Fragments, Feedback, and Fast Drugs: When Generative Models Grow a Spine
A lab does not slow down because nobody can generate molecules. That is the polite fiction. In many drug discovery workflows, candidate molecules can be generated in bulk. The slower part comes after generation: chemists inspect what the model proposes, explain what looks wrong or promising, and then someone has to translate that feedback into the model’s objective function. This “someone” is usually an AI engineer who understands the code but not necessarily the medicinal chemistry intuition. The chemist understands the target, the scaffold, and the quiet reasons a molecule feels suspicious. The model understands none of that unless the translation layer works. ...