Virtual Cell Models, Tahoe-100 and Data for AI-in-Bio with Vevo Therapeutics and the Arc Institute

On this week’s episode of No Priors, Sarah Guo is joined by leading members of the teams at Vevo Therapeutics and the Arc Institute – Nima Alidoust, CEO/Co-Founder at Vevo Therapeutics; Johnny Yu, CSO/Co-Founder at Vevo Therapeutics; Patrick Hsu, CEO/Co-Founder at Arc Institute; Dave Burke, CTO at Arc Institute; and Hani Goodarzi, Core Investigator at Arc Institute. Predicting protein structure (AlphaFold 3, Chai-1, Evo 2) was a big AI/biology breakthrough. The next big leap is modeling entire human cells—how they behave in disease, or how they respond to new therapeutics. The same way LLMs needed enormous text corpora to become truly powerful, Virtual Cell Models need massive, high-quality cellular datasets to train on. In this episode, the teams discuss the groundbreaking release of the Tahoe-100M single cell dataset, Arc Atlas, and how these advancements could transform drug discovery. Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @Nalidoust | @IAmJohnnyYu | @PDHsh | @Davey_Burke | @Genophoria Download the Tahoe Dataset Show Notes: 0:00 Introduction 1:40 Significance of Tahoe-100M dataset 4:22 Where we are with virtual cell models and protein language models 10:26 Significance of perturbational data 17:39 Challenges and innovations in data collection 24:42 Open sourcing and community collaboration 33:51 Predictive ability and importance of virtual cell models 35:27 Drug discovery and virtual cell models 44:27 Platform vs. single hypothesis companies 46:05 Rise of Chinese biotechs 51:36 AI in drug discovery