Molecular Docking and Computational In Silico Investigations of Metal-Based Drug Agents

The in silico computational techniques that include pharmacophores, databases, homology models, quantitative structure-activity relationships, AI machine learning, data mining and other molecular modeling approaches, data analysis, and network analysis tools are used to predict the hypothesis and validation of binding affinity and modes of drug candidates and to integrate experimental in vitro data to create the computational model or simulation. Such models have been used in the discovery and optimization of novel ‘lead’ therapeutics molecules in medicine and predict their affinity to a specific target and their pharmacodynamic nature. Molecular docking is a structure-based in silico computational method, that involves predicting and validating the specific interactions (binding mode and affinity) between small molecules and larger biomolecules such as enzymes, receptors, RNA, DNA, and other proteins at a subatomic level. The commonly employed docking software methods that are used often are AutoDock, Discovery Studio, AutoDock Vina, Surfex, Glide, Autodock GOLD, FlexX, LeDock, DOCK, rDock, FRED, LigandFit, UCSF Dock, ICM, etc. Among these Glide, GOLD, and AutoDock Vina have been recognized as top-performing choices, often providing the accurate results.