Machine Learning–Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation
TL;DR
This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches.
Abstract
Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare.