Molecular Docking and Structure-Based Drug Design

Abstract

Computer-aided drug designing (CADD) relates to drug discovery, also characterized as a cost-effective and active tool that manages or creates theoretical models that would be used by large databases for discovery and virtual screening. Till now, several algorithms have been developed and managed through CADD to study different prospects like protein structure and function prediction, identification of ligands interaction, residues of the active site, and study of protein–ligand interactions, which possibly leads to the discovery of newer therapeutic agents or drugs. As per in terms of new medicine discovery, designing and binding of small molecules (ligand) with DNA, RNA, or protein (target) is the key step, defined as docking. Docking actively identifies specific hit from large data libraries through simple rigid or flexible docking approaches with the receptor to maximize hit rates in virtual screening. The calculated scores of free energy of binding (poses) define the active compounds involved in interactions. Different new prospects in docking programs are now being used that more focuses accuracy on molecular interaction energy calculation without stringent parameters. The quantum-chemical methods, implicit solvent models, and new global optimization algorithms are now being used to improve flexibility and mobility of ligands and proteins, respectively. This chapter presents some basic algorithms, molecular docking programs based on rigid and flexible receptor/ligand-based on various machine learning techniques used in CADD and molecular docking.