Chai Discovery's BioAI Phase Shift: Chai-3, Photoshop for Molecules, One-Shot Drug Design, & Engineering Biology latent.space/p/chai-discover… @chaidiscovery cofounder @Mattmcpartlon1 and product lead @realdealpatil explain why AI drug design has crossed the threshold from impressive demos to pharma-trusted tools, how Chai-2 and Chai-3 are pushing toward one-shot therapeutic-grade molecules, how Chai’s CAD-like design suite lets scientists precisely engineer antibodies and binding sites, why faster model-to-lab feedback loops could turn pharma’s waterfall discovery process into an iterative loop, and how better models, validation, compute, and infrastructure are turning biology from trial-and-error science into an engineering discipline.

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. @chaidiscovery is changing the paradigm… describe the molecule you want, and the model designs it. @joshim5 and @Mattmcpartlon1 took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next