Network Pharmacology of Herbal Medicines: An Integrative Review of Multi-Compound, Multi-Target, and Pathway-Level Therapeutic Discovery

Download PDF

Network pharmacology has become a prominent systems-oriented approach for studying herbal medicines, medicinal plants, botanical preparations, traditional formulations, phytochemicals, and natural-product-derived compounds as chemically complex therapeutic systems. This integrative review examines how network pharmacology is used to organize herbal multi-component composition into candidate-compound profiles, putative target relationships, disease-associated gene intersections, protein interaction networks, pathway-level hypotheses, and validation-oriented research priorities. The review integrates conceptual, methodological, empirical, and critical literature rather than claiming systematic completeness or pooled effect estimation. It focuses on how compound annotation, target prediction, disease-network mapping, protein–protein interaction analysis, pathway enrichment, and multi-layer interpretation contribute to multi-target discovery and therapeutic mechanism hypothesis generation. The synthesis emphasizes that herbal complexity provides a rationale for network-level analysis, but does not by itself establish pharmacological activity, therapeutic synergy, causal mechanism, or clinical effectiveness. Across the reviewed literature, network pharmacology offers strengths in evidence organization, target prioritization, disease-module exploration, pathway-level interpretation, and experimental planning. However, recurrent weaknesses include database dependency, annotation bias, uncertain target prediction, popular-target and degree bias, limited exposure context, insufficient tissue specificity, pathway redundancy, workflow opacity, and overinterpretation of computational convergence. The review argues that more reliable herbal network pharmacology requires evidence-weighted compound characterization, transparent database provenance, uncertainty-aware target inference, context-specific disease mapping, orthogonal experimental validation, pharmacokinetic plausibility, reproducible computational workflows, and translation-oriented interpretation. Network pharmacology should therefore be understood as a hypothesis-prioritization framework that can guide mechanistic research, not as an independent substitute for experimental or clinical evidence.