Ash, Jeremy R., Wognum, Cas, Rodriguez Perez, Raquel, Aldeghi, Matteo, Cheng, Alan C., Clevert, Djork-Arne, Engkvist, Ola, Fang, Cheng, Price, Daniel J., Hughes-Oliver, Jacqueline M. and Walters, W. Patrick (2025) Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. Journal of chemical information and modeling. ISSN 1549-960X

Abstract

Machine Learning (ML) methods that relate molecular structure to properties are frequently proposed as in silico surrogates for expensive or time-consuming experiments. In small molecule drug discovery, such methods inform high-stakes decisions like compound synthesis and in vivo studies. This application lies at the intersection of multiple scientific disciplines. When comparing new ML methods to baseline or state-of-the-art approaches, statistically rigorous method comparison protocols and domain-appropriate performance metrics are essential to ensure replicability and ultimately the adoption of ML in small molecule drug discovery. This paper proposes a set of guidelines to incentivize rigorous and domain-appropriate techniques for method comparison tailored to small molecule property modeling. These guidelines, accompanied by annotated examples using open-source software tools, lay a foundation for robust ML benchmarking and thus the development of more impactful methods.

Item Type:	Article
Date Deposited:	26 Sep 2025 00:45
Last Modified:	26 Sep 2025 00:45
URI:	https://oak.novartis.com/id/eprint/55660