Molecular Docking in Drug Development

Abstract

Molecular docking has emerged as a key computational technique in modern drug development, enabling the prediction of interactions between drug molecules (ligands) and biological targets (proteins). By estimating binding affinity and identifying the most favorable binding orientation, molecular docking supports structure-based drug design and significantly reduces the time, cost, and resources required for conventional drug discovery.The docking workflow includes target protein selection, protein and ligand preparation, active-site identification, docking simulation, scoring, and lead optimization. Depending on the study objectives, rigid, flexible, and semi-flexible docking approaches are employed using widely accepted software such as Auto Dock, Auto Dock Vina, Glide, GOLD, and Schrodinger Suite. Molecular docking has broad applications in lead identification, virtual screening, drug re purposing, and the discovery of novel therapeutic agents for cancer, infectious, and inflammatory diseases. Integration with ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction enables early evaluation of pharmacokinetic and toxicity profiles, while Artificial Intelligence (AI) enhances docking accuracy, virtual screening, and lead optimization. A representative case study involving novel thiazolidinone -piperazine hybrid compounds as potential dual COX-2 and 5-LOX inhibitors demonstrates the combined application of molecular docking and ADMET prediction in anti-inflammatory drug design. Although limitations such as scoring-function accuracy and protein flexibility remain, ongoing advances in AI, machine learning, and computational power continue to improve the reliability and efficiency of molecular docking. Consequently, molecular docking remains an indispensable tool for accelerating the discovery of safer, more effective, and targeted therapeutic agents in modern medicine.