Machine Learning for Screening Bioactive Compounds in Traditional Chinese Medicine: Computational Prediction, Multi-Omics Integration, and Clinical Translation Potential

Abstract

Identifying bioactive compounds in traditional Chinese medicine (TCM) requires computational prioritization to be connected with chemical and pharmacological evidence. This structured narrative and critical review examines machine learning applications in compound prediction, formula-compatibility analysis, multi-omics integration, and drug development and repurposing. Molecular fingerprints, graph and sequence representations, knowledge graphs, and generative models are discussed in relation to their intended endpoints and validation conditions. Studies are classified along two independent dimensions: application scope, distinguishing direct TCM applications from natural-product studies and general drug-discovery methods, and validation level, ranging from computational prediction to chemical, experimental, and clinical evidence. Three case analyses illustrate a TCM-specific graph-learning platform with chemical confirmation, a downstream animal and metabolomics study without machine learning screening, and a proposed causal framework assembled from separate studies. Across these applications, machine learning supports candidate ranking and hypothesis generation, but predicted targets, attention weights, and multi-omics associations do not independently establish target engagement, formula synergy, or therapeutic efficacy. Major limitations include chemical-domain shift, inconsistent database provenance, data leakage, sparse negative results, and incomplete experimental follow-up. Reliable advancement requires scaffold-aware or external evaluation, explicit applicability domains and uncertainty, traceable data processing, and constituent-resolved intervention studies. A minimum reporting checklist consolidates these requirements, while route-specific assessment distinguishes synthetic accessibility from biosynthetic feasibility. Together, these considerations provide a basis for evaluating screening claims and designing reproducible workflows toward experimentally supported TCM compound discovery.