{
  "abstract": "Scoring functions built with advanced deep learning algorithms have recently gained prominence in protein-ligand pose generation and binding affinity prediction for computational drug design. However, a number of shortcomings make it increasingly difficult to evaluate scoring functions for a specific task across studies. Transparent disclosure of training data, code and procedures is essential to permit reproducibility, reuse and independent scrutiny. Guarding against leakage, sampling bias and flawed design is equally important. Furthermore, meaningful comparisons require common data splits, checks for run-to-run variability and performance baselines. Here we propose a set of guidelines to help the community identify the most promising methods for these important problems.",
  "abstract_shown": true,
  "affiliations": [
    "Université Paris Cité",
    "INSERM ERL",
    "Department of Bioengineering",
    "Imperial College London"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Viet-Khoa Tran-Nguyen"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Pedro J. Ballester",
      "orcid": "0000-0002-4078-743X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e4yb9t34jp6aw.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008174/v1",
  "fulltext_url": null,
  "id": "e4yb9t34jp6aw",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008174/v1",
  "language": null,
  "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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7205013520",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008174/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/e4yb9t34jp6aw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008174",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008174/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Guidelines to Evaluate Structure-based Methods for Protein-Ligand Binding Prediction",
  "title_text": "Guidelines to Evaluate Structure-based Methods for Protein-Ligand Binding Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/e4yb9t34jp6aw",
  "venue": "ChemRxiv",
  "versions": []
}