Advances in Computational Chemistry for Drug Discovery

Abstract

The integration of computational chemistry into drug discovery has significantly enhanced the efficiency and precision of developing new therapeutics. This review provides a comprehensive overview of the evolution and key techniques in computational chemistry, including molecular docking, molecular dynamics simulations, quantum mechanics/molecular mechanics (QM/MM) methods, pharmacophore modeling, and quantitative structure-activity relationship (QSAR) modeling. The application of these techniques in virtual screening, lead optimization, drug-target interaction predictions, and drug repurposing is discussed, highlighting their impact on the drug discovery process. The review also explores the role of artificial intelligence (AI) and machine learning (ML) in advancing predictive modeling and accelerating drug design, emphasizing the challenges associated with computational costs, prediction accuracy, data quality, and ethical considerations. Furthermore, emerging trends in the field, such as quantum computing, personalized medicine, and the integration of computational methods with experimental techniques, are examined. The importance of open science and collaborative platforms in democratizing drug discovery is also addressed. In conclusion, while computational chemistry has already revolutionized drug discovery, ongoing advancements in technology and methodology promise to further transform the field, enabling more targeted and efficient therapeutic development.