{"version":1,"type":"story","url":"https://digestai.news/story/nature-machine-intelligence-publishes-guidelines-for-evaluating-protei","json":"https://digestai.news/story/nature-machine-intelligence-publishes-guidelines-for-evaluating-protei.json","markdown":"https://digestai.news/story/nature-machine-intelligence-publishes-guidelines-for-evaluating-protei.md","slug":"nature-machine-intelligence-publishes-guidelines-for-evaluating-protei","headline":"Nature Machine Intelligence publishes guidelines for evaluating protein‑ligand scoring methods","summary":"The paper by Tran‑Nguyen and Ballester in *Nature Machine Intelligence* proposes a comprehensive set of guidelines for assessing AI‑based scoring functions used in protein–ligand pose generation and binding affinity prediction. The authors argue that transparent disclosure of training data, code, and procedures is essential for reproducibility, while guarding against data leakage, sampling bias, and flawed experimental design.\n\nKey recommendations include using common data splits, reporting run‑to‑run variability, and providing baseline performance metrics. The authors also stress the need for independent scrutiny and the adoption of standardized evaluation protocols to enable meaningful comparisons across studies. Funding for the work came from the Engineering and Physical Sciences Research Council (grant EP/X012026/1), the Wolfson Foundation, the Royal Society (grant RSWF\\R1\\221005), and the French National Research Agency (grant ANR‑25‑CE45‑0171).","keyPoints":["Authors propose reproducibility guidelines for AI scoring functions in protein‑ligand binding prediction.","Guidelines call for transparent data, code release, common data splits, and reporting run‑to‑run variability.","Research funded by EPSRC (EP/X012026/1), Wolfson Foundation, Royal Society (RSWF\\R1\\221005), and ANR (ANR‑25‑CE45‑0171)."],"whyItMatters":"Standardized evaluation reduces bias and data leakage, making AI models for drug discovery more reliable and comparable, which speeds trustworthy virtual screening.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["Nature Machine Intelligence","Engineering and Physical Sciences Research Council","Wolfson Foundation","Royal Society","Agence Nationale de la Recherche"],"models":[],"people":["Tran‑Nguyen V.K.","Ballester P.J."]},"firstPublishedAt":"2026-10-23T00:00:00Z","updatedAt":"2026-10-23T00:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"Nature Machine Learning","title":"Guidelines for evaluating structure-based methods for protein–ligand binding prediction","url":"https://nature.com/articles/s42256-026-01331-8","publishedAt":"2026-10-23T00:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Nature Machine Intelligence publishes guidelines for evaluating protein‑ligand scoring methods\", 23 October 2026, https://digestai.news/story/nature-machine-intelligence-publishes-guidelines-for-evaluating-protei","publisher":"Digest AI","title":"Nature Machine Intelligence publishes guidelines for evaluating protein‑ligand scoring methods","datePublished":"2026-10-23T00:00:00Z","url":"https://digestai.news/story/nature-machine-intelligence-publishes-guidelines-for-evaluating-protei"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}