Integrated Computational Approaches for Natural Product-Based Drug Discovery: Target Prediction, Network Pharmacology, Molecular Docking, and ADMET Prediction
TL;DR
An integrated overview of target prediction, network pharmacology, molecular docking, and ADMET prediction within a unified computational workflow for natural product-based drug discovery is provided to support more efficient, systematic, and evidence-based natural product-driven drug discovery.
Abstract
Natural products remain an important source of drug discovery, while advances in computational approaches have accelerated early-stage candidate identification. This review aims to provide an integrated overview of target prediction, network pharmacology, molecular docking, and ADMET prediction within a unified computational workflow for natural product-based drug discovery. A narrative literature review was conducted by analyzing peer-reviewed articles published between 2015 and 2025 from major scientific databases. The reviewed studies consistently demonstrate that integrating complementary in silico approaches improves target identification, mechanism elucidation, lead compound prioritization, and pharmacokinetic assessment before experimental validation. This integrated workflow provides a practical framework to support more efficient, systematic, and evidence-based natural product-driven drug discovery.