{
  "abstract": "Elucidating reaction mechanisms requires efficient generation of transition states (TSs) and products. Existing diffusion and sequence-based models accelerate parts of this process over traditional string-based methods, but typically still require manual enumeration of either TSs or products, and stochastic diffusion dynamics can be inefficient and hard to control. We introduce MolGEN, a conditional flow-matching framework that uses deterministic optimal transport to map Gaussian priors to chemical distributions. For TS generation, MolGEN improves TS geometry and barrier-height prediction over diffusion models while enabling sub-second sampling. For reaction product generation, it achieves competitive top-k accuracy while preserving mass and electron balance. Using the same backbone for TS and product sampling, MolGEN enables template-free generative exploration of reaction networks without the repeated quantum-chemistry searches required by prior methods. For the γ-ketohydroperoxide decomposition network, it produces more valid TSs than string-based methods using only 12 quantum-chemistry evaluations instead of 1156, and identifies a lower-barrier pathway.",
  "abstract_shown": true,
  "affiliations": [
    "Bakar Institute of Digital Materials for the Planet, University of California, Berkeley, Berkeley, CA, USA. tuoping@berkeley.edu",
    "Institute of Science and Technology Austria, Klosterneuburg, Austria",
    "Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. liju@mit.edu",
    "Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. liju@mit.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ping Tuo",
      "orcid": "0000-0002-6477-5900"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiale Chen",
      "orcid": "0000-0001-5337-5875"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Ju Li",
      "orcid": "0000-0002-7841-8058"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/343z4pk6rxdrp.bib",
  "cited_by": 12,
  "doi": "10.1038/s41467-026-75654-w",
  "fulltext_url": null,
  "id": "343z4pk6rxdrp",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-75654-w",
  "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": "W7169615833",
  "pdf_url": "https://www.nature.com/articles/s41467-026-75654-w_reference.pdf",
  "pmcid": "PMC13493859",
  "pmid": "42469237",
  "published": "2026-07-17",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/343z4pk6rxdrp.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:00Z",
      "native_id": "W7169615833",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-75654-w"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Flow matching for reaction pathway generation",
  "title_text": "Flow matching for reaction pathway generation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:17Z",
  "url": "https://research.draconitic.com/paper/343z4pk6rxdrp",
  "venue": "Nature Communications",
  "versions": []
}