DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.
4 affiliations
- 1Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.
- 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, USA.
- 3Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY 10016, USA.
- 4School of Pharmacy and Pharmaceutical Sciences & Center for Drug Discovery, Northeastern University, Boston, MA 02115, USA.
Abstract
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.
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BibTeX
@article{badkul2026drugptmbench,
title = {{DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.}},
author = {Amitesh Badkul and Li Xie and Mohammadsadeq Mottaqi and Lei Xie},
journal = {Information (Basel)},
year = {2026},
month = oct,
doi = {10.3390/info17111079},
url = {https://europepmc.org/article/MED/42997616},
note = {CC BY},
}
RIS
TY - JOUR
TI - DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.
AU - Amitesh Badkul
AU - Li Xie
AU - Mohammadsadeq Mottaqi
AU - Lei Xie
PY - 2026
DA - 2026/10/31
JO - Information (Basel)
DO - 10.3390/info17111079
UR - https://europepmc.org/article/MED/42997616
AB - 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.
SN - 2078-2489
ER -