Computational strategies for drug discovery: Harnessing Indian medicinal plants

Abstract

Indian medicinal plants have been a cornerstone of traditional medicine, offering a wealth of bioactive compounds with significant therapeutic potential. However, their integration into modern drug discovery processes remains underexplored. This study leverages advanced computational techniques, including ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, network pharmacology, molecular docking, and molecular dynamics simulations, to identify and characterize bioactive compounds from Indian medicinal plants. Through a systematic approach, we compiled a database of these compounds, assessed their pharmacokinetic properties, and predicted their interactions with target proteins implicated in various diseases. The ADMET analysis facilitated the prediction of the pharmacokinetic profiles, ensuring the selection of compounds with favorable absorption, distribution, metabolism, excretion, and toxicity characteristics. Network pharmacology provided insights into the multi-target effects of these compounds, elucidating their mechanisms of action within biological systems. Molecular docking predicted the binding affinities and modes of selected compounds with target proteins, while molecular dynamics simulations validated and refined these interactions, ensuring their stability and efficacy. This integrative approach not only accelerates the discovery of novel drug candidates but also bridges the gap between traditional knowledge and contemporary science, fostering the development of effective and culturally resonant therapies. Our findings highlight the potential of Indian medicinal plants as a rich source of new drug candidates, paving the way for innovative therapeutic solutions.