DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications

Protein post-translational modifications (PTMs), particularly phosphorylation, regulate cellular signaling, drug response, and disease-associated pathway activity. However, existing PTM resources are largely static and do not capture how PTM regulation varies across drug, dose, time, cellular context, and modified site. We present DrugPTM-Bench, a curated benchmark derived from decryptM dose-dependent PTM measurements, standardizing site-level drug response across seven cancer cell lines, 27 drugs, 11,167 proteins, six time points, 16 dosage levels, and pEC50 potency values. Reflecting the central role of phosphorylation in drug-responsive signaling, DrugPTM-Bench is phosphorylation-dominant, with 99.5% phosphorylation events. We formulate a protein-disjoint out-of-distribution classification task to predict whether a PTM site is up-regulated, downr-egulated, or unchanged following drug treatment. Our results show that drug-induced PTM regulation is a highly imbalanced and context-dependent prediction problem: baseline machine learning and deep learning models fail to robustly recover rare regulated events, while standard rebalancing strategies improve recall only at the cost of precision and overall F1-score. Beyond classification, DrugPTM-Bench enables pEC50 regression, mechanism-of-action prediction from PTM fingerprints, and drug-specific PTM-site sensitivity ranking.