AI R&D

Drug discovery is a wonderful place for AI demos. The model proposes a molecule, the molecule looks plausible, a docking score improves, and the slide deck starts to glow with that familiar color: almost-commercial blue. Then the evaluation protocol arrives and ruins the party. The problem is simple, and therefore easy to underestimate. A drug discovery agent is rarely asked to return one impressive molecule. It is asked to return a set of molecules that jointly satisfies several requirements: enough candidates, enough diversity, acceptable binding proxies, drug-likeness, synthetic accessibility, novelty, and other threshold-style constraints. One molecule can look good. A few molecules can look good. The final returned pool can still fail. ...

A benchmark is supposed to be a ruler. In AI, it often becomes a trophy shelf. A model gets a higher score, a chart moves up and to the right, and everyone politely pretends the hard part has been settled. That ritual works when the task is narrow: classify an image, answer a question, pass a coding test, retrieve a document. But it becomes much less comforting when the system being evaluated is no longer just answering. It is planning experiments, writing code, debugging failures, training models, interpreting results, and deciding what to try next. ...