Nature Machine Intelligence 发布蛋白–配体结合预测评估指南
Guidelines for evaluating structure-based methods for protein–ligand binding prediction
摘要
Ballester 与 Tran-Nguyen 在 Nature Machine Intelligence 提出评估蛋白–配体结合预测方法的规范，主张在数据、代码和实验流程上保持透明。文章讨论防止数据泄漏与偏差的良好实践，以实现不同打分方法之间公平、可复现且有意义的比较。
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正文 · 原文
Nature Machine Intelligence, Published online: 23 October 2026; doi:10.1038/s42256-026-01331-8
Ballester and Tran-Nguyen advocate for transparency in data, code and experimental procedures in the evaluation of protein–ligand predictions. They discuss good practices for preventing data leakage and bias, enabling fair, reproducible and meaningful comparisons across different scoring methods.
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来源：Nature Machine Intelligence · nature.com
引用原文
Viet-Khoa Tran-Nguyen. Guidelines for evaluating structure-based methods for protein–ligand binding prediction[EB/OL]. (2026-10-23)[访问日期]. https://www.nature.com/articles/s42256-026-01331-8.