Computational Modeling of Protein-Ligand Interactions for Drug Discovery

Abstract

Computational modeling of protein-ligand interactions plays a crucial role in drug discovery by enabling the prediction of binding affinities, identification of potential drug candidates, and optimization of lead compounds. This approach leverages molecular docking, molecular dynamics simulations, and other computational techniques to understand how small molecules interact with target proteins. In this article, we review the key computational methods used to model protein-ligand interactions, including their applications in virtual screening, drug optimization, and mechanistic studies. We also discuss the challenges and future directions in computational drug discovery, emphasizing the role of artificial intelligence and machine learning in enhancing predictive models.