{
  "abstract": "We introduce MolMiner, a fragment‐based, geometry‐aware, and order‐agnostic autoregressive model for molecular design. MolMiner supports high‐dimensional conditional control over 12 physicochemical and structural properties from partial specifications, constructs molecules via symmetry‐aware fragment attachments, and conditions each generation step on force‐field‐relaxed three‐dimensional geometry of the partial structure. Conditional control emerges without auxiliary property losses. On targeted property windows, conditioning lifts hit rates by up to 5.25 over unconditional generation and 3.5 over the training distribution itself—overriding the model's intrinsic biases—at the cost of a small reduction in unconditional distributional fidelity. MolMiner unifies dynamic geometry, symmetry handling, order‐agnostic generation, and scalable multi‐property conditioning within a single framework.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Energy Conversion and Storage CAPeX Pioneer Center for Accelerating P2X Materials Discovery, Technical University of Denmark Lyngby Denmark",
    "Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Raul Ortega Ochoa",
      "orcid": "0000-0001-5709-5969"
    },
    {
      "affs": [
        0
      ],
      "name": "Tejs Vegge",
      "orcid": "0000-0002-1484-0284"
    },
    {
      "affs": [
        1
      ],
      "name": "Jes Frellsen",
      "orcid": "0000-0001-9224-1271"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cqnbgms91n911.bib",
  "cited_by": 6,
  "doi": "10.1002/aidi.70138",
  "fulltext_url": null,
  "id": "cqnbgms91n911",
  "issns": [
    "2943-9981"
  ],
  "landing_url": "https://doi.org/10.1002/aidi.70138",
  "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": "W4404391321",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cqnbgms91n911.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:00Z",
      "native_id": "W4404391321",
      "source": "openalex",
      "url": "https://doi.org/10.1002/aidi.70138"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design",
  "title_text": "MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:52Z",
  "url": "https://research.draconitic.com/paper/cqnbgms91n911",
  "venue": "Advanced Intelligent Discovery",
  "versions": []
}