Molecular Property Prediction

Molecular property prediction — estimating toxicity, solubility, binding affinity, or other properties from molecular structure — is the workhorse task of AI-driven drug discovery. GNNs operate on molecular graphs while transformer approaches (ChemBERTa, Uni-Mol) use SMILES strings or 3D coordinates. MoleculeNet (2018) and the Therapeutic Data Commons (TDC) provide standardized benchmarks, but the real bottleneck is distribution shift: models trained on known chemical space struggle with novel scaffolds, and the gap between leaderboard accuracy and actual wet-lab utility remains the field's central challenge.