Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery

Abstract

The field of Computational Medicinal Chemistry has undergone significant advancements, transitioning from traditional methodologies to contemporary strategies powered by artificial intelligence, machine learning, and big data. Traditional approaches, such as molecular docking and QSAR modeling, have long been the foundation of drug discovery, offering reliable frameworks for target identification and lead optimization. However, contemporary methodologies, including AI-driven target identification, adaptive virtual screening, and generative models, are reshaping the landscape by increasing efficiency and expanding chemical space exploration. This article provides a comprehensive comparison between these two paradigms, highlighting their respective strengths, limitations, and the potential of their integration. By bridging traditional and contemporary approaches, researchers can establish innovative workflows to accelerate drug discovery, ultimately contributing to the development of safer and more effective therapeutics.