Is SaaS a good business model for drug‑discovery companies?

That’s the pitch, yes—but it rests on a crucial assumption: that the properties those models optimize (binding energy, predicted off-targets, etc.) are strongly correlated with what pharma ultimately cares about—clinical success. So far, that link is weak. That's great that I got 100 candidates instead of 1 or two, but if I have no way of knowing which one will be a better candidate for pre-clinical and clinical testing (which costs much more than the discovery stage, and hence, can't be done for more than a handful of molecules) the problem is only very partly solved.

Machine-learning screens are great at ranking compounds for tight binding or flagging obvious off-target liabilities, but most Phase II/III failures stem from issues the models don’t yet capture: PK/PD quirks, formulation hurdles, human safety signals, or simply the complex biology of disease. Narrowing the search space upstream is helpful, but it doesn’t tackle the real bottleneck—turning a “perfect” binder into a safe, effective therapy in people. Until the models address those downstream risks, the overall attrition curve won’t shift as much as the marketing decks suggest.