# Episode 16: Why Chemistry has Held Drug Discovery Back

Page: https://stenobird.com/podcast/from-models-to-medicine-7769799/episode-16-why-chemistry-has-held-drug-discovery-back
Text version: https://stenobird.com/podcast/from-models-to-medicine-7769799/episode-16-why-chemistry-has-held-drug-discovery-back.md
Podcast: [From Models to Medicine](https://stenobird.com/podcast/from-models-to-medicine-7769799)
Published: 2026-07-15T15:05:00+00:00
Episode link: https://podcasters.spotify.com/pod/show/kami-think-tank/episodes/Episode-16-Why-Chemistry-has-Held-Drug-Discovery-Back-e3k3es8
Audio file: https://anchor.fm/s/110164688/podcast/play/120748360/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-4-30%2Fbf816b1d-afeb-4b19-cb6e-777efda5e1a1.mp3
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JSON: https://stenobird.com/v1/public/podcasts/from-models-to-medicine-7769799/episodes/episode-16-why-chemistry-has-held-drug-discovery-back
Duration seconds: 2255

## Resource

Stan Jastrzębski is the co-founder of molecule.one and a deep learning researcher who made a deliberate pivot into synthetic chemistry. In this episode, he explains why chemistry is the real bottleneck in drug discovery today. Unlike biology, which has the Protein Data Bank and tools like AlphaFold, chemistry lacks the massive, balanced datasets AI needs to work. Scientific literature makes it worse, publishing wins and burying failures, which starves models of exactly the negative data they learn from most. Stan also talks about "vibe binding," the industry's growing tendency to over-rely on biological binding models that only work on well-trodden targets and quietly kill scientific creativity in the process. He closes with a sharp take on where LLMs actually hit their ceiling, and why he thinks scientific discovery is not just a use case for AI but its ultimate test.

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## Transcript

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