🔬Biology Is Turning Into Software — Matt McPartlon and Neil Patil, Chai Discovery
This episode features Matt McPartlon and Neil Patil from Chai Discovery, a protein design startup, discussing their AI models (Chai 1, 2, and 3) for protein and antibody design. They elaborate on their unique partnership-driven business model with major pharma companies, the technical challenges of scaling AI for biology, and their vision for transforming drug discovery into a precision engineering discipline. The conversation highlights the shift from traditional methods to AI-driven design, addressing critical bottlenecks in compute, data, and validation.
- Traditional drug discovery follows a "waterfall model" where each stage, from target to optimization, takes months or years and costs a fortune to even try new things, according to Chai Discovery's Neil Patil. Read →
- Neil Patil of Chai Discovery, a protein design startup, identifies "talent obscurity" as the biggest bottleneck for AI in biology, noting top ML talent often gravitates to LLMs or traditional software. Read →
- Chai Discovery acts as a "neutral software factory" for medicines, focusing solely on providing AI models and a product platform rather than developing its own drugs. Read →
- Epitope prediction is "ridiculously hard" for novel targets: Historically, finding the exact spot on a protein an antibody needs to block (the epitope) has been a massive challenge, often relying on trial and error. Read →
- Protein design startups like Chai Discovery face brutal validation loops, where it takes months to know if an AI model's prediction was correct, severely slowing iteration. Read →