{
  "abstract": "Identifying potent lead molecules for specific targets remains a major bottleneck in drug discovery. As structural information about proteins becomes increasingly available, ultra-large virtual screenings (ULVSs) which computationally evaluate billions of molecules offer a powerful way to accelerate early-stage drug discovery. Here, we introduce AdaptiveFlow, an open-source platform designed to make ULVSs more accessible, scalable, and efficient. AdaptiveFlow provides free access to a screening-ready version of the Enamine REAL Space, the largest library of ready-to-dock, drug-like molecules, containing 69 billion compounds that we prepared using the ligand preparation module of the platform. A key innovation of the platform is its use of a multi-dimensional grid of molecular properties, which helps researchers explore and prioritize chemical space more effectively and reduce the computational costs by a factor of approximately 1000. This grid forms the basis of a new method for identifying promising regions of chemical space, enabling systematic exploration and prioritization of compound libraries. An optional active learning component can further accelerate this process by adaptively steering the search toward molecules most likely to bind a given target. To support a broad range of applications, AdaptiveFlow is compatible with over 1,500 docking methods. The platform achieves near-linear scaling on up to 5.6 million CPUs in the AWS Cloud, setting a new benchmark for large-scale cloud computing in drug discovery. Using this approach, we identified nanomolar inhibitors of two disease-relevant targets: ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1 (PARP-1). By leveraging newly solved crystal structures of FSP1 in complex with NAD+, FAD, and coenzyme Q1, we validated these hits experimentally and determined the first co-crystal structures of FSP1 bound to small-molecule inhibitors, enabling insights into inhibitor binding mechanisms previously unknown. With its high scalability, flexibility, and open accessibility, AdaptiveFlow offers a powerful new resource for discovering and optimizing drug candidates at an unprecedented scale and speed.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biology and Biotechnology, University of Pavia, Pavia, Italy",
    "Department of Computer Science, Stanford University, Stanford, CA, USA",
    "Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA",
    "Frazer Institute, Faculty of Medicine at the Translational Research Institute Australia, The University of Queensland, Brisbane, Queensland, Australia",
    "School of Biomedical Sciences, Centre for Genomics and Personalised Health at the Translational Research Institute Australia, Queensland University of Technology, Brisbane, Queensland, Australia",
    "Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA",
    "Amazon Web Services, Seattle, WA, USA",
    "Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA",
    "Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA, USA",
    "Department of Biochemistry, University of Oxford, Oxford, UK",
    "Department of Bioengineering, Ege University, Bornova, Türkiye",
    "Department of Chemistry, University of Zurich, Zurich, Switzerland",
    "V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, Kyiv, Ukraine",
    "Google, Mountain View, CA, USA",
    "Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA",
    "Chemical Biology Program, Dana-Farber Cancer Institute, Boston, MA, USA",
    "Max Delbrück Center for Molecular Medicine, Berlin, Germany",
    "Zuse Institute Berlin (ZIB), Berlin, Germany",
    "Department of Chemistry, University of Toronto, Toronto, Ontario, Canada",
    "Department of Computer Science, University of Toronto, Toronto, Ontario, Canada",
    "Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada",
    "Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada",
    "Department of Oncology, University of Oxford, Oxford, UK",
    "Mathematical Institute, University of Oxford, Oxford, UK",
    "Harvard College, Cambridge, MA, USA",
    "Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, India",
    "Institute for Mathematics, Technical University Berlin, Berlin, Germany",
    "Department of Genetics, Stanford University, Stanford, CA, USA",
    "Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA",
    "Enamine Ltd, Kyiv, Ukraine",
    "NVIDIA, Santa Clara, CA, USA",
    "Intel, Santa Clara, CA, USA",
    "Pulse for Integrated Solutions, Unterschleißheim, Germany",
    "Wesleyan University, Middletown, CT, USA",
    "Acceleration Consortium, Acceleration Consortium, Toronto, Ontario, Canada",
    "Canadian Institute for Advanced Research, Toronto, Ontario, Canada",
    "Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario, Canada",
    "Department of Materials Science & Engineering, University of Toronto, Toronto, Ontario, Canada",
    "Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada",
    "NVIDIA, Toronto, Ontario, Canada"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 48,
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        0
      ],
      "name": "Domiziana Cecchini",
      "orcid": "0009-0004-0089-7652"
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      ],
      "name": "AkshatKumar Nigam",
      "orcid": "0000-0002-5152-2082"
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        2,
        3,
        4
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      "name": "Ming Tang",
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        5
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      "name": "Joana Reis"
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      "name": "Matt Koop"
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      "name": "Andrea Gottinger",
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      "name": "Callum R. Nicoll",
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      "name": "Süleyman Selim Çınaroğlu",
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      "name": "Ricarda Törner",
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      "name": "Yehor S. Malets",
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      "name": "Minko Gehev"
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      "name": "Krishna M. Padmanabha Das"
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      "name": "Nidhin Thomas"
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      "name": "Yong Li",
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      "name": "Hyuk‐Soo Seo",
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      "name": "Sirano Dhe‐Paganon",
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      "name": "Christopher Secker",
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      "name": "Mohammad Haddadnia",
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      "name": "Alexander Hasson",
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      "name": "Minkai Li"
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      "name": "Abhishek Kumar",
      "orcid": "0000-0003-3042-2098"
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      "name": "Roni Levin‐Konigsberg",
      "orcid": "0009-0004-6272-7093"
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      "affs": [
        28
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      "name": "Eun‐Bee Choi",
      "orcid": "0000-0003-4269-4584"
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      "affs": [
        28
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      "name": "Geoffrey Ira Shapiro",
      "orcid": "0000-0002-3331-4095"
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        5
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      "name": "Huel Cox",
      "orcid": "0009-0004-5808-868X"
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        5
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      "name": "Luke Sebastian",
      "orcid": "0000-0002-6863-9420"
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      "name": "Chelsea Braithwaite",
      "orcid": "0009-0007-7720-1045"
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      "name": "Puspalata Bashyal",
      "orcid": "0000-0002-7145-7660"
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      "name": "Dmytro S. Radchenko",
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        2,
        26
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      "name": "Aditya Kumar",
      "orcid": "0000-0001-7550-8034"
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        14
      ],
      "name": "Lei Yang",
      "orcid": "0000-0002-3060-0790"
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    {
      "affs": [
        6,
        30
      ],
      "name": "Pierre-Yves Aquilanti"
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      "name": "Henry A. Gabb",
      "orcid": "0000-0002-9507-4250"
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      "name": "Amr Alhossary",
      "orcid": "0000-0002-4470-5817"
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      "name": "Eric O’Neill"
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      "name": "Gerhard Wagner",
      "orcid": "0000-0002-2063-4401"
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      "name": "Alán Aspuru‐Guzik",
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      "name": "Yurii S. Moroz",
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      "name": "Charalampos G. Kalodimos",
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      "name": "Konstantin Fackeldey",
      "orcid": "0000-0002-4373-3672"
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      "name": "John D. Schuetz",
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      "name": "Andrea Mattevi",
      "orcid": "0000-0002-9523-7128"
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      "name": "Haribabu Arthanari",
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      "name": "Christoph Gorgulla",
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  "bibtex": "https://research.draconitic.com/paper/ahzcah9z4st0v.bib",
  "cited_by": 30,
  "doi": "10.1038/s41587-026-03217-x",
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  "id": "ahzcah9z4st0v",
  "issns": [
    "0733-222X",
    "1087-0156",
    "1546-1696",
    "2331-3684"
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  "landing_url": "https://doi.org/10.1038/s41587-026-03217-x",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
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  "pdf_url": "https://www.nature.com/articles/s41587-026-03217-x.pdf",
  "pmcid": "PMC13041786",
  "pmid": "41929058",
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/ahzcah9z4st0v.ris",
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  "subjects": [
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      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
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  "title": "AI-enhanced adaptive virtual screening of large libraries for ligand discovery",
  "title_text": "AI-enhanced adaptive virtual screening of large libraries for ligand discovery",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:17Z",
  "url": "https://research.draconitic.com/paper/ahzcah9z4st0v",
  "venue": "Nature Biotechnology",
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