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
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
Chai-2: The AI Model Accelerating Drug Discovery with Chai Discovery Co-Founders Jack Dent and Joshua Meier
AI has already fueled breakthroughs in biotechnology—but now, further advances in AI are poised to fuel pharmaceutical discoveries as well. Sarah Guo sits down with Joshua Meier and Jack Dent, co-founders of Chai Discovery, whose newly launched Chai-2 designs bespoke antibodies that bind to their targets at a jaw-dropping 20% rate. Jack and Joshua talk about the implications for Chai-2’s success rate at discovering antibodies for the pharmaceutical industry, how structure prediction is pivotal in making the model work, and future potential for using the model to optimize other molecular properties. Plus, they talk about what they believe bioscientists should be learning to best utilize Chai-2’s technology.
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Chapters:
00:00 – Joshua Meier and Jack Dent Introduction
01:09 – Genesis of Chai Discovery
06:12 – Chai-2 Model
10:13 – Criteria for Specifying Targets for Chai-2
13:12 – How the Chai-2 Model Works
16:12 – Emergent Vocabulary from Chai-2
18:15 – Hopes for Chai-2’s Impact
20:33 – Reception of the Chai-2 Model
22:16 – Future of Wet Lab Screening and Biotech
27:08 – Optimizing Other Molecule Properties
31:37 – Where Chai Invests From Here
36:20 – What Bioscientists Should Learn for Chai-2
40:23 – How Jack and Josh Oriented to the Biotech Space
43:38 – Platform Investment and Chai-2
46:53 – Scaling Chai Discovery
48:21 – Hiring at Chai Discovery
49:09 – Conclusion
Learning the language of life
AI is discovering new drugs. Sound like science fiction? Not at Absci! Sean and Joshua join us to discuss their AI-driven pipeline for drug discovery. We discuss the tech along with how it might change how we think about healthcare at the most fundamental level.
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Featuring:
Sean McClain – LinkedIn, X
Joshua Meier – GitHub, LinkedIn, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
View Absci’s AI Lead Scientist Joshua Meier’s presentation at NVIDIA GTC 2022
Learn more about Absci’s machine learning breakthroughs presented at GTC
Learn more about Absci AI Research (AAIR) Lab
View career opportunities at Absci
Absci AI drug discovery technology
Watch the Truist AI Symposium Podcast to get help demystifying the use of AI in drug discovery
Absci website
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