YEARS WE'VE BEEN ABLE TO FILL
OUT WHAT'S UNDERSTOOD ABOUT THE
MOLECULE TRADITIONALLY THOUGHT
OF AS AN ANTI-MALARIAL AND HAVE
COULD HAVE PK INFORMING.
THIS ENABLES US TO DO A SINGLE
DOSE STUDY IN MICE AND COULD DO
A TIME ASSAY AS WELL.
PROVIDING THE DRUG WINDOWS AFTER
DOSING WITH THE EBOLA VIRUS AND
SHOWING WE COULD HAVE PROTECTION
UP TO 24 HOURS.
SO WE'RE ABLE TO LOOK AT PLAQUE
ASSAY TO ASSESS THE VIRAL LEVELS
AND THIS WORK WAS PUBLISHED BY
OUR LAB AND HE'S WITHIN
RESPONSIBLE FOR MOST THE WORK
DURING THIS PERIOD AND SINCE.
AND ULTIMATELY THIS ENABLED US
TO OBTAIN AN ORPHAN DRUG
DESIGNATION FROM THE FDA IN
2019.
FOLLOWING THIS WE WERE ABLE TO
CHARACTERIZE THE MOLECULE AND
SHOW WE COULD DOSE AT A SINGLE
DOSE AND WE HAD SOME PROTECTION
AGAINST THE EBOLA VIRUS.
IT WASN'T AS CONVINCING AS IN
THE MOUSE AND PRIMARILY DUE TO
THE INCREASED METABOLISM IN THE
GUINEA PIG.
ADDITIONAL STUDIES WITH
COLLABORATORS FOUND IT WAS
EFFECTIVE AGAINST THREE STRAINS.
AND THESE ASSAYS WERE ABLE TO
SHOW A SINGLE MICRO MOLAR.
THE NEXT STAGE IS TO GET INTO
NON HUMAN PRIMATES AND WE HAVE
TO OBTAIN SEVERAL MILLIONS OF
FUNDING AND WE'RE CURRENTLY
PUTTING GRANTS IN TO GET THAT
WORK DONE.
WHAT WAS INTERESTING FOR US
AROUND 2020, WE COULD NOT HAVE
PREDICTED THERE WOULD BE A MAJOR
OUTBREAK OF A NEW VIRUS THAT
ULTIMATELY BECAME SARS COV2.
WE WERE EXCITED BECAUSE
REMDESIVIR ANOTHER EBOLA DRUG
SHOWED TO BE EFFICACIOUS AND
WANTED TO LOOK AT THE ACTIVITY.
WE COLLABORATED WITH A NUMBER OF
GROUPS IN THE U.S. AND OUTSIDE
TO THE U.S. TO TRY TO TEST THE
MOLECULE.
INITIALLY WE WERE ABLE TO SHOW
ACTIVITY IN CELL LINES WE FOUND
ACTIVITY IN THE CELLS AND THEN
WE WERE ABLE TO TEST THE
MOLECULE IN A MOUSE MODEL WITH A
GROUP IN BRAZIL AND SHOW IT'S
EFFICACIOUS AND IT HAD AN IMPACT
ON INFLAMMATORY CYTOKINES AND
WAS AN ANTI-INFLAMMATORY
MOLECULE AND DECREASED CELL
INFILTRATION AND SHOWED IT WAS
AN INHIBITOR IN VITRO.
THE MOLECULE APPEARS TO TARGET
DIFFERENT TARGETS IN DIFFERENT
VIRUSES BASED ON THE DATA WE
HAVE.
AND THE PHARMACEUTICAL COMPANY
TOOK THIS COMBINATION WHICH THEY
MADE FOR MALARIA AND TOOK IT
THROUGH PHASE 3 CLINICAL TRIAL
AS WELL IN HUMANS.
AT ABOUT THE SAME TIME WE WERE
WORKING ON THAT WE HAD DONE
VIRTUAL SCREEN AGAIN WITH THE
DATA SET THIS TIME FOR PARASITE
RESULTING IN A DISEASE.
THIS IS WITH COLLABORATORS UCSD
AND IN THE STUDY WE LOOKED AT
BUILDING A MODEL WITH 4,000
MODELS FROM THE LITERATURE AND
THEN SCREENED THE LIBRARY OF
ABOUT 7,500 MOLECULES AND PICKED
FOR SOME FOR TESTING AND THRN WE
WERE ABLE TO TEST IN AP MALLS
AND IN THE ACUTE MODEL AND
SHOWED IT HAD GOOD EFFICACY AND
AGAIN WE USED THIS IN VIVO DATA
TO OBTAIN THE ORPHAN DRUG
DESIGNATION FOR THIS PARTICULAR
USE.
THAT ALSO LED US TO OBTAIN
ANOTHER GRANT THE UG2, UG3 GRANT
THAT FUNDED ANOTHER GROUP TO DO
MORE IN VIVO WORK AND PK.
IN THIS CASE WE WERE LOOKING AT
DIFFERENT STRAINS AND TRYING TO
SHOW WHETHER THERE WAS EFFICACY
IN THE CHRONIC MODEL OF THE
DISEASE.
WHAT WE FOUND USING FROM IP
DOSING TO DOSING THE DRUG
ORALLY, WE'RE ABLE TO SHOW
EFFICACY STILL IN ACUTE MODELS
WITH A DIFFERENT STRAIN OF THE
PARASITE.
UNFORTUNATELY WE WEREN'T ABLE TO
SHOW EFFICACY IN A LONGER
CHRONIC MODEL THAT GOES ON FOR
SIX MONTHS OR SO.
SO WE THINK THERE'S STILL SOME
WAY TO GO IN TERMS OF OPTIMIZING
THE MODELS HERE IN TERMS OF THE
STRAINS.
WE MIGHT HAVE A STRAIN DEPENDENT
EFFECT.
INTERESTINGLY THE HIGH LEVEL
VIEW GOING FROM MACHINE LEARNING
TO IDENTIFYING MOLECULES THROUGH
IN VIVO WE'VE DONE SCREENS FOR
OTHER TARGETS AND DONE WORK AS
HIGHLIGHTED BRIEFLY ON THIS
SLIDE WITH A COUPLE PROVOCATIONS
AGAIN THIS WORK WAS LED BY ANNA
POOL IN OUR GROUP AND WORKING
WITH KEN JACOBSON AND IT
DEMONSTRATES FINDING NEW USES
FOR FDA APPROVED DRUGS AND THEY
HAVE ACTIVITY IN BINDING S FOR A
NUMBER OF ADENOSINE RECEPTORS
AND HAVING ACTIVITY IN IN TAR
GETS AS WELL.
WE TRIED THE TECHNOLOGIES IN
TERMS OF PHENOTYPIC SCREENS AND
MER TARGET BASED SCREENS.
THEN A MORE RECENT EXAMPLE WAS
AT THE REQUEST OF AN ACADEMIC AT
THE UNIVERSITY OF KENTUCKY WHO
IDENTIFIED A COUPLE CHEMOKINE
RECEPTORS AS BEING INVOLVED IN
STROKE.
HE WONDERED IF WE COULD FIND AN
FDA APPROVED DRUG THAT MAY BE AN
ANTAGONIST AND CURATED A COUPLE
DATA SETS AND BUILT MACHINE
LEARNING MODELS AND USED THOSE
TO SCREEN A LIBRARY OF FDA
APPROVED DRUGS.
WE'RE ABLE TO FIND A NUMBER OF
KINASE I
KIN
KINASE INHIBITORS AND ONE HAD A
TARGET FOR HIV.
SO WE'VE DONE PSEUDO VIRUS WORK
TO SHOW THE MOLECULE ACTUALLY
BLOCKS ENTRY AS WELL.
SO REALLY INTERESTING HOW WE'VE
BEEN ABLE TO TAKE THIS BASIC
MACHINE LEARNING APPROACH AND GO
AFTER DIFFERENT TYPES OF
TARGETS.
WE'VE ALSO LOOKED AT ENZYMES AND
LOOKING AT AN IMPORTANT ENZYME
FOR A NUMBER OF CNS DISEASES AND
TARGET THROUGH MOLECULE AND
ENVIRONMENT, PESTICIDES,
ETCETERA.
AND THERE ISN'T CURRENTLY A
SELECTIVE INHIBITOR THAT'S FDA
APPROVED.
SO WE LOOKED AT DIFFERENT
MACHINE LEARNING APPROACHES TO
TRY TO COME UP ONE SELECTIVE
MOLECULE.
SO WE BUILT MODELS WITH A COUPLE
THOUSAND MOLECULE AND CURATED
SOME LITERATURE WE CAME UP WITH
MACHINE LEARNING APPROACHES AND
DEEP LEARNING AND A CONTRASTED
LEARNING THAT WILL HOPEFULLY
ENABLE US TO DO SELECTIVE
MOLECULE.
ONCE WE VALIDATED THE MODELS
THIS IS WORK WITH A POSTDOC IN
OUR GROUP AND WE DID A SCREEN OF
ABOUT 5 MILLION COMMERCIAL
MOLECULES OR TESTING IN VITRO
AND THEN WE PURCHASED ABOUT 20
MOLECULES FOR TESTING AND
FOLLOWED UP WITH DOSE RESPONSE
DATA AND WERE ABLE TO GET A 35%
HIT RATE AND ONE MOLECULE HAD AN
IC50 AND WAS COMPARABLE TO
CONTROL MOLECULE OF ABOUT HALF A
MOLAR.
FASCINATING APPROACH TRYING TO
FIND MOLECULES THAT ARE
SELECTIVE.
ALL THESE ARE GOING AFTER TRYING
TO FIND BIO OPTIC MOLECULES.
THESE ARE FRAET EXAMPLES BUT
THERE'S A GROWING AMOUNT OF DATA
TRYING TO UNDERSTAND THE
PROPERTIES OF MOLECULES AND
THOUGH THESE DATA SETS ARE
NOWHERE NEAR THE AMOUNT A LARGE
PHARMA WOULD HAVE, THEY'RE
ENOUGH INFORMATION TO BUILD
CONVINCING MACHINE LEARNING
MODEL TO TAKE ADVANTAGE OF.
I'LL SHOW ONE EXAMPLE OF RECENT
PUBLICATION FROM LAST YEAR WHERE
WE WERE CURATING FOR NOT ONLY
RATS BUT ALSO MOUSE AND VARIOUS
FISH SPECIES IN ORDER TO BUILD
CLASSIFICATION AND REGRESSION
MODELS TO PREDICT LC50 AND THESE
WE CAN COMBINE AND MAKE
AVAILABLE IN A MEGA TOX AND
PULLED TOGETHER A COLLECTION
MUCH -- OF MODELS AND ONE
CHALLENGE IS TO COME UP WITH A
WAY TO HONE IN ON WHICH MODEL TO
USE WITH WHICH PARTICULAR DATA
SET.
WE LOOKED AT THIS AND A RECENT
PUBLICATION GOES INTO THIS GOLD
THE GOLDILOCKS.
I RECOMMEND CHECKING THAT OUT.
AND THE LAST EXAMPLE I'D LAKE TO
PROVIDE IS OUR WORK AROUND USING
GENERATIVE A.I.
WE'VE BUILT ON SOME TOOLS THAT
ARE AVAILABLE.
WE FOCUSSED IN ON AN LSDM MODEL
AND I PLUGGED INTO MODELS FOR
PROPERTIES AND WE USE A COUPLE
WAYS OF BEING ABLE TO GENERATE
MOLECULES.
WE COULD THINK OF THIS APPROACH
AS BEING AN INTER AND OUTER LOOP
WHERE IF WE'RE TRYING TO GET
SELECTIVELY WE'D HAVE THRESHOLDS
WITH TARGETS AND OFF TARGETS OF
INTEREST AND GO THROUGH THE
SELECTIVE WIN TO COME UP WITH
MOLECULES THAT MASS OUR
CRITERIA.
AND ONE EXAMPLE I'LL SHOW IS
CURRENTLY A MANUSCRIPT THAT'S
BEEN SUBMITTED.
THIS IS UNPUBLISHED AS OF NOW
WHERE WE STARTED USING
PSILOCYBIN WITH VARIOUS MODELS
AND RECEPTORS TRYING TO FIND
MOLECULES THAT WERE AGONISTS AND
SELECT IT AND ULTIMATELY THE
MODEL WE GENERATED CAME UP WITH
01 MOLECULES THAT WE'RE ABLE TO
SYNTHESIZE AND WE PERFORMED THE
IN VITRO TESTING AND ULTIMATELY
ONE OF THESE TURNED OUT TO BE
SELECTIVE AGONIST WITH AN EC50
OF ABOUT 12 NANO MOLAR AND FOR
REFERENCE THERE'S THIS RECEPTOR
OF 9 NANO MOLMOLAR.
WE DON'T THINK IT'S THE PERFECT
MOLECULE BUT HAVE ACHIEVED GOOD
BRAIN MODELS SO GOING BEYOND
TRYING TO REPURPOSE THEM BUT
TRYING TO DESIGN AND SYNTHESIZE
MOLECULES.
OBVIOUSLY MORE EXPENSIVE AND
TIME CONSUMING.
OUR INTEREST IN THE AREA HAS
TAKEN OFF IN TERMS OF TRYING TO
AVOID MOLECULES WITH PSYCHEDELIC
EFFECTS.
THIS LED US TO GOING OUT TO
LITERATURE TRYING TO FIND DATA
SETS IN VITRO AND IN VIVO WE
COULD THEN MODEL TO PREDICT
WHETHER A MOLECULE WILL BE
PSYCHEDELIC OR NOT.
A RECENT PUBLICATION HIGHLIGHT
THE WORK.
A GREAT EXAMPLE OF GOING INTO
THE LITERATURE AND TRYING TO
MANUALLY CREATE DATA FROM BOOKS
WHERE IT MADE MOLECULES THAT
WERE PSYCHEDELIC OR NOT AND
TESTED THEM ON HIMSELF AND
FRIENDS AND SCORE THEM AND WE
WERE ABLE TO BUILD
CLASSIFICATION MODELS AND THEN
ABLE TO VALIDATE WITH ADDITIONAL
MODELS AND THESE MODELS IN VITRO
AND IN VIVO WE USED TO PREDICT
OTHER MOLECULES AS WELL AS
MOLECULES WITH OTHER DATA.
A STARTING POINT TO TRY TO COME
UP WITH THE NEXT GENERATION OF
MODELS TO PREDICT PSYCHEDELIC
EFFECT.
OBVIOUSLY THERE'S GREAT
OPPORTUNITY BUT A WORD OF
CAUTION AND ONE EXAMPLE LED TO A
PUBLICATION A COUPLE YEARS AGO
THAT RECEIVED A LITTLE BIT OF
VISIBILITY AND ENDED UP ON
NETFLIX WHERE WE WERE WE WERE
TRYING TO SHOW THE POTENTIAL FOR
MISUSE OF A.I.
IN THIS CASE WE'RE DESIGNING NEW
MOLECULE USING MODELS FOR
INHIBITION AND OBVIOUSLY THIS IS
NOT IDEAL.
WE DIDN'T MAKE THE MOLECULE BUT
IT GOES TO SHOW THESE KINDS OF
TECHNOLOGIES CAN DESIGN
PLAUSIBLE MOLECULES AND THE
POTENTIAL FOR MISUSE IS GREAT.
SO JUST TO SUMMARIZE, THERE'S A
LARGE AMOUNT OF INFORMATION BY
ACTIVITY FOR TARGETS IN THE
PUBLIC DOMAIN.
THIS IS RIFE FOR CURATION AND
MODELLING.
HOPEFULLY WHAT I'VE GIVEN IS A
TASTE OF WHAT WE'VE BEEN ABLE TO
DO WITH SMALL AMOUNTS OF FUNDING
IN GENERAL MATERIALS.
I THINK A.I. ENABLES US TO MOVE
DEFINITELY FASTER, SAVES US A
LOT OF MONEY AND WE'RE DOING
THIS CHEAPLY AND ABLE TO REDUCE
ANIMAL USE.
THE CHALLENGE IS WE HAVE TO GO
FURTHER AND TAKE THE HITS OR
MOLECULES IN ANIMAL MODELS AND
IT'S EXPENSIVE.
DESIGNING IS CHEAP AND FINDING
MOLECULES IS CHEAP AND TESTING
AND SYNTHESIZING THEM IS A HUGE
PROBLEM.
WE'LL STILL HAVE THIS VALLEY OF
DEATH.
A.I. IS ANOTHER TOOL IN OUR TOOL
KIT AND FOR US IT'S BEEN IMPACT.
I'D LIKE TO ACKNOWLEDGE THE NIH
AND THE COLLABORATORS ON EBOLA
AND TOO MANY COLLABORATORS TO
ACKNOWLEDGE BUT GRATEFUL FOR THE
IMPACT AND COLLABORATORS ON
COVID AND FINALLY MY TEAM WHO
HAVE BEEN INVOLVED.
MOST THE WORK I'VE SHOWN
INVOLVES THESE PEOPLE AND IF
YOU'D LIKE TO LEARN MORE WE PUT
TOGETHER A BOOK THAT SHOULD BE
COMING OUT LATER THIS YEAR.
I'D LIKE TO THANK OUR OTHER
COLLABORATORS AND FROM YOU HAVE
AN A.I. FOCUSSED MANUSCRIPTS,
PLEASE SUBMIT THEM FOR
METABOLISM DISPOSITION AND A.I.
LIFE SCIENCES.
THANK YOU.
>> THANK YOU, SEAN FOR AN
EXCELLENT TALK.
I'M TRYING TO SEE IF THERE ARE
ANY QUESTIONS.
NONE SO FAR.
MAYBE I CAN ASK ONE QUESTION,
SEAN.
THE STUDIES SHOWED PARTICULARLY
FOR EBOLA, I ASSUME IS THAT
MANUFACTURED AT THIS POINT OR
MORE STILL IN THE LAB
DEVELOPMENT STAGES?
>> IT'S ACTUALLY AN APPROVED
DRUG IN EUROPE.
IT'S USED IN AFRICA.
IT'S USED AS AN ANTI-MARLARIAL
BUT NOT AN INDIVIDUAL MOLECULE.
WE WERE SHOWING THE INDIVIDUAL
USE AND YOU CAN SEE ONE THE
GROUP.
I'M HAPPY TO TOUCH BASE WITH YOU
LATER AND FOLLOW-UP.
>> THAT WOULD BE GREAT.
THANK YOU.
WE PRESENTED IT TO BARDA OVER
THE YEARS.
I THINK WHEN THERE WAS THE COVID
O
OUTBREAK SEVERAL TIMES AND GOT
NO INTEREST FROM ANY OF THE
ORGANIZATIONS WE PRESENTED TO.
I THINK THEY WERE LOOKING FOR
NOVEL MOLECULES AND LESS ON THE
REPURPOSING SIDE.
THERE'S DEFINITELY PROS AND CONS
TO REPURPOSING AND WE WERE
CERTAINLY INTERESTED TO SEE WHAT
INITIATIVES WERE OUT THERE AND
AS A SMALL COMPANY SOME REQUIRED
US TO PUT UP FUNDING AND
CERTAINLY DIDN'T HAVE THE WHE
WHEREWITHAL AND THAT WAS A
CHALLENGE IS A SIGNIFICANT
INVESTMENT FROM OUR SIDE AND
THAT'S IMPOSSIBLE FOR A TINY
COMPANY SO IT WASN'T REALISTIC.
THANK YOU.
>> BASED ON WHAT YOU PRESENTED,
ALL THE OTHER MOLECULES IN THE
GPCR MODULATORS IS THAT FOCUS TO
LOOK AT ALREADY ESTABLISHED
MOLECULES AND REPOSITION THEM?
IT SEEMS LIKE YOUR A.I. TOOLS
ARE PREDOMINANTLY DIRECTED
TOWARDS THAT RATHER THAN
DEVELOPING NEW MOLECULES.
IS THAT A FAIR STATEMENT?
>> WHAT I WAS TRYING TO DO AND
PROBABLY WASN'T CLEAR ABOUT IT
WHICH I'M HAPPY TO CLARIFY SO
IT'S A GREAT QUESTION.
WE STARTED OFF ON REPURPOSING
HEAVILY ON THE TRADITIONAL A.I.
JUST TRYING TO MINE DATA SETS
AND MOVED TO DESIGNING NEW
MOLECU
MOLECULES.
MORE DE NOVO DESIGN AND I THINK
THAT'S WHERE WE ARE TODAY.
WE'RE MORE ON THAT DE NOVO
DESIGN SIDE.
WE STILL SCREEN THROUGH BUT WE
HAD SO MUCH FEEDBACK FROM PHARMA
AND THERE WERE ONLY INTERESTED
IN MOFL MOLECULES.
THEY WEREN'T INTERESTED IN
REPURPOSING AND THAT'S WHAT
DROVE US TO DO DESIGN AND FIND
FUNDERS TO DO THE SYNTHESIS.
WE'VE BEEN FORTUNATE TO GET
GRANTS AND NIDA THAT ALLOWED TO
DO MORE GENERATIVE DESIGN AND
COMING UP WITH DESIGNS FOR
OPIOID USE DISORDER AND HAVE
BEEN AUTOMOBILE TO LEVERAGE
INHIBITION WORK TO TRY TO DESIGN
MOLECULES AS POTENTIAL
REACTIVATORS USED FOR PESTICIDE
AND CHEMICAL WEAPON POISONING.
IT'S EVOLVED AND THERE'S BEEN
SERIOUS PAIR PIVOTS BUT HAPPY
TO FOLLOW-UP WITH ANYONE IF YOU
WANT TO DROP ME AN E-MAIL.
>> INSTEAD OF SYNTHESIZING
COMPOUNDS, DO YOU HAVE GOOD LUCK
PURCHASING COMPOUNDS THAT ARE IN
SIMILARLY TO THE VIRTUAL HITS?
>> WE'VE TRIED THAT AS WELL.
I THINK THE MOST CONVINCING --
THIS IS JUST REALLY A FIS SOF
CAL -- PHILOSOPHICAL QUESTION.
I'VE BEEN INTERESTED TO VALIDATE
THE MODELS CONVINCINGLY AND YOU
CAN NEVER REALLY WIN OVER PEOPLE
UNLESS YOU DESIGN NOVEL
MOLECULES.
BUYING THEM IS A CHEAP WAY TO DO
IT AND I'M FINE WITH THAT BUT
PEOPLE ARE NOT GOING TO BE
REALLY TRULY CONVINCED UNTIL YOU
DESIGN TRULY NOVEL MOLECULE THAT
ARE NOT AVAILABLE FROM ANYONE
ELSE, HAVE NEVER BEEN PUBLISHED,
NOT PUT INTO A PATENT.
THAT'S THE ONLY WAY WE'LL
CONVINCE PEOPLE.
AND THEN YOU JUST GET IN THE MIN
YOU -- MINUTIA AND IT'S NEVER
ENDING AND SOMEONE WILL FIND A
METHOD OR SIMILARITY OR WHATEVER
OH, IT'S SIMILAR TO THIS AND
THERE'S -- IT'S CHEAPER TO DO
THAT BUT THE MOST CONVINCING
THING IS TO MAKE MOLECULES.
>> IF YOU CAN STAY ONLINE, SEAN
AND ANSWER THE REST OF THE
QUESTION.
>> THANK YOU.
>> OUR NEXT SPEAKER IS WOODY
SHERMAN.
HE IS A CHIEF INNOVATION OFFICER
OF A THERAPEUTICS COMPANY AND
WAS CHIEF COMPUTATIONAL
SCIENTIST AND AT SILICON
THERAPEUTICS AND HEAD OF
APPLICATIONS SCIENCE AT
SHORTINGER.
AND HE'S A PROFESSOR AT THE
UNIVERSITY OF MASSACHUSETTS
AMHERST WHERE HE GIVES LECTURES
ON DRUG DESIGN AND COMPANY
CREATION.
HE COMPLETED HIS Ph.D. AT MIT
AND PUBLISHED 400 PEER REVIEWED
PAPERS INCLUDING QUANTUM
MECHANICS AND OTHER AREAS OF
INFORMATICS AND MACHINE
LEARNING.
HIS TOPIC IS FROM CONCEPT TO
CLINIC.
DISCOVERY.
THE FLOOR IS ALL YOURS.
>> THANK YOU FOR THE
INTRODUCTION AND THANK YOU FOR
THE INVITATION TO BE HERE AND
ORGANIZERS.
ARE YOU SEEING THE FULL SCREEN
MODE AND NOT MY PRESENTATION
MODE?
>> YES.
>> GREAT.
I'LL START WITH A HIGH LEVEL
SUMMARY OF OUR VIEW AND IT'S AN
A.I. SYMPOSIUM AND JOHN DID A
GREAT JOB EMPHASIZING THE
IMPORTANCE OF DYNAMICS SO I'LL
BE ABLE TO GET THROUGH THIS
QUICKLY.
TALK ABOUT THE PLATFORM WE'VE
BEEN DEVELOPING SINCE THE DAYS
OF SILICON THERAPEUTICS AND
CONTINUE TO DEVELOP WITHIN
PSIVANT AND LIVING THINGS CAN BE
UNDERSTAND IN TERMS OF THE
JIGGLING OF ATOMS.
WE'VE KNOWN THE CHALLENGE IS
BEING ABLE TO MAKE PREDICTIONS
AN RUN SIMULATIONS AT SCALE TO
INFLUENCE OUR DRUG DISCOVERY
PROCESSES.
WE STARTED WITH THE LAB AND
SOMETIMES COMPUTRATION IS THE
RIGHT TOOL AND BEING PRACTICAL
AND FOCUSSING ON WHAT MATTERS IN
CONTEXT OF OUR DRUG DISCOVERY
PRODUCT.
WE'RE NOT JUST TALKING ABOUT
SIMULATIONS AND WATCHING THINGS
JIGGLE AROUND WE'RE INTERESTED
IN FREE ENERGY LANDSCAPES AND
DIFFERENT CONFIRMATIONS AND HOW
THOSE RELATE TO BIOLOGY.
WE DON'T DO THIS WITH MOLECULAR
DYNAMICS AND BELIEVE THAT IS
GOING TO BE THE MOST EFFECTIVE
WAY OF MAKING AN IMPACT WITH
MOLECULAR SIMULATIONS.
HOW DO WE THINK ABOUT THIS?
WITH KNOW PROTEINS ARE FLEXIBLE
AND THAT FLEXIBILITY ENDS UP
INFLUENCING BIOLOGY.
HOW WE ARE HANDLING THESE WITH
THE SIMULATIONS WE DEAL WITH
WATERS AND COFACTORS AND IONS
AND THE THINGS IN THERE.
OUR ULTIMATE GOAL IS
UNDERSTANDING HOW MOLECULES
BOUND TO PROTEINS.
NOT JUST HOW THEY BIND IN TERMS
OF BINDING AFFINITIES BUT WHAT
HAPPENS WHEN THEY BIND TO THE
PROTEIN CONFIRMATIONAL CHANGES
AS MOLECULES LOCK INTO CERTAIN
CONFIRMATIONS AND HOW TO GO
ABOUT DESIGNING BETTER MOLECULE
WITH BETTER PROFILES AND
INFLUENCE PROTEIN IN WAYS THAT
INFLUENCE BIOLOGY.
WE USE THE SIMULATION TO
UNDERSTAND MOLECULAR MECHANISMS
WHICH ARE BASED ON DYNAMICS TO
PREDICT BINDING POCKETS AND
STICKS TO A PROTEIN.
YOU HAVE TO UNDERSTAND NOT JUST
BINDABILITY BUT DRUGABILITY AND
LOU THEY RELATE TO FUNCTION
BASED ON DYNAMICS AND BINDING
FREE ENERGY CALCULATION WHICH I
BELIEVE ARE THE MOST ACCURATE
APPROACH TO BINDING AFFINITY
ALSO DONE WITH MOLECULAR DYN
DYNAMICS EXAMPLE.
WHY NOT MACHINE LEARNING FOR
EVERYTHING?
WE'RE A DATA POOR REGIME.
WE NEED TO WORK ON THAT IT'S A
GREAT PLACE TO PITCH IN THERE'S
BEEN PROTEIN STRUCTURE
PREDICTION AND THAT'S ONE OF
MANY AREAS TO SOLVE.
YOU WE'RE TOLD WHAT TO DO BY
PEOPLE AND YOU NEED TO PROVIDE
INSIGHT TO PEOPLE AND THERE'S
NOT A LOT OF IN THAT SPACE
THOUGH IT'S A GREAT AREA OF
RESEARCH FOLKS ARE WORKING ON
AND THERE'S NOT A LOT OF END TO
END SOLUTIONS PVEN I THINK
SEVERAL FOLKS HERE ARE TALKED
ABOUT BUILDING ALONG THAT
PROCESS.
WE'RE STILL EARLY IN THE DAYS
NOT TO SAY THEY'RE NOT USEFUL.
ALPHA FOLD MADE A BIG IMPACT AND
ESSENTIALLY SOLVED THE PROTEIN
STRUCTURE PREDICTION PROBLEM BUT
WITHOUT COMPOUNDS.
AS SUCH THIS IS CURRENTLY
COMPARABLE TO SOLVING AN APOE
CRYSTAL STRUCTURE AND IT MAKES
MINIMAL IMPACT AND NEED TO TALK
ABOUT HOW MOLECULES BIND THE
PROTEINS AND CAN INFLUENCE THE
STRUCTURE.
THERE'S GENERATIVE MODELLING AND
TOOLS OUT THERE QUITE USEFUL IN
THE CONTEXT OF A BROADER SUITE
OF THINGS.
ACTIVE LEARNING.
CONNOR DEVELOPED THIS TOOL AND
WE'RE BIG BELIEVERS IN ACTIVE
LEARNING.
IT IMPROVES THE EFFICIENCY OF
WHATEVER YOU'RE APPLYING THE
SCORING FUNCTION IN.
WHETHER IT'S DOCKING OR FREE
ENERGY CALCULATIONS.
IT DIDN'T MAKE THE CALCULATIONS
MORE ACCURATE IT JUST ALLOWS YOU
TO GET TO MORE MOLECULES MORE
QUICKLY BUT IT'S VALUABLE.
OVER ALL THERE ARE GREAT TOOLS
OUT THERE AND THE FIELD IS
EVOLVING AND WE'RE IN A FASTLY
GROWING AID WITH INVESTMENTS BUT
LIMITED PREDICTIVE ABILITY FOR
LIGAND BINDING.
ONE QUESTION EARLIER AND THE
CHAT WAS RELATED TO ML METHODS
AND THEY CAN PREDICT LIGAND
AFFINITIES WHEN YOU HAVE A LOT
OF DATA BUT THEN IT'S LESS
INTERESTING TO PREDICT AFFINITY
SO WHEN YOU GET IN THE MOST
INTERESTING NOVEL DRUG DISCOVERY
PROBLEMS FOR CHALLENGING TARGETS
WHERE THERE MAY NOT BE A LOT OF
CHEMICAL MATTER, PHYSICS IS
STILL WHAT WE RELY ON MOST
HEAVILY.
WE USE ML MODELS AND FOR
PREDICTIONS AND SOME GENERATIVE
MODELLING AS WELL.
OUR APPROACH HAS BEEN SINCE THE
BEGINNING SINCE THE SILICON
THERAPEUTICS DAYS TO BUILD A
TOOL KIT.
ONE THING I LEARNED MANY TIMES
OVER THE YEARS IS DRUG DISCOVERY
IS HARD FOR MANY DIFFERENT
REASONS AND EACH DRUG DISCOVERY
TENDS FOR CHALLENGING FOR
DIFFERENT REASONS.
WHILE THERE'S SOME RECURRING
THEMES LIKE BINDING ENERGIES AND
THAT WAS A UNIQUE PROBLEM THOUGH
WE WERE TRYING TO PREDICT
BINDING ENERGIES AND STANDARD
STUFF.
RATHER THAN BUILDING A BLACK BOX
OR SINGLE TOOL TO SOLVE ONE
THING REALLY WELL, WE BUILT A
FLEXIBLE TOOLKIT BASED ON
QUANTUM MECHANICS AND MEC LAR
SIMULATION AND A.I. MACHINE
LEARNING AND ROBUST COMPUTING
INFRASTRUCTURE AND WE HAVE
PUBLISHED A LOT OF THIS WORK
ALONG THE WAY.
FORTUNATELY I DON'T NEED TO TALK
ABOUT BINDING FREE ENERGY
METHODS.
THEY DESCRIBED THE IMPORTANCE OF
THESE IN THE FREE ENERGY
CONSORTIUM IS EXCELLENT AND
WE'RE STRONG SUPPORTERS OF THE
OPEN SCIENCE MOVEMENT IN GENERAL
AS I'LL TALK ABOUT BUT FOR FREE
ENERGY CALCULATION WE BUILT OUR
OWN INTERNAL INFRASTRUCTURE AND
BUILD ON TOOLS ARE OUT THERE
WHEN THEY ARE EXIST FOR EXAMPLE
STARTING WITH FLOW MAP TOOL FOR
CERTAIN PIECES OF IT.
WE HAD TO BUILD A LOT OF STUFF
ON OUR OWN AND INTEGRATE THIS.
THE REASON WE'VE DONE THIS IS WE
CAN RUN AT SCALE WHICH IS SUPER
IMPORTANT FOR ASKING BIG
QUESTIONS ABOUT NOT JUST LOOKING
AT A HANDFUL OF MOLECULE BUT
HUNDREDS OR THOUSANDS ON A
ROUTINE BASIS.
AND ALLOWS US TO CUSTOMIZE THE
TOOLS TO SOLVE OUR PROBLEMS AND
EXPLAIN WHY THAT WAS IMPORTANT.
WE BUILT OUT OTHER FUNCTIONALITY
THAT IS ABOUT ENABLING HUMANS.
WE DON'T JUST WANT TO PRODUCE A
NUMBER BUT ADDITIONAL INSIGHT AS
WELL.
WE'VE CREATED THIS FREE ENERGY
COMPOSITION WHERE YOU CAN
EXTRACT FROM THE FREE ENERGY
SIMULATIONS ATOMIC
CONTRIBUTIONS.
THIS IS NOT RIGOROUS BUT USEFUL.
YOU CAN'T BREAK UP FREE ENERGIES
IN TO ATOMIC CONTRIBUTIONS WITH
ENTROPY IN THERE AND THE
MOLECULE COULD BE GOOD BY
PREDICTIVE MINUS AND THE THINGS
YOU MIGHT NOT WANT TO MESS WITH
AND OTHER PARTS OF THE LIGAND
THAT CHSHOULD BE CHANGED AND WH
IS UNHAPPY TO LEAD TO ADDITIONAL
DESIGN INSIGHT.
AND IN ADDITION TO BINDING FREE
ENERGY CALCULATION WE DEVELOPED
A LOT IN THE CONTEXT OF WHAT WE
CALL BIO LOGICALLY RELEVANT
PROTEIN MOTION.
NOT BRUT FORCE SIMULATIONS
WATCHING BINDS FOLD AND UNFOLD
AND THE RESEARCH GROUP HAS DONE
AN EXCELLENT JOB BUILDING A
COMPUTER TO RUN SIMULATIONS AT
THE SCALE AND MOST DON'T HAVE
THAT KIND OF COMPUTE CAPABILITY
AND WE NEED TO THINK MORE
CAREFULLY ABOUT THE PROBLEM
WE'RE TRYING TO SOLVE AND IN THE
CONTEXT OF OUR WORK AND DRUG
DISCOVERY THAT COMES DOWN TO
SAMPLING OR VARIOUS SAMPLING
PROTOCOLS TO INVESTIGATE
SPECIFIC DEGREES OF FREEDOM IN
THE CONTEXT OF WHAT MATTERS IN
BIOLOGY AND THIS PARTICULAR WORK
HERE DESCRIBE SOME OF THE TOOLS
WE'VE DEVELOPED ALONG THAT
PROFESSION.
THE COMPUTATION IS NOT ENOUGH.
WE CAN'T SIMULATE EVERYTHING AT
TIME SCALE AND RESOLUTION WE
NEED.
AND SO WE ALSO INTEGRATE BIO
PHYSICAL DATA AND DEVELOPED A
FRAMEWORK TO INTEGRATE DATA NOT
ATOMIC RESOLUTION BUT BRING IN
THAT ALLOWS US TO CREATE MORE
ACCURATE AND FASTER SIMULATIONS
AROUND WHAT MATTERS AND WHAT'S
HAPPENING IN THE EXPERIMENT.
FOR EXAMPLE MASS SPEC IS A
POWERFUL TOOL TO UNDERSTAND WHAT
PARTS OF A PROTEIN ARE GETTING
BLOCKED FROM SOLVENT UPON LIGAND
BINDING AND DOESN'T GIVE YOU
INFORMATION WHERE THE ATOMS ARE
JUST WHICH RESIDUES ARE BLOCKED
AND INTEGRATE THAT AS AN
ADDITIONAL FACTOR AND THE MD
SIMULATIONS AND THAT ALLOWS US
TO GET SIMULATIONS THAT ARE
CONSISTENT WITH EXPERIMENT.
THEY'RE NOT CONSTRAINED ONLY TO
THE EXPERIMENT BUT BIASSING THE
SIMULATIONS IN AN INTELLIGENT
WAY AND WE LOOKED AT MOLECULES
BRINGING TWO PROTEINS TOGETHER
AND THESE TEND TO BE PRETTY
FLEXIBLE AND WERE ABLE TO USE
THIS APPROACH WITH DYNAMICS AND
MASS SPEC TO GET ACCURATE
MOLECULAR REPRESENTATION
ESSENTIALLY ATOMIC CRYSTAL
RESOLUTION WITH FULL DYNAMICS
AND ABLE TO EXPLAIN PREVIOUSLY
UNEXPLAINABLE DATA ABOUT
SPECIFIC LINKERS AND HOW THOSE
ARE INFLUENTIAL IN THE
DEGRADATION PROFESSION.
-- PROCESS.
IT'S IMPORTANT AGAIN TO BRING
THIS BACK TO PEOPLE.
WE DEVELOPED AN INTEGRATIVE
DESIGN PROCESS WHERE WE START
AND END WITH HUMANS LOOKING AT
THE FREE ENERGY DECOMPOSITION
WORK I SHOWED AND OTHERS AND
ENUMERATE LARGE SPACES AND
WRITING OUT CHEMICAL REACTIONS
WITH KNOWN BUILDING BLOCKS TO
EXPLORE CHEMICAL SPACE AND LOOK
AT PREDICTIONS AND GETTING INTO
MOLECULAR DYNAMICS AND FREE
ENERGY CALCULATIONS AND ALL THIS
BEHIND THE SCENES GETS PRESENTED
TO PEOPLE ON THE TEAM THROUGH
THE DATA PLATFORM WE'VE
DEVELOPED SPECIFICALLY FOR THE
PURPOSE OF INTEGRATING HUMANS
AND COMPUTERS IN A WAY I THING
IS QUITE NOVEL AND USEFUL SO THE
RESULTS THAT COME OUT OF HERE
EVERYBODY ON THE TEAM GETS A
LINK AND CAN GO IN THE INTERFACE
AND CAN LOOK AT THE
COMPUTATIONAL DATA IN MANY
REPRESENTATIONS AND MAKE
DECISIONS AND PROVIDE OPINIONS
IT'S ALL COLLECTED SO WE'RE
DECIDING WHICH MOLECULES IT MAKE
AND CAPTURED EVERYBODY'S
FEEDBACK FROM BIOLOGY,
CHEMISTRY, BIO PHYSICS AND
COMPUTATION.
WE CAN'T CO -- DO THIS ALL
ALONE.
WE HAVE GREAT PEOPLE THAT HAVE
BEEN INVOLVED AND A LOT OF
THANKS TO THESE FOLKS AND THE
WORK THEY'RE DOING AS WELL AS
OTHER OPEN SCIENCE CONTRIBUTORS
NOT OUT THERE AND WE'RE ALSO
DOING OUR BIT ON THAT SIDE AND
DEVELOPING A NEW SIMULATION
ENGINE MADE TO BE MORE
PERFORMANT ON MODERN SIMULATIONS
WITH 50,000 ATOM DOESN'T
SATURATE A CPU AND THESE GROWING
SO FAST WE HAVE TO THINK MORE
CREATIVELY HOW TO LEVERAGE THEM
MAX -- MAXIMAL AND THIS IS OPEN
SOURCE UNDER THE MIT LICENSE.
SO WITH THAT AS BACKGROUND I
WANT TO JUMP INTO THE STORY FROM
OUR SILICON THERAPEUTICS DAYS
THE DESIGN OF A SYSTEMICALLY
AVAILABLE SMALL MOLECULE STING
AGONIST.
SO STING INTERFERON JOINS IS A
REGULATOR IN THE IMMUNE SYSTEM
AND IN RED.
IT GOES UNDER RAY LARGE SCALE
CONFIRMATIONAL CHANGE FROM THE
BLUE TO RED STRUCTURE WHEN I
BINDS AND THIS IS WHAT ACTIVATES
THE INNATE IMMUNE PATHWAY AND WE
DEVELOPED AN ANALOG TO DESIGN A
MOLECULE THAT'S SYSTEMICALLY
AVAILABLE.
WE HAD A NUMBER OF COMPANIES AND
TOOK NUCLEOTIDE AND IMPROVED THE
PROPERTIES AND THEY'RE LARGE AND
POLAR AND GET CLEARED OUT OF THE
BODY QUICKLY AND NON-DRUG LIKE
AND THESE ONLY SLIGHTLY IMPROVED
THE PROPERTIES BUT NOTHING TO BE
SYSTEMICALLY DELIVERED AND OUR
FEELING WAS A TRUE SMALL
MOLECULE COULD MAKE AN IMPACT
HERE.
I'LL SHOW A QUICK VIGNETTE AND
ANIMATION THAT COMBINES
DIFFERENT PARTS AND BREAK IT
DOWN TO THE INDIVIDUAL PIECES.
FIRST WE'RE RUNNING AT
STIMULATION AND I'LL PEEL BACK
THE WATER LOOKING AT THE STING
PROTEIN.
WE'LL SEE THE CONFIRMATIONAL
CHANGE FROM THE ACTIVE OPEN
CONFIRMATION TO WHEN A
NUCLEOTIDE IS BOUND FROM
INACTIVE TO ACTIVE.
WE WANT TO HAVE MOLECULES THAT
BIND TO THE ACTIVE CONFIRMATION.
WE DID THIS BY DESIGNING A SMALL
MOLECULE THAT DIMERIZES ON
ITSELF IN THE CONTEXT OF THE
BINDING SITE.
IT'S WHY SO MANY UNIQUE TOOLS
NEEDED TO BE DEVELOPED.
THIS IS INITIALLY AN IDEA.
THEY DIDN'T HAVE DETECTABLE
BINDING AFFINITY BUT BY
ANALYZING HIGH ENERGY WATER WE
WERE ABLE TO BUILD OUT INITIAL
POTENCY.
WE THEN STACK THE MOLECULE ON
ITSELF.
AGAIN IT'S A NOVEL MECHANISM.
AND WE WERE ABLE TO GET CRYSTAL
STRUCTURES AND GO THROUGH
QUANTUM MECHANICAL CALCULATION
AND GET INTO FREE BINDING ENERGY
CALCULATION USING WATER THERMAL
DYNAMICS TO GUIDE WHERE WE
WANTED TO GO AND SYNTHESIZABLE
LIBRARIES AND WE IDENTIFIED SOME
INTERESTING UNIQUE CONFIRMATION
IN ORANGE IS A CON FIRM OF THE
PROTEIN THAT LED TO A TIGHT
BINDING MOLECULE BUT DIDN'T HAVE
A STRONG PATHWAY STIMULATION.
WE HAD TO STEP BACK AND DESIGN
MOLECULES THAT BLOCK THE PROTEIN
INTO A HIGHLY ACTIVE CONFIRM AND
ONCE WE WERE ABLE TO IDENTIFY
THAT CHEMICAL SERIES WE THEN
FOCUSSED ON OPTIMIZING THE
PROPERTIES TO BRING THAT
MOLECULE FROM A LEAD TO A DRUG
AND THIS IS CURRENTLY IN THE
CLINIC.
WE STARTED FROM AN IDEA INTEREST
FROM THE MOUSE STEAM PROTEIN AND
LAD NO DETECTABLE BINDING
AFFINITY OR ACTIVITY TO HUMAN
STING.
WE STARTED DOING MODELLING AND
CAME UP WITH THE IDEA AND
OPTIMIZED THE MOLECULE.
WE WENT THROUGH VARIOUS STAGES
OF LOOKING AT THE WATER THERMAL
DYNAMICS AND FREE ENERGY
CALCULATION.
ALL REQUIRED CUSTOMIZED TOOLS TO
BE DEVELOPED.
THERE'S NOTHING OUT OF THE BOX
THAT ALLOWS YOU TO LOOK AT
MOLECULES BINDING AS A DIMER TO
EACH OTHER AND INTERACTING WITH
THE PROTEIN.
A VALUE OF OPEN TOOLS,
CUSTOMIZABLE TO HAVE A TEAM TO
GET IN THERE AND MAKE
MODIFICATIONS AS NEEDED.
THE FIRST WAS THE WATER THERMAL
DYNAMICS.
GREAT TOOLS OUT THERE AND WE
WORKED WITH THE DEVELOPER OF
WATER MAP AND THERE'S A GREAT
TOOL OUT THERE THAT GIVES YOU
PICTURES LIKE THIS AND WITH THE
INITIAL HYPOTHESIS OF WHERE THIS
WAS IT'S EASY TO LOOK AT A
PICTURE AND SAY IF WE COULD JUST
GET OUT THERE WE COULD GAIN
POTENCY AND THAT ENDED UP
WORKING AT THE HIGHEST ENERGY
WATER PATCHES.
AND ONCE BEGOT THE INITIAL
DETECTABLE BINDERS WE GOT INTO
THE STACKING INTERACTION.
EVEN WITH SMALL HALOGEN AND 1 TO
2 ATOM CHANGES THERE'S
COMBINATIONS ONE CAN DO WITH THE
EIGHT POTENTIAL SITES OF
CHEMISTRY.
WE ENUMERATED THE IDEAS AND RAN
THIS FUNCTIONAL SYMMETRY THEORY
AND WORKED WITH TODD MARTINEZ
WITH STANFORD THE DEVELOPER OF A
FAST QUANTUM MECHANICAL TOOL
THAT RUNS ON GPUs AND IN
COLLABORATION WERE ABLE TO VFRL
THE TOOL IN REAL TIME FROM THE
IDENTIFICATION OF THE NEED AND
WE HAD A TOOL TO COMPUTE THE
STRENGTH OF THE STACKING
INTERACTION AND THAT CORRELATES
WELL WITH THE ACTIVITY WE WERE
LOOKING TO BUILD AND WERE ABLE
TO EXPLORE THOUSANDS OF
MOLECULES VIRTUALLY AND
SYNTHESIZE ONLY 10 TO COME UP
WITH A MOLECULE MORE POTENT THAN
WHERE WE WERE AND IMPROVING
PROPERTIES WHILE MAINTAINING AND
IMPROVING BINDING AFFINITY.
THIS IS A DISTRIBUTION YOU'RE
LOOKING AT ON THE RIGHT WHICH IS
ALL THE 200 OR SO COMPOUNDS OVER
THE COURSE OF THE PROJECT.
I LIKE TO SHOW THE CORRELATION
PLOTS AND RMS ERRORS AND THIS IS
WHAT YOU'D WANT TO SEE AT VEGAS
MAKING A BET ON SOMETHING.
THERE'S A PREDICTIVE VALUE AND
MOLECULES WE PREDICT IN THE
FLUID DISTRIBUTION AND END UP
BEING BETTER AND SELECT FROM A
POOL OF MOLECULES WE'D PREFER
THIS BLUE DISTRIBUTE UKS THAN
AVERAGE DISTRIBUTION AND
MOLECULES WE PREDICT TO BE BAD
BUT ENDED UP SYNTHESIZING AND
FALSE NEGATIVES.
WE WERE PROBABLY TRYING TO
IMPROVE OTHER PROPERTIES BEYOND
THE BINDING AND MOLECULE WE
PREDICTED TO BE FALSE POSITIVES
BUT IN GENERAL HIGHLY PREDICTIVE
AND USEFUL AND SPECIFIC EXAMPLES
OF THE TYPES OF PERTURBATIONS WE
WERE MAKING AND THE EXPERIMENTAL
BINDING ENERGY VERSUS OUR
ENERGIES.
FINDINGS AREN'T ALWAYS WHAT
MATTERS.
WE WERE LOOKING FOR
STABILIZATION OF A GOOD
CONFIRMATION AND EXPERIMENTALLY
IN THE LAB BINDING AFFINITY
DIDN'T CORRELATE WITH CELL
ACTIVITY AND CAME UP WITH A
STABILIZATION ASSAY THAT
CORRELATED WITH WHAT WE CARED
ABOUT CELLULAR ACTIVITY.
AND TO MENTION IT'S IMPORTANT TO
KNOW WHICH YOU'RE GOING AFTER
AND WHAT YOUR VARIOUS MODELS ARE
GOING TO BE THROUGHOUT THE
PROCESS.
ONE PROBLEM WITH THE EARLIER
MOLECULE IS YOU COULD SEE IT
STABILIZES THE MOUSE VERSION BUT
HAD NO ACTIVITY ON HUMAN.
IN THE PROCESS WE LOOKED AT THE
HUMAN WILD TYPE ALLELE AND THE
PRECLINICAL MODELS.
WE WANT TO MAKE SURE THEY'LL
WORK IN MOUSE MODELS.
THE REASON IT MATTERS IS THE
MAIN CONCERN IS ON TARGET TALKS
CYTOKINE STORM.
WE WANTED TO MAKE SURE IT WAS
BINDING AND DOING WHAT IT WAS
DOING ACROSS THE RELEVANT
SPECIES.
THROUGH THE PROJECT WE WERE ABLE
TO DESIGN A POTENT AND SELECTIVE
SMALL MOLECULE AGONIST THAT
WORKED ON HHQ AND OTHER VARIANTS
MORE POTENT AND THE ANALOGS AND
SINGLE DOSE RESULTED IN A
MEDIATED MECHANISM.
THE GOAL ISN'T TO INHIBIT THE
PROTEIN BUT TURN THE BODY ON AND
DO THE REST AND YOU SEE THE
TREATED AND UNTREATED POPULATION
AND SINGLE DOSE COMPLETED IN
COMPLETE TUMOR ELIMINATION.
MORE INTERESTINGLY THE PERFECTLY
HEALTHY MICE WERE RE-INOCULATED
WITH THE TUMOR AND WHILE IT GREW
IN NAIVE MICE THE PREVIOUSLY
TREATED MICE WITHOUT ADDITIONAL
TREATMENT DIDN'T GROW.
THERE WAS INNATE LEARNING IN THE
PROCESS.
THIS IS A SUMMARY OF THE TOOLS I
COVERED AND USED BUT THE MAIN
CONCLUSION IS WE EXPLORED
MILLIONS VIRTUALLY AND APPLIED
CUSTOM TOOLS IN THE PROJECT
BECAUSE OF THE UNIQUE PROBLEMS
OF THE CHAIN AND BACK TO THE
CLINICAL MOLECULE.
DRUG DISCOVERY IS HARD FOR
DIFFERENT REASONS AND BLACK
BOXES ARE NOT THERE FOR DRUG
DISCOVERY.
WE NEED BESPOKE SOLUTIONS TO
SOLVE PROBLEMS AS THEY ARISE.
WE DEVELOPED A TOOL KIT APPROACH
TO APPLY THE RIGHT TOOL AT THE
RIGHT TIME.
COME PREPARED OR ANYTHING AND
EVERYTHING AND HAVE NOT JUST THE
TOOLS BUT PEOPLE TO DO
ADDITIONAL DEVELOPMENT WITH THE
TOOLS AS NECESSARY.
THE FUTURE HOLDS GREAT PROMISE
FOR A.I.
THERE'S GREAT STUFF COMING OUT
BUT WE LOVE IN THE PRESENT AND
HAVE TO USE WHAT'S THERE.
WE'RE INVESTING IN BOTH
DIRECTIONS AND HAPPY WITH THE
DIRECTION THINGS ARE MOVING IN
THE FIELD.
VERY IMPORTANTLY COMPUTERS ALONE
CAN'T INVOLVE THE PROBLEM.
IT'S THE INTEGRATION OF THE LAB
AND IN SILICO APPROACHES AND
APPROPRIATE PEOPLE TO MAKE THE
DECISIONS.
I'D LIKE TO THANK EVERYBODY AND
HAPPY TO ANSWER QUESTIONS.
>> THANK YOU FOR THE
PRESENTATION.
WE'LL TAKE A COUPLE QUESTIONS.
WHAT MADE THE DIMERIZATION AND
STRUCTURE PROCESS?
>> AS THE THROUGH STACKING
INTERACTION AND THROUGH TO
ENTROPY AND CONCENTRATIONS IT'S
NOT OBSERVED.
THERE'S ANOTHER GROUP THAT
DEMONSTRATED SOME WEAK
AGGREGATION AND THE COMBINATION
OF THE BOUNDING SITE AND FIT
COUPLED WITH THAT INTERACTION
DRIVING THINGS TOWARDS THAT
THERMAL DYNAMIC STATE AND
PROTEIN PLUS THE MOLECULES
TOGETHER.
LIKELY THERE'S FAVORABLE
INTERACTION AND SOLUTION.
WE'RE MEASURING ON THUR -- THUR
OWN WHAT IS THE FAVORABLE
INTERACTION AND CONCENTRATION
AND SOLUTION WITH ENTROPY YOU
WON'T SEE THE DIMER.
THE MOLECULES LOOK AND BEHAVE
LIKE MONODISPERSED TRUE SMALL
MOLECULES WITH SOLUBILITY AND
PERMEABILITY OF A SMALL MOLECULE
AND WITH A POPULATION DIMERIZED
AND STABILIZING THE ACTIVE
CONFIRMATION OF STING.
>> YOU ARE USING YOUR OWN ENGINE
AS FAR AS I UNDERSTOOD.
WHAT FORCE FIELD DO YOU USE HAVE
YOU TRIED OR PLAN TO TRY
CREATING YOUR OWN ML BASED FORCE
FIELD ALONG THE LINES JOHN
OUTLINED EARLIER.
>> WE ARE VERY MUCH KEEPING WITH
THE FIELD THROUGH OUR
DEVELOPMENT AND WORKING WITH
WHAT'S OUT THERE.
INTERNALLY WE DEVELOPED AN
INFRASTRUCTURE THAT DOES BESPOKE
DIMERIZATION AND PORTION SCANS
FROM MECHANICAL FITTING AND THE
STATE OF APPROACH AND OPEN FORCE
FIELD WASN'T A THING WHEN WE
STARTED THIS AND WE NOW USE IT
QUITE REGULARLY AND THERE'S
PROMISE THERE.
>> THANK YOU.
WE'LL CONTINUE ONE THE TOPIC
TOMORROW WITH THE SPEAKERS.
AND I'LL HAND IT OVER TO SARINE
FOR FINAL COMMENTS.
>> THANK YOU SO MUCH AND THANK
YOU FOR THE SPEAKERS AS WELL AS
ALL THE SPEAKERS TODAY ALL
SESSION CHAIRS AND EVERYONE
ELSE.
IT WAS PHENOMENAL.
THIS WAS AN AWESOME DAY.
I'D LIKE TO SHARE THE AGENDA FOR
TOMORROW QUICKLY AGAIN TO REMIND
YOU ALL ABOUT THE EXCITING TALKS
WE'RE GOING HAVE TOMORROW.
PLEASE COME BACK TOMORROW.
WE HAVE EXCITING SESSIONS.
WE'RE GOING TO CONTINUE SESSION
3 ON APPLICATIONS OF METHOD
METHODOLOGIES AND DRUG
DISCOVERY.
WE'LL HAVE A SESSION FOCUSES ON
EMERGING TRENDS THAT WILL HAVE A
NEWLY MINTED NOBEL LAUREATE
SPEAK AT 2:00 IN THE AFTERNOON
AND THEN RIGHT AFTER WE'LL HAVE
A PANEL DISCUSSION THAT IS GOING
TO COVER BRIDGING THE GAPS IN
TRANSLATION WHERE WE'RE GOING TO
HAVE AWESOME DISCUSSIONS AND
HAVE A LOT OF TIME AFTER THE
SHORT TALKS OF THE PANELISTS TO
DISCUSS MORE.
IF WE DIDN'T HIT ANY OF YOUR
QUESTIONS OR MISSED ANYTHING
THIS IS THE TIME TO COME BACK
AND ASK YOUR QUESTIONS.
I'D ALSO LIKE TO THANK THE
ATTENDEES.
I KNOW IT'S LATE IN THE NIGHT OR
SUPER EARLY IN THE MORNING.
WE APPRECIATE YOU BEING HERE
WITH US AND YOUR EAGERNESS TO
LEARN.
I'D LIKE TO CLOSE TODAY'S
SESSION AND SEE YOU BACK
TOMORROW 11:00 EASTERN TIME.
THANK YOU EVERYONE.
BYE, GOOD EVENING.