AI for Bio has a Fuzzy API problem
The hype around AI in biology overlooks the fundamental mismatch between software's clean APIs and drug discovery's fuzzy feedback loops, which makes machine learning uniquely challenging in this domain.
“AI for bio” is getting hot again. Given the excitement in the current moment, I thought I’d share a bit about what actually makes biology uniquely hard as an application domain for machine learning. The reason is not simply that biology is complicated, though it obviously is. ML is good at many things that are complicated. The deeper reason is that drug discovery does not have the kind of clean feedback loops and clean interfaces that made modern ML so powerful elsewhere.
In software, we are used to clean APIs. One team can build a backend service, expose an endpoint, and another team can build on top of it. The interface is typed. The object either satisfies the contract or it does not. If something breaks, you can usually trace the failure to a bug, fix the code, rerun the test, and ship again. This is so much the case that billion dollar companies are regularly built satisfying exactly one interface (e.g. Supabase for databases, Exa for search, NVIDIA for GPU compute).
It is tempting to imagine drug discovery the same way:
target = target_discovery(disease) drug = drug_design(target) medicine = clinical_trial(drug)