Recent Advances in Molecular Docking Techniques: Transforming Perspectives in Distinct Drug Targeting and Drug Discovery Approaches

INTRODUCTION: Drug targeting and drug discovery methodologies are advancing rapidly due to recent developments in molecular docking techniques. Molecular docking forecasts the interactions between a small molecule, such as a potential medicine, and a target protein or receptor. OBJECTIVES: This comprehensive review focuses on significant advances in molecular docking algorithms such as Vina, Glide, and AutoDock, including their enhanced accuracy and efficiency in predicting drug-target interactions. It also examines how novel features, such as fragment-based docking, covalent docking, and virtual screening, have expanded the significance of docking in modern pharmaceutical research. METHODS: The literature search was carried out by employing search engines such as PubMed and Google Scholar with keywords such as Molecular Docking, Lead-Optimization, Protein Flexibility, Fragment-Based Docking, Covalent Docking, and Virtual Screening. RESULTS: This present state-of-the-art review highlights recent advances in various docking methodologies and their significant applications in drug discovery, while also discussing the scoring functions of some well-established studies. Furthermore, by predicting the interactions between putative medications and protein residues involved in the creation of covalent bonds, covalent docking provides new opportunities for targeting difficult drug-resistant mutations. The efficiency and precision of these simulations have been increased by improved sampling techniques and sophisticated algorithms, enabling the investigation of conformational changes and protein flexibility throughout the drug-binding process. CONCLUSION: These approaches may hasten the course of emerging new remedies, increase the precision of hit-finding, and make it easier to find cutting-edge treatments for a variety of diseases. Molecular docking alone is insufficient to ensure the safety and efficacy of a pharmacological agent for commercialization. While it predicts binding affinity and interaction, it does not account for pharmacokinetics, toxicity, off-target effects, or in vivo behavior. Therefore, experimental validation through MD simulation, ADMET, in vitro, in vivo, and clinical studies is essential.