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Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
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Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
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Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2025, 65, 18, 9398–9411
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https://doi.org/10.1021/acs.jcim.5c01609
Published September 11, 2025
Copyright © 2025 American Chemical Society
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Abstract

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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.

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01609.

  • Additional best practices for exceptional cases, a detailed description of our cross-validation experiment, background information on statistical testing, as well as on performance metrics, and details on a supporting visualization (PDF)

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Cited By

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This article is cited by 23 publications.

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Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2025, 65, 18, 9398–9411
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jcim.5c01609
Published September 11, 2025
Copyright © 2025 American Chemical Society
Request reuse permissions

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