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This narrative review examines the application of network pharmacology in phytocompound-based drug discovery, focusing on methodologies for identifying active phytochemicals, screening bioactive compounds, and elucidating complex interactions. It covers experimental workflows, computational and database network pharmacology, and validation approaches. The review highlights advancements in bioactivity screening, pharmacochemical repositories, and omics frameworks, supported by case studies demonstrating the delineation of herbal medicine action and disease network therapeutic fusions.
Network pharmacology refines the methodologies used to elucidate functional polypharmacy and phytocompounds targeted complex interactions.
Background: The way drug discovery is conducted has evolved from a singular focus on monotherapy to a more advanced approach which utilizes polypharmacology. The development of such an approach is critical to drug development, especially to drug development which is based on phytocompounds which targets multiple pathways at once. Objective: The aim of this paper is to conduct a narrative review on the use of network pharmacology poly pharmaceutical approaches to phytocompounds, starting from molecular profiling to therapeutic use. Methods: We conduct a narrative review on the current approach to the identification of active phytochemicals, bioactive compound screening, and drug discovery by network pharmacology. The review details experimental workflows, computational network pharmacology, database network pharmacology, and validation approaches which network pharmacology to drug development. Results: The application of network pharmacology to drug discovery has resulted in the refinement of methodologies used to elucidate functional polypharmacy and phytocompounds targeted complex interactions. Notable refinements advanced in bioactivity screening, linkable pharmacochemical repositories, and holistic omics frameworks. Multiple case studies show the delineation of herbal medicine action and the discovery of connected disease network therapeutic fusions. Synthesizing current bioinformatics enables rational, structure-based prediction of the protein in gene-protein interactions