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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 and open-source software tools, lay a foundation for robust ML benchmarking and thus the development of more impactful methods.
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All code associated with this paper has been made available in this Github repository.
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