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Briefings and ideas about AI drug discovery from AI podcast discussions and the research they cite, with links to the original sources.
Elon Musk says spending cuts cannot fix the U.S. national debt and only AI and robots can. On Moonshots, Chamath Palihapitiya and the hosts offered competing forecasts of how, from cheaper health care to automated defense spending.
Biohub science head Alex Rives argues that no lab animal can ever be a perfect stand-in for people. He says an AI model trained across many species, as protein models were trained across evolution, could predict human biology far better.
Biohub's head of science, Alex Rives, says virtual cell models fall short above all because biology can't yet measure what matters. He expects an information-level cell model within about five years.
Alexander Wissner-Gross forecast that virtual-cell AI will propose small-molecule cures for the top 5,000 diseases within two to three years. He said regulatory approval would be a harder, policy-dependent question.
Leash Bio co-founder Ian Quigley says messy DNA-encoded library screens can yield reproducible AI training data, using dozens of barcodes per molecule and hundreds of screens per library. He says absolute binding strength is still out of reach.
Ian Quigley says co-folding models still place a molecule where a pocket was digitally destroyed, so Leash Bio engineers protein mutants for negative data. In a 20-protein exploratory analysis, its model's attention spread more evenly after retraining on mutants plus about 40% more ordinary data.
In Leash Bio's 2024 Kaggle contest, no team beat chance on an unseen chemical library. Ian Quigley says Leash's later Hermes model roughly matched Boltz on public tests without training on outside data, but published results were mixed.
Leash Bio co-founder Ian Quigley says a model given only guesses of who made each molecule, plus the target protein's identity, predicted public ChEMBL activity data nearly as well as one using structure plus the same protein. His team's preprint adds that those guesses still carry chemistry.
Ian Quigley of Leash Bio says AI agents querying its premeasured binding screens can search for strong binders within 100 picks, and argues this resists memorization better than scaling its Hermes model. Leash released a version, Minos, October 14.
Biohub's Sal Candido and Google DeepMind's Pushmeet Kohli argued on a panel that scaling laws in biology must be found, not assumed, and that the problem should decide whether to build better models or gather new data.
A preprint credits an unreleased Claude model with the seed of a 3SUM algorithm just below quadratic time. On Moonshots, the result was cited in an argument that labs' internal models are pulling away from open ones.
Richard Socher, author of The Eureka Machine, argues AI can reconnect fragmented science and that biology gains most. He expects some single-gene diseases cured within 12 months, but says FDA delays mean five to 10 years for cures at scale.