Molecular Foundation Models in Pharmaceutical Sciences: A Critical Review

Molecular foundation models, pre-trained on millions of chemical structures, are increasingly promoted as a universal solution for pharmaceutical prediction tasks. Their appeal lies in the possibility that large-scale chemical pretraining can reduce dependence on small, noisy, task-specific datasets. Despite their rapid proliferation, critical examination of their pretraining data, leakage risks, transferability, and validation practices remains limited and fragmented. This is problematic becaus