What question did this study set out to answer?

To explore the use of machine learning in small-molecule drug discovery and identify ongoing challenges and advancements.

Machine Learning Applied in Small Molecule Drug Discovery: Models,Strategies, and Future Prospects

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Review surveys machine learning applications in drug discovery, suggesting improvements for future challenges.

To explore the use of machine learning in small-molecule drug discovery and identify ongoing challenges and advancements.
Systematic review of machine learning algorithms used in drug discovery.
Categorization of algorithms by model type and application stage.
Analysis of challenges related to data quality and feature selection.
Identification of notable advancements in ML applications for drug discovery.
Highlighting the challenges of data quality and model generalizability.
Discussion of various ML techniques tailored for specific stages of drug development.

Zhang et al. (2026) studied this question.

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