Refnd: Preventing Data Leakage in Relational Datasets

Abstract:Machine learning models trained on biochemical data are routinely evaluated using splits that fail to account for relational structure, causing information leakage and over-optimistic performance estimates. Existing splitting methods lack theoretical grounding and scale at best quadratically. We introduce the Relational Generative Process (RGP), a mathematical formalization explaining why relational structure arises in biochemical datasets, and Refnd, a splitting algorithm that leverages a proximity graph computed in loglinear time using Hierarchical Navigable Small World (HNSW). We validate on an antimicrobial peptide dataset, showing that Refnd splits yield lower but more realistic evaluation performance than traditional splits. Refnd is applicable to any dataset arising from an RGP such as protein sequences and structures, small molecules, and nucleotide sequences, and is openly available as a Rust accelerated Python package: pip install refnd.

Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)

arXiv:2607.19376 [q-bio.QM]

(or arXiv:2607.19376v1 [q-bio.QM] for this version)

https://doi.org/10.48550/arXiv.2607.19376 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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