Powering AI-Driven Drug Discovery with President of Isomorphic Labs Max Jaderberg

Drug discovery and development has long relied on expensive, time-consuming empirical trial and error. By shifting the discovery process onto computers, Isomorphic Labs wants to fundamentally change medicine from a process of discovery to one of precision engineering. Elad Gil sits down with Isomorphic’s president Max Jaderberg to discuss his vision for using predictive AI models to achieve experimental accuracy. Max details how the success of AlphaFold provided the first proof of concept that a deep learning model could generalize in the field of biology, and how an AI model’s compounding of learnings allows it to achieve generalizability with scale, thus greatly increasing the probability of success as well as speed in real world clinical trials. He also talks about evolving past the need for animal testing through in-silico models, Isomorphic’s focus on developing drug design engines that are agnostic of disease area, target class, and modality, why accurate molecule engineering unlocks the potential for multidrug cocktails that span complex biological pathways, and how he believes AI molecule optimization will improve both human lifespan and healthspan. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MaxJaderberg | @IsomorphicLabs Chapters: 00:00 – Trailer 00:45 – Max Jaderberg Introduction 01:24 – Isomorphic Origins 05:23 – Drug Discovery Stagnation 11:49 – Hiring at Isomorphic 14:35 – Bringing Drug Discovery Onto Computers 19:31 – Isomorphic’s Datasets 20:30 – Rethinking Endpoints 22:50 – External vs. Internal Dependences 23:57 – Automating Biologic Labs 26:22 – Internal Development vs. Partnerships 27:19 – Areas of Human Health Focus 29:21 – Multidrug Approaches 32:17 – Risk Appetite Variation 34:26 – Disease Treatment Outlook 35:42 – Human Longevity and Drug Development 39:16 – Conclusion

GPT-4正在为你翻译摘要中......

翻译全文