{
  "abstract": "Recent advances in deep learning-based structure prediction, including AlphaFold3 and the open-source Boltz model family, have extended biomolecular modeling to joint prediction of protein-ligand, protein-nucleic acid, and multi-chain complexes with binding-affinity estimation. Boltz-2 is among the most feature-complete of these open models, but its practical use requires a local CUDA-capable GPU, command-line execution, and manually authored YAML configuration files, limiting accessibility for researchers without dedicated computational infrastructure. We developed Boltz2-Notebook, a Colab-native interface comprising four integrated stages - automated environment setup, interactive parameter-to-YAML generation, execution management, and automated confidence and affinity visualization - together with a manifest-driven batch mode for multi-target screening. All modelling capabilities are inherited unmodified from Boltz-2; Boltz2-Notebook's contributions are limited to accessibility, input construction, and workflow automation. Independent of the software, we curated a benchmark of 317 protein-ligand pairs (122 proteins, 277 ligands) from BindingDB and predicted binding affinity in triplicate using the Boltz-2 command-line engine on high-performance computing infrastructure. Predicted and experimental pIC50 values showed moderate correlation (Pearson r = 0.609 [95% CI 0.540-0.675]; Spearman {rho} = 0.625; R2 = 0.371; MAE = 0.968 pIC50 units), with high triplicate reproducibility (pairwise r = 0.97) but a systematic compression of the predicted affinity range and no measurable relationship between Boltz-2's self-reported confidence metrics and prediction accuracy. Boltz2-Notebook is freely available as open-source software and provides external, reproducible evidence - including a specific confidence-calibration limitation - relevant to interpreting Boltz-2 affinity predictions responsibly.",
  "abstract_shown": true,
  "affiliations": [
    "Gujarat Biotechnolgy University"
  ],
  "article_type": "Confirmatory results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "A. Tilewale"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Patel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a8nbfnbx55bze.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.18.752645",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.18.752645v1.full",
  "id": "a8nbfnbx55bze",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.18.752645v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7213587529",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.18.752645v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a8nbfnbx55bze.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:24Z",
      "native_id": "10.64898/2026.09.18.752645",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.18.752645v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Boltz2-Notebook: An Interactive Google Colab Platform for Diffusion-Based Biomolecular Structure and Binding Affinity Prediction using the Boltz2 model.",
  "title_text": "Boltz2-Notebook: An Interactive Google Colab Platform for Diffusion-Based Biomolecular Structure and Binding Affinity Prediction using the Boltz2 model.",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:24Z",
  "url": "https://research.draconitic.com/paper/a8nbfnbx55bze",
  "venue": "bioRxiv",
  "versions": []
}