{
  "abstract": "Motivation\n\nPocket-conditioned molecular generation incorporates the three-dimensional (3D) geometry and chemical environment of protein binding sites into molecular design. However, receptor conformational variation can introduce distribution shifts between training and inference, particularly under cross-docking conditions. Existing approaches often do not fully exploit the correspondence between related receptor conformations during training.Results\n\nWe propose a structure-conditioned 3D molecular generation framework that combines pocket-level contrastive learning with reference-conformation feature fusion. Built on a stepwise constructive generative model, these training-stage components encourage stable pocket representations across related conformations while preserving single-pocket inference without requiring a reference conformation. Evaluation on CrossDocked2020 and three external datasets, ASB-E, DUD-E, and APObind, demonstrates improvements in generation success, structural validity, and docking-related performance, particularly under cross-dock evaluation. These results support improved robustness of pocket-conditioned molecular generation under receptor conformational variation.Availability and implementation\n\nThe source code and reproduction instructions are available at https://github.com/LHHHHuan/MTMGen. Pretrained models and an archival code snapshot are available at https://doi.org/10.5281/zenodo.21944857.",
  "abstract_shown": true,
  "affiliations": [
    "College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.",
    "Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.",
    "Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huan Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yijie Ding",
      "orcid": "0000-0002-3438-9259"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaoyi Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongjie Wu",
      "orcid": "0000-0001-5921-8707"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/exsgmb6s5sq8k.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag740",
  "fulltext_url": "https://europepmc.org/articles/PMC13671754",
  "id": "exsgmb6s5sq8k",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42834449",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7220664720",
  "pdf_url": "https://europepmc.org/articles/PMC13671754?pdf=render",
  "pmcid": "PMC13671754",
  "pmid": "42834449",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/exsgmb6s5sq8k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "MED:42834449",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42834449"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag740",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag740/8866642"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1367-4803",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Pocket contrastive learning and conformation fusion for robust structure-conditioned 3D molecular generation under receptor conformational variation.",
  "title_text": "Pocket contrastive learning and conformation fusion for robust structure-conditioned 3D molecular generation under receptor conformational variation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/exsgmb6s5sq8k",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}