Molecular and structure-based drug design: From theory to practice.

Abstract

Structure-based drug design (SBDD) and molecular docking have revolutionized drug discovery by providing effective strategies for identifying and optimizing therapeutic agents. This review highlights the principles and methodologies of SBDD, which uses high-resolution structural data of biological targets to design drugs with enhanced selectivity and efficacy. Techniques like nuclear magnetic resonance (NMR) spectroscopy, cryo-electron microscopy (cryo-EM), and X-ray crystallography are key in providing the structural information necessary for SBDD. Molecular docking, a crucial component of modern drug discovery, simulates interactions between drug candidates and biological targets. By predicting how a ligand fits into a receptor's binding site, researchers can assess the strength and nature of these interactions, guiding compound selection. Advances in molecular docking have incorporated machine learning to improve scoring functions and prediction accuracy. Docking studies include search algorithms, scoring functions, and binding site identification to predict the optimal orientation of a ligand when bound to a protein. Despite its widespread use, molecular docking has limitations, such as challenges in achieving high prediction accuracy, modeling protein flexibility, and accounting for solvation effects. Improvements in computational power and the integration of machine learning techniques are addressing these issues. This review emphasizes the importance of ongoing innovation and interdisciplinary collaboration in enhancing molecular docking and its role in discovering novel therapies.