Molecular docking approaches in drug repurposing: A mini review

Abstract

Drug repurposing has emerged as an effective strategy to accelerate the drug discovery process by identifying new therapeutic applications for existing drugs. Among the various computational techniques employed in drug repurposing, molecular docking has gained significant importance due to its ability to predict ligand–protein interactions, binding affinity, and molecular stability with high efficiency and reduced cost. The present mini review highlights the fundamental principles, methodologies, and applications of molecular docking in drug repurposing research. Different docking approaches, including rigid docking, flexible docking, and induced-fit docking, along with commonly used software such as AutoDock, Glide, GOLD, and Schrödinger Suite, are discussed. The review further emphasizes the wide applications of molecular docking in identifying potential therapeutics for infectious diseases, cancer, neurodegenerative disorders, and cardiovascular diseases. Particular attention is given to the role of docking studies during the COVID-19 pandemic, where several approved drugs were rapidly screened against SARS-CoV-2 targets. In addition, recent advances involving artificial intelligence, machine learning, molecular dynamics simulations, network pharmacology, and structural bioinformatics have considerably improved the accuracy and efficiency of docking-based drug repurposing studies. Despite these advancements, challenges such as protein flexibility, inaccuracies in scoring functions, computational limitations, and false-positive predictions remain major concerns. Therefore, integration of docking with complementary computational and experimental approaches is essential for improving prediction reliability and therapeutic validation. Overall, molecular docking continues to serve as a powerful and economical tool in modern drug repurposing research, offering promising opportunities for accelerated drug discovery and precision medicine.