Computational Pharmacology Meets Phytopharmacology: A Scoping Review of Network-Based, Structure-Based, and Omics-Driven Discovery Strategies

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Computational pharmacology is increasingly reshaping phytopharmacology by enabling structured interpretation of medicinal plants, phytochemicals, botanical mixtures, molecular targets, biological pathways, and mechanism-oriented evidence. This scoping review maps how network-based computational pharmacology, structure-based phytochemical screening, and omics-driven mechanistic discovery are being used to support natural-product and phytopharmacological research. The review uses a scoping logic rather than an effectiveness-synthesis design, emphasizing evidence mapping, methodological classification, data-source transparency, validation practices, integration patterns, and research gaps. Network-based approaches are examined for their use in compound–target mapping, target prediction, disease-module analysis, protein–protein interaction networks, pathway enrichment, polypharmacology, and synergy hypothesis generation. Structure-based strategies are assessed for their roles in virtual screening, molecular docking, binding-mode assessment, molecular dynamics, pharmacophore modeling, QSAR where relevant, and ADMET prioritization. Omics-driven approaches are considered in relation to transcriptomics, proteomics, metabolomics, multi-omics integration, response signatures, biomarker discovery, and mechanism-of-action inference. Across these domains, the review highlights that computational convergence can strengthen hypothesis prioritization but cannot independently establish pharmacological mechanism, therapeutic efficacy, safety, or clinical relevance. Major limitations include database dependency, target-prediction uncertainty, pathway redundancy, hub overinterpretation, structural-model assumptions, scoring-function limitations, omics confounding, incomplete validation, and weak reproducibility. The review contributes an integrated conceptual map for computational–experimental phytopharmacology and identifies future priorities centered on transparent workflows, uncertainty-aware inference, orthogonal validation, standardized botanical characterization, and translationally meaningful evidence integration.