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How is AI reshaping the work of drug development?

In this episode of the Ask AI Podcast, we speak with Eric Sigel, a biologist, computer scientist, and serial founder whose early experiments with machine learning in drug discovery delivered results that even surprised Google.



AI & the work of new drug discovery


AI is changing the future of work in scientific research but not always at the pace or in the ways we expect.


In this episode of the Ask AI Podcast, host Elena Putilina speaks with Eric Sigel, a data scientist and scientific computing leader with over 20 years of experience in biotech. Trained in biology and computer science at Yale Eric has devoted his career to building informatics IT and data science capabilities in small molecule therapeutic focused companies.


He led the development of the scientific computing and data science systems behind X-Chem’s groundbreaking DNA Encoded Library platform one of the first of its kind in the industry. His pioneering application of machine learning to DEL technology paved the way for the launch of ZebiAI a spin off that was acquired by Relay Therapeutics in under a year. At ZebiAI Eric led the data science team driving innovation at the intersection of AI and early stage drug discovery.


Eric is also founding co chair of the Broad Institute Symposium on Machine Learning in Drug Discovery, an annual event that brings together leading researchers and voices in the ML in drug discovery space. He currently heads the advertising and sponsorship subcommittee for the 2024 symposium.


Together they unpack how AI is helping researchers identify new molecules why the lab remains the biggest bottleneck and what scientists need to do to keep pace with AI’s rapid evolution. Spotify Podcasts Apple Podcasts


What you'll hear


  • What happened when Eric’s startup surprised Google with breakthrough results

  • Why AI is powerful but not yet plug-and-play in drug discovery

  • Where predictive models help the most in early-stage research

  • How long timelines and lab bottlenecks shape the future of scientific work

  • Why the scientists who embrace AI will be the ones who thrive


Episode links



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