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47:22EN
Sequoia Capital· 2026年8月4日播客From One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.模型与训练AI for scienceScaling Laws+3
1:29:30EN
Cognitive Revolution "How AI Changes Everything"· 2025年10月14日播客2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics主持 Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.模型与训练AI for scienceDrug Discovery+3
57:52EN
Unsupervised Learning: With Jacob Effron· 2025年8月13日播客They Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.主持 Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.模型与训练AI for scienceComputational Biology+3
49:27EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年7月3日播客 · 2 人对谈From 0.1% to 20%: Chai-2 Turns Antibody Design Into Prompt Engineering主持 Sarah Guo, Elad GilChai Discovery co-founders Joshua Meier and Jack Dent tell Sarah Guo how a generative model now designs antibodies that bind on the first 24-well plate.AI for scienceDrug DiscoveryAI startups & small teams+3
55:40EN
Sequoia Capital· 2025年4月29日播客To Solve All Diseases, Isomorphic Labs Needs a Half-Dozen AlphaFoldsMax Jaderberg on turning DeepMind's game-playing research into a general AI engine for drug design—and why prediction alone cannot search a 10^60 molecular universe.企业与战略AI for scienceSynthetic data+3
57:41EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年2月25日播客 · 2 人对谈A Hundred Million Cells, Four People, Three Days: Biology's ImageNet Moment主持 Sarah Guo, Elad GilVevo Therapeutics and the Arc Institute open-sourced Tahoe-100M and a 230-million-cell atlas in the same week — and argued that virtual cells, not just protein folders, are the next language model of biology.模型与训练AI for scienceData quality & curation+3