Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery

2025/09/11 by Jeremy R. Ash, Cas Wognum, Raquel Rodríguez-Pérez +8 · 1 voice · 4 citations
Computer Science · Materials Science · Mathematics · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Statistical Methods in Clinical Trials

paper · pdf · doi:10.1021/acs.jcim.5c01609

openalex publication_date 2025/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

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.

Citations

• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
• Cross-Validation: What Does It Estimate and How Well Does It Do It?
• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
• Combining IC ₅₀ or <i>K</i> _<i>i</i> Values from Different Sources Is a Source of Significant Noise
• Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
• Why Most Published Research Findings Are False

Cited by

• A Conformation-Centric Generative Foundation Model for Linear Polymer Modeling and Design
• Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
• Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study
• Machine Learning‐Guided Repositioning of a SARS‐CoV‐2‐Targeting Molecular Series as Cruzain Inhibitors

Discussions

• Ash et al (#JCIM ASAP 2025) Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery #cheminformatics #ML #chemsky 🧪 doi.org/10.1021/acs.... [bsky, 4 points, 0 comments]

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