Molecular Docking: A Comprehensive Understanding of Trends, AI Integration and Future Perspectives in Drug Discovery

Molecular docking has become an essential tool in modern drug discovery and structure-based drug design (SBDD) because it helps Predict how a small molecule interacts with target proteins quickly and cost effectively. Over time, docking method have advanced from simple rigid docking approaches to flexible and AI-assisted computational workflows. This review presents a comprehensive overview of molecular docking Including its principles, workflows, major software tools, databases, applications, challenges and future perspectives. Important docking tool Such as AutoDock Vina, GOLD, Glide, GNINA, PyRx and Discovery Studio are discussed along with major databases including PDB, ZINC, PubChem, DrugBank, ChEMBL and binding DB. The review also highlights the growing role of artificial intelligence, machine learning, deep learning and AlphaFold-based structure prediction in improving docking accuracy, Virtual screening, binding affinity prediction and De novo Drug design. Applications of molecular docking in cancer, infectious diseases, neurodegenerative disorders, Drug repurposing and natural product research are also summarised. Despite major advancements, challenges such as receptor flexibility, scoring inaccuracies, computational cost and limited interpretability of AI model still remain. Overall, the integration of molecular docking with AI and advanced computational technologies is expected to accelerate and improve future drug discovery workflows.