{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.",
    "Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, USA.",
    "Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY 10016, USA.",
    "School of Pharmacy and Pharmaceutical Sciences & Center for Drug Discovery, Northeastern University, Boston, MA 02115, USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Amitesh Badkul",
      "orcid": "0009-0000-9207-1954"
    },
    {
      "affs": [
        1
      ],
      "name": "Li Xie"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammadsadeq Mottaqi",
      "orcid": "0000-0002-1398-7540"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Lei Xie",
      "orcid": "0000-0001-9051-2111"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0rhcrwq7tnz9h.bib",
  "cited_by": 0,
  "doi": "10.3390/info17111079",
  "fulltext_url": "https://europepmc.org/articles/PMC13742136",
  "id": "0rhcrwq7tnz9h",
  "issns": [
    "2078-2489"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997616",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1607|Advanced Proteomics Techniques and Applications"
    ]
  ],
  "openalex": "W7160065473",
  "pdf_url": "https://europepmc.org/articles/PMC13742136?pdf=render",
  "pmcid": "PMC13742136",
  "pmid": "42997616",
  "published": "2026-10-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0rhcrwq7tnz9h.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42997616",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997616"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Advanced Proteomics Techniques and Applications",
      "subfield": "physical"
    }
  ],
  "title": "DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.",
  "title_text": "DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/0rhcrwq7tnz9h",
  "venue": "Information (Basel)",
  "versions": []
}