Nature Machine Intelligence publishes guidelines for evaluating protein‑ligand scoring methods
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,…
Key points
- 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).
Key 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).
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