{
  "abstract": "Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction plays a central role in modern computer-aided drug discovery by enabling early evaluation of pharmacokinetic behaviour and safety liabilities. This tutorial presents a reproducible computational workflow for large-scale ADMET profiling using ADMET-AI 2.0, a graph neural network-based prediction platform, applied to 200 cyclin-dependent kinase 2 (CDK2) inhibitors curated from ChEMBL release 35. Implemented within an annotated Jupyter notebook, the workflow integrates cheminformatics preprocessing, physicochemical property analysis, drug-likeness evaluation, structural alert screening, absorption and distribution profiling, CYP450-mediated metabolism prediction, excretion assessment, toxicity evaluation, Tox21 pathway analysis, DrugBank percentile benchmarking, and composite multi-parameter optimisation scoring. A total of 104 ADMET-related outputs were generated per compound, including 52 predictive endpoints and 52 corresponding DrugBank approved-drug percentile rankings. The 200 curated CDK2 inhibitors exhibited 100% Lipinski Ro5 compliance, 38.5% predicted CYP3A4 inhibition, 73.5% predicted hERG liability, and 99.5% predicted DILI risk, with a mean composite ADMET score of 0.46 and a mean traffic light green flag count of 9.5/13. The workflow provides an accessible and FAIR-compliant framework for ADMET analysis, cheminformatics education, and reproducible computational drug discovery.Scientific contributionThis work contributes a structured educational framework for teaching and implementing computational ADMET analysis in modern drug discovery. To be explicit about the nature of this contribution: the novelty is pedagogical and integrative rather than methodological, no new prediction model, algorithm, or endpoint is introduced. The study demonstrates how an existing graph neural network-based prediction model (ADMET-AI) can be integrated with open-source cheminformatics workflows to support interpretable and reproducible pharmacokinetic assessment. Beyond endpoint prediction, the tutorial emphasises practical data handling, workflow automation, compound prioritisation, and transparent result interpretation within a Jupyter notebook environment. The inclusion of DrugBank percentile benchmarking, traffic-light visualisation, and composite scoring strategies provides learners and researchers with practical, worked examples of established multi-parameter decision-making approaches during lead optimisation, rather than new decision frameworks in their own right. By combining methodological clarity with FAIR and reproducible research principles, the workflow serves as a scalable resource for cheminformatics education, computational pharmacology training, and ADMET-guided drug development studies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "other",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Festus O. Ogungbemiro",
      "orcid": "0009-0003-2322-9887"
    },
    {
      "name": "Jane O. Anebi"
    },
    {
      "name": "Felix O. Okunlola"
    },
    {
      "name": "Love A. Onyekwere"
    },
    {
      "name": "Evna M. M. Pereira"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/97qq1m1k5h83p.bib",
  "cited_by": 0,
  "doi": "10.1186/s13321-026-01289-0",
  "fulltext_url": "https://europepmc.org/articles/PMC13729302",
  "id": "97qq1m1k5h83p",
  "issns": [
    "1758-2946"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13321-026-01289-0",
  "language": "eng",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Educational Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13321-026-01289-0.pdf",
  "pmcid": "PMC13729302",
  "pmid": "42972609",
  "published": "2026-11-02",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/97qq1m1k5h83p.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T00:23:30Z",
      "native_id": "10.1186/s13321-026-01289-0",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13321-026-01289-0"
    },
    {
      "first_seen": "2026-11-04T15:20:43Z",
      "native_id": "MED:42972609",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42972609"
    },
    {
      "first_seen": "2026-11-07T12:22:05Z",
      "native_id": "PMC13729302",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13321-026-01289-0"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:1758-2946",
      "subfield": "theoretical"
    }
  ],
  "title": "ADMET property prediction and toxicity profiling of CDK2 inhibitors: a practical cheminformatics tutorial using ADMET-AI 2.0 and RDKit",
  "title_text": "ADMET property prediction and toxicity profiling of CDK2 inhibitors: a practical cheminformatics tutorial using ADMET-AI 2.0 and RDKit",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:05Z",
  "url": "https://research.draconitic.com/paper/97qq1m1k5h83p",
  "venue": "Journal of Cheminformatics",
  "versions": []
}