Maurizio Morri – Science Blog

In the last couple of weeks, the most interesting shift in biology focused AI has not been a better single structure predictor. It is the jump from predicting shapes to predicting interactions and designing the parts that create them. A Nature report described a new proprietary drug discovery model from Isomorphic Labs that impressed researchers because it appears to predict how drug sized molecules interact with protein targets at a level people compare to a hypothetical next generation AlphaFold, but now aimed at binding, selectivity, and chemistry relevant signals rather than only static structure. (Nature)

The important technical point is that biology is not only geometry. Drug action is about ensembles, pockets that breathe, water and ions, and the coupling between protein motion and ligand chemistry. If a model can learn interaction landscapes well enough to propose molecules that survive real world constraints, then the bottleneck shifts from “can we model a protein” to “can we close the loop from target to candidate with fewer wet lab cycles”. That is why pharma partnerships keep clustering around models that explicitly predict binding and other interaction level properties, not just sequence to structure. (Reuters)

In parallel, Nature also highlighted how generative biology tools are moving up the abstraction ladder toward designing biological components more directly, including higher level assemblies and genomes, with the same pattern: you get value when the model is constrained by what can actually function inside cells and what can actually be built. The takeaway is that the frontier is becoming system level. The winning models will not just output a plausible sequence. They will output a design that fits a manufacturable path, a measurable assay, and a safety envelope. (Nature)

Sources

https://www.nature.com/articles/d41586-026-00365-7
https://www.reuters.com/business/healthcare-pharmaceuticals/takeda-deepens-ai-drug-discovery-push-with-17-billion-iambic-deal-2026-02-09/
https://www.nature.com/articles/d41586-026-00566-0