Classical Docking to Machine Learning Based Docking: Molecular Docking in Drug Discovery

Molecular docking has emerged as a cornerstone methodology in computational drug discovery, enabling the prediction of ligand-receptor interactions with considerable accuracy and efficiency. This article provides a comprehensive overview of docking fundamentals, including its workflow, scoring functions, and various types, ranging from rigid to flexible and ensemble docking approaches. Docking serves as an essential tool for virtual screening, lead optimization, and structure-based drug design, significantly reducing experimental costs and accelerating the identification of therapeutic candidates. The review details contemporary scoring strategies such as force-field-based, empirical, knowledge-based, and consensus scoring, highlighting their respective strengths and documented limitations. Additionally, a comparative evaluation of widely used docking platforms such as AutoDock, MOE, GOLD, Glide, and MVD is presented, incorporating recent benchmarking results and practical considerations. Special emphasis is placed on the integration of molecular docking with machine learning, artificial intelligence, molecular dynamics simulations, and other computational methods. Innovations such as deep learning architectures, AlphaFold-based structural modeling, reinforcement learning, and cloud-based high-throughput screening are redefining the predictive power, scalability, and clinical relevance of docking. Applications extend across drug discovery, drug repurposing, natural product research, and personalized medicine. The article also discusses critical challenges such as protein flexibility and scoring inaccuracies, and reviews emerging hybrid solutions designed to enhance accuracy and reliability. The review underscores the transformative impact of molecular docking in modern drug development.