Computational Approaches to Molecular Docking and Protein Modeling in Drug Discovery

Abstract

Protein modeling and molecular docking are crucial computational methods in contemporary drug discovery. To identify potential therapeutic possibilities with high affinity and specificity, molecular docking predicts the ideal binding interactions between tiny molecules (ligands). The best binding interactions between target macromolecules, such proteins, and tiny molecules, or ligands, are predicted by molecular docking. When experimental structures are not accessible, protein modeling—including homology modeling and ab initio techniques—allows for the creation of three-dimensional protein structures. By cutting down on time and expense, these methods work together to expedite the drug discovery process. related to experimental techniques. This review explores the principles of molecular docking, emphasizing key algorithms, scoring functions, and software tools like AutoDock Vina and Discovery Studio. Additionally, it highlights advancements in protein modeling approaches, such as AlphaFold and comparative modeling, and their integration with docking workflows. By using these computational approaches, researchers can effectively predict binding mechanisms, find lead compounds, and improve drug design. The growing increases integration between molecular docking, protein modeling, and artificial intelligence holds promise for more accurate predictions and faster drug development processes in the pharmaceutical industry. Keywords: Molecular Docking; Protein Modeling: AutoDock Vina.