Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
Jeremy R. Ash, Cas Wognum, Raquel Rodríguez-Pérez, Matteo Aldeghi, Alan C. Cheng, Djork-Arné Clevert, Ola Engkvist, Cheng Fang, Daniel J. Price, Jacqueline M. Hughes‐Oliver, W. Patrick Walters · Journal of Chemical Information and Modeling · 2025
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.