Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter lesson: scale data, models, and compute, and the model can learn what a hand-built pipeline simply couldn't capture. The results are concrete: Chai-2 pushed de novo antibody design from a sub 0.1% hit rate to 16%, turning a needle-in-a-haystack search into something more like designing a key to fit a lock. Josh argues, counterintuitively, that biology is more verifiable than code, and explains why the goal should be more lab experiments, not fewer. Their bet: a design suite that collapses drug discovery from nine months to nine days, and arms the pharma industry rather than competing with it.

Hosted by Pat Grady and Sonali Singh, Sequoia Capital

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