NIH VideoCast - Judith H. Greenberg Early Career Investigator Lecture: Artificial Intelligence Approaches for Antibiotic Discovery

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Judith H. Greenberg Early Career Investigator Lecture: Artificial Intelligence Approaches for Antibiotic Discovery

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Air date:	Wednesday, September 28, 2022, 1:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 191 (112 Live, 79 On-demand)
Category:	Special
Runtime:	00:55:50
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	Innovations in artificial intelligence may help to replenish our arsenal of effective drugs, such as those to treat antibiotic-resistant bacterial infections. Since their discovery in the early 1900s, antibiotic medicines have saved countless lives, but their extensive use has resulted in some bacteria developing resistance to them—meaning our current antibiotics no longer work to treat some infections. To effectively probe antibiotic design and discovery using computational approaches, researchers must first program computers to sort through the many characteristics of molecules and determine which properties optimize antimicrobial activity. Dr. César de la Fuente has trained a computer to select for molecular structures that interact with bacterial membranes, leading to artificial antimicrobials that kill bacteria both in vitro and in research organisms. Additionally, his lab has applied pattern recognition algorithms to mine the human proteome, identifying thousands of possible antibiotics encoded in proteins throughout the human body. His research group has also used computational tools to successfully reprogram venoms into potential novel antimicrobials. For more information go to https://nigms.nih.gov/News/meetings/Pages/2022-nigms-directors-early-career-investigator-lecture.aspx

NLM Title:	Artificial intelligence approaches for antibiotic discovery / César de la Fuente.
Author:	De la Fuente, César. National Institute of General Medical Sciences (U.S.),
Publisher:
Abstract:	(CIT): Innovations in artificial intelligence may help to replenish our arsenal of effective drugs, such as those to treat antibiotic-resistant bacterial infections. Since their discovery in the early 1900s, antibiotic medicines have saved countless lives, but their extensive use has resulted in some bacteria developing resistance to them--meaning our current antibiotics no longer work to treat some infections. To effectively probe antibiotic design and discovery using computational approaches, researchers must first program computers to sort through the many characteristics of molecules and determine which properties optimize antimicrobial activity. Dr. Cesar de la Fuente has trained a computer to select for molecular structures that interact with bacterial membranes, leading to artificial antimicrobials that kill bacteria both in vitro and in research organisms. Additionally, his lab has applied pattern recognition algorithms to mine the human proteome, identifying thousands of possible antibiotics encoded in proteins throughout the human body. His research group has also used computational tools to successfully reprogram venoms into potential novel antimicrobials.
Subjects:	Anti-Bacterial Agents Artificial Intelligence Data Science Drug Discovery Drug Resistance, Bacterial--physiology
Publication Types:	Lecture Webcast

NLM Classification:	QV 350
NLM ID:	9918503179406676
CIT Live ID:	45918
Permanent link:	https://videocast.nih.gov/watch=45918

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