{
  "abstract": "Computational blind challenges are essential for advancing drug discovery, especially in research areas with limited public data, such as absorption, distribution, metabolism, excretion, toxicity (ADMET) prediction. In this paper, we present the results of our latest community blind challenge, organized in collaboration with Expansion Therapeutics (ExpansionRx). Participants were tasked with predicting nine distinct ADMET endpoints routinely evaluated during lead optimization of compounds in drug discovery programs. Following unprecedented engagement from the global community, this challenge provided valuable insights into the state of the art in machine learning for ADMET modeling. Evaluating submissions against a realistic, challenging dataset from active ExpansionRx drug discovery programs revealed that message-passing neural networks and chemical foundation models dominated among the top performers. Furthermore, the results highlighted the power of proprietary industry datasets and a critical need to optimize predictive workflows for different endpoints independently via multitasking and task-affinity grouping. We also demonstrate how hosting our open-source blind challenge infrastructure on a flexible platform like Hugging Face was pivotal for driving community engagement and visibility. Ultimately, blind challenges supported by robust infrastructure, widespread dissemination, and rigorous post-challenge analysis are fundamental to maximizing the utility of public data releases. We anticipate that these community benchmarks will continue to drive the advancement of ADMET models, enabling their deployment in both early- and late-stage drug discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Open Molecular Software Foundation",
    "Expansion Therapeutics",
    "Hugging Face AI4Science",
    "Faculty of Science and Engineering, Antonius Deusinglaan 1, Groningen, Netherlands, 9713",
    "Simulations Plus, Inc.",
    "Graduate Center",
    "City University of New York",
    "Independent Researcher",
    "AIDRx Strategy Consulting Ltd",
    "VIB Discovery Sciences",
    "Aptuit (Verona) Srl, an Evotec Company",
    "Department of Medicinal Chemistry and Drug Design, Merck Healthcare KGaA, Frankfurter Str. 250, Darmstadt, Germany, 64293",
    "Evotec (France) SAS",
    "Department of Chemical Engineering",
    "Massachusetts Institute of Technology",
    "School of Pharmacy",
    "University of Pittsburgh",
    "Novalix",
    "Faculty of Chemical Technology",
    "University of",
    "QuantaBricks LLC, 211 Warren Street, Newark, NJ",
    "Merck & Co., Inc.",
    "Shiv Nadar University",
    "Commonwealth Scientific and Industrial Research Organisation (CSIRO",
    "École Polytechnique Fédérale de Lausanne (EPFL",
    "deepmirror",
    "Schrödinger, Inc.",
    "Department of Biostatistics and Medical Informatics",
    "University of Wisconsin-Madison & Morgridge Institute for Research",
    "Institute of Pharmaceutical and Medicinal Chemistry",
    "University of Münster",
    "Lomonosov Moscow State University (MSU",
    "Independent Consultant",
    "Apheris",
    "LIFE Pharma FZE",
    "Francis Crick Institute",
    "University College London (UCL",
    "thinkMolecular Technologies Bengaluru",
    "Department of Physical Chemistry of Drugs, Faculty of Pharmacy",
    "Comenius University Bratislava"
  ],
  "article_type": null,
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  "author_total": 106,
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      "name": "Xin Chen",
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      "name": "Alan C. Cheng",
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      "name": "Vishnu Vardhan Chundu"
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      "name": "Wim Dehaen",
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      "name": "Valeriia Fil",
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      "name": "Jozef Fülöp",
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      "name": "Oleg Gromov"
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      "name": "Leonard F. Haasbroek",
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      "name": "Xibing He"
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      "name": "Dimitrios Iliadis"
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      "name": "Oliver Koch",
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      "name": "Vaishnavi Kodamala"
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    {
      "name": "Stephanie Labouille"
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    {
      "name": "Shaolong Lin"
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  "bibtex": "https://research.draconitic.com/paper/a43n1bv902vz4.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009342/v1",
  "fulltext_url": null,
  "id": "a43n1bv902vz4",
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  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009342/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
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  "openalex": "W7214098325",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009342/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/a43n1bv902vz4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009342",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009342/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Assessing the state of the art in ADMET prediction: Lessons from the OpenADMET-ExpansionRx community blind challenge",
  "title_text": "Assessing the state of the art in ADMET prediction: Lessons from the OpenADMET-ExpansionRx community blind challenge",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/a43n1bv902vz4",
  "venue": "ChemRxiv",
  "versions": []
}