How Monte Carlo Tree Search Helps Explain Predictions

Jim Collins describes using Monte Carlo tree search to find overrepresented substructures and reveal novel chemical rationales learned by the models.

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Jim Collins, Termeer Professor at MIT, unveils his AI-powered project that has discovered several new antibiotics, effective against resistant strains and often employing entirely new mechanisms of action. He details how their refined multi-step AI process, even with small datasets and modest compute, can efficiently screen vast chemical spaces to identify promising drug candidates. This breakthrough offers a realistic and affordable path to tackling the staggering antibiotic resistance crisis, which currently claims over a million lives annually. Collins argues this practical application of AI represents a transformative win for humanity, often overlooked amidst the focus on AGI.

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CHAPTERS:

(00:00) About the Episode

(04:30) Introducing Jim Collins

(05:26) Antibiotic Resistance Primer

(14:04) The Antibiotic Market Failure

(18:45) AI Discovers Halicin (Part 1)

(18:51) Sponsors: AssemblyAI Ad 1 | Claude

(22:11) AI Discovers Halicin (Part 2)

(30:58) The Economics of Discovery

(39:10) Inside the AI Architecture (Part 1)