AI drug discovery

Drug discovery traditionally starts with screening millions of compounds to find one that binds a disease target, then spends years testing whether it is safe and effective enough to reach patients. ML-based approaches compress the early search: models trained on protein structures and known binding data predict which candidate molecules are worth making, cutting the number of compounds that need physical synthesis and testing. The hard part is that a molecule that looks good in silico still fails most of the time in cells, animals, and humans — the models accelerate the search, they do not replace the experimental gauntlet.