Computational Discovery of Plant Bioactive Compounds Targeting Cancer Signaling Pathways

Abstract

Cancer is still one of the major causes of morbidity and mortality around the world, and there is a need for safe and effective therapeutic approaches. The application of plant-derived bioactive compounds has gained considerable interest because these compounds are structurally diverse, have pharmacological properties, and can modulate various signaling pathways associated with cancer. The present review summarizes concisely the use of computational methods for the discovery of phytochemicals that hit important onco-signaling pathways, including PI3K/Akt/mTOR, MAPK/ERK, Wnt/β-catenin, NF-κB, JAK/STAT, and p53. The importance of major computational techniques, such as database mining, virtual screening, molecular docking, molecular dynamics simulation, binding free-energy calculation, ADMET prediction, network pharmacology, machine learning, and artificial intelligence, is discussed as a powerful set of techniques to speed up the plant-based drug discovery process. In representative computational studies, the phytochemicals curcumin, quercetin, resveratrol, epigallocatechin gallate (EGCG), berberine, luteolin, withaferin A, and genistein can be multi-target anticancer agents. The review also sheds light on some challenges, prospects, and the integration of computational approaches with experimental validation for the development of safe, effective, and precision-based plant-derived anticancer therapeutics.