Natural products have historically been integral to pharmacotherapy, attributed to their remarkable structural diversity and evolutionary refinement for biological interactions. Nonetheless, traditional natural product-based drug discovery has faced challenges such as mechanistic ambiguity, scalability limitations, and inadequate translational predictability. Concurrently, reductionist single-target approaches have been insufficient for addressing complex diseases characterized by network-level dysregulations. Recent advancements in analytical chemistry, genomics, and data-driven methodologies have rejuvenated natural product research by facilitating rapid structural elucidation, systematic exploration of biosynthetic diversity, and rational prioritization of bioactive compounds. Notably, many natural products exhibit multitarget effects that necessitate interpretation beyond isolated molecular interactions. Systems pharmacology offers a quantitative framework to analyze such network-level perturbations by integrating omics data, computational modeling, and experimental validation. However, molecular and computational predictions alone do not suffice to establish therapeutic relevance. Experimental pathology, encompassing histopathology, immunohistochemistry, spatial analysis, and ultrastructural evaluation, remains essential for validating efficacy and safety at tissue and organ levels. This review synthesizes technological innovation, systems pharmacology, and pathological validation to reposition natural products as mechanistically grounded and translationally robust resources for contemporary drug discovery.
Nakadate et al. (2026) studied this question.