{
  "abstract": "Here we introduce FLOWR, a structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a curated dataset comprising ligand-pocket cocrystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy and interaction recovery, while offering an inference speed-up, achieving up to 70-fold faster performance. In addition, we introduce FLOWR.MULTI, a highly accurate multi-purpose model allowing for the targeted sampling of ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of retraining or any resampling strategies. Collectively, our results indicate that FLOWR and FLOWR.MULTI represent an advancement in artificial intelligence-driven structure-based drug design, substantially enhancing the reliability and applicability of de novo, interaction- and fragment-based ligand generation in real-world drug discovery settings.",
  "abstract_shown": true,
  "affiliations": [
    "Machine Learning and Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany. julian.cremer@pfizer.com",
    "Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden. rossir@chalmers.se",
    "Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. rossir@chalmers.se",
    "Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden",
    "Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden",
    "Machine Learning and Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Julian Cremer",
      "orcid": "0000-0001-6319-7283"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Ross Irwin",
      "orcid": "0009-0003-0067-8313"
    },
    {
      "affs": [
        3
      ],
      "name": "Alessandro Tibo",
      "orcid": "0000-0002-9070-740X"
    },
    {
      "affs": [
        3
      ],
      "name": "Jon Paul Janet",
      "orcid": "0000-0001-7825-4797"
    },
    {
      "affs": [
        4
      ],
      "name": "Simon Olsson",
      "orcid": "0000-0002-3927-7897"
    },
    {
      "affs": [
        5
      ],
      "name": "Djork-Arné Clevert",
      "orcid": "0000-0003-4191-2156"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9ycnbbh8q0ta1.bib",
  "cited_by": 24,
  "doi": "10.1038/s43588-026-00998-8",
  "fulltext_url": null,
  "id": "9ycnbbh8q0ta1",
  "issns": [
    "2662-8457"
  ],
  "landing_url": "https://doi.org/10.1038/s43588-026-00998-8",
  "language": "en",
  "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",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7162681529",
  "pdf_url": "https://www.nature.com/articles/s43588-026-00998-8.pdf",
  "pmcid": "PMC13293864",
  "pmid": "42209794",
  "published": "2026-05-28",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/9ycnbbh8q0ta1.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:00Z",
      "native_id": "W7162681529",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s43588-026-00998-8"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation",
  "title_text": "FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:17Z",
  "url": "https://research.draconitic.com/paper/9ycnbbh8q0ta1",
  "venue": "Nature Computational Science",
  "versions": []
}