{
  "abstract": "Diffusion-based models have substantially advanced biomolecular structure prediction. Within this framework, restraint-guided inference (RGI) corrects the denoiser output at each reverse-diffusion step using a differentiable restraint loss, allowing available experimental information, stereochemical requirements, or a specified target conformational state to be incorporated into predictions without retraining the model. Here, we present RGI-Toolkit, a general-purpose Python library that makes RGI available for a range of structure predictors. The toolkit consolidates restraint definition, atom selection, loss evaluation, and coordinate optimization into a shared, backend-agnostic engine, limiting predictor-specific code to a thin preprocessing adapter and a single hook in the denoising loop. Restraints on distances, angles, and dihedrals, on deviations from reference structures, and on user-defined reaction coordinates, together with ligand conformer restraints, are all specified in a common configuration format. The toolkit currently supports six predictors, including AlphaFold3. Across all six predictors, conformer restraints eliminated chirality and cis/trans errors in the predicted ligands and improved their local geometry, while geometric and custom restraints systematically shifted the sampled protein conformations toward the specified target states. RGI-Toolkit thus provides a unified framework for integrating experimental information and prior geometric knowledge into structure prediction and for generating biomolecular structures with specified conformational and stereochemical properties.",
  "abstract_shown": true,
  "affiliations": [
    "Medical Research Laboratory, Institute of Science Tokyo"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "T. Hori"
    },
    {
      "name": "Y. Moriwaki"
    },
    {
      "affs": [
        0
      ],
      "name": "R. Ishitani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/crr3w2xmb4x5d.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.05.756905",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756905v1.full",
  "id": "crr3w2xmb4x5d",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756905v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7220718258",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756905v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/crr3w2xmb4x5d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:20Z",
      "native_id": "10.64898/2026.10.05.756905",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756905v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "RGI-Toolkit: Differentiable Restraints for Controllable Biomolecular Structure Prediction",
  "title_text": "RGI-Toolkit: Differentiable Restraints for Controllable Biomolecular Structure Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:07Z",
  "url": "https://research.draconitic.com/paper/crr3w2xmb4x5d",
  "venue": "bioRxiv",
  "versions": []
}