Google DeepMind is pivoting from attacking hard biological problems to joining the race for an "AI scientist." This will likely harvest a lot of low-hanging fruit, but I think it will stall our actual understanding of biology. If we think of biological knowledge and drug discovery as a distribution, we currently observe only a slice of it, along only a few dimensions. That is why drug discovery remains largely a matter of luck rather than optimisation over known parameters, and luck is not something LLMs with better reasoning can fix. What we need is new biological data, generated specifically to expose the missing dimensions and the unobserved mass of that distribution. Above all, we need causal data and models with strong inductive biases, AlphaFold2 being the obvious example. Almost everything since has been reaping the fruit AF2 planted. If this resonates with you and you have a strong AI background, come work with us.

This is great news. As you age, sugar binding to your proteins creates stiff, sticky chemical scars that affect skin, arteries, eyes and more. It was considered irreversible and now may be reversible, restoring to a healthy state. Researches did this by using AlphaFold to search 45,000 oxidases, then screened more than 500 million engineered variants through directed evolution. The work is still ex vivo in a lab setting. Delivering a large bacterial enzyme safely into living tissues, with sufficient penetration and bioavailability, remains a major challenge.

AlphaEvolve is now GA on Google Cloud! Co-developed with @GoogleDeepMind, AlphaEvolve is a Gemini-powered evolutionary agent that autonomously designs and discovers highly optimized code to solve your hardest problems and algorithmic bottlenecks across industries: