Resolving data bias improves generalization in binding affinity prediction
David Graber; Peter Stockinger; Fabian Meyer; Siddhartha Mishra; Claus Horn; Rebecca Buller · 2025 · Nature Machine Intelligence
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The field of computational drug design requires accurate scoring functions to predict binding affinities for protein-ligand interactions. However, train-test data leakage between the PDBbind database and the Comparative Assessment of Scoring Function benchmark datasets has severely inflated the performance metrics of currently available deep-learning-based binding affinity prediction models, leading to overestimation of their generalization capabilities. Here we address this issue by proposing PDBbind CleanSplit, a training dataset curated by a new structure-based filtering algorithm that elim
Abstract by David Graber; Peter Stockinger; Fabian Meyer; Siddhartha Mishra; Claus Horn; Rebecca Buller, Nature Machine Intelligence (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s42256-025-01124-5