Addressing a Data Problem to Enable AI-Drug Discovery
About this episode
David Younger discusses the challenges of data availability in AI-driven drug discovery and how A-Alpha Bio's AlphaSeq platform addresses these issues.
AI’s promise in drug discovery is being held back by a simple but stubborn problem: the field doesn’t have enough of the right kind of lab-generated data to train good models. Public databases and scattered studies give a patchy view of how proteins interact, mostly showing successful interactions and using different methods and conditions, which makes it hard for AI systems to learn broad rules or understand what doesn’t work. A-Alpha Bio tackles this data gap with AlphaSeq, a lab platform that can test around a million protein pairs in one experiment under the same conditions, producing rich, consistent information on both hits and misses that’s well suited for AI. David Younger, co-founder and CEO of A-Alpha Bio, talks about why far more lab data and infrastructure will be needed than most people expect, why current public datasets fall short, and why the company’s business model is built around providing data and services rather than developing its own drugs.
People in this episode
Guest: David Younger
Topics covered
- AI in drug discovery
- lab-generated data
- protein interactions
- data infrastructure
- business model
Keywords
- AI
- drug discovery
- lab data
- protein pairs
- AlphaSeq
- data gap
- business model
Mentioned in this episode
Organizations: A-Alpha Bio
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