The Role of Molecular Docking in Drug Discovery: Current Approaches and Innovations

Abstract

Molecular docking is a crucial computational technique used to predict the binding mode and affinity between ligands and target macromolecules, playing an integral role in drug discovery and development. This review delves into the methodologies, challenges, and advancements in molecular docking, emphasizing its application in virtual screening, drug repurposing, and lead optimization. The process of molecular docking relies on accurate pose and affinity predictions, which are influenced by the choice of docking program, test set diversity, and algorithmic approaches. Various docking software tools, each with their strengths and limitations, are compared to highlight their performance in different contexts. The review also explores recent trends in molecular docking, particularly its contribution to combating emerging diseases like COVID-19. Additionally, it addresses the integration of deep learning (DL) algorithms into docking experiments, enhancing pose selection and improving the accuracy of binding affinity predictions. Despite the progress, the identification of near-native binding poses remains a significant challenge, with current scoring functions often falling short in predicting the correct ligand conformation. As molecular docking methods evolve, the combination of computational techniques and DL-driven innovations holds great promise for more efficient and precise drug discovery. This review aims to provide valuable insights for researchers and clinicians in the bioinformatics and pharmaceutical industries, offering guidance on the use of docking techniques to design and optimize therapeutic agents for various diseases.