Assessing molecular docking tools: understanding drug discovery and design

Background

In this twenty-first century, artificial intelligence and computational-based studies, i.e., pharmaceutical biotechnology, are more important in every field, even in the field of drug discovery, design, and development, and they should be for managing time, cost, energy, and the environment, as well as chemical consumption in laboratories and research centers.

Main Body of the Abstract

The study of molecular docking with relative components like classifications, models, and different types of approaches and techniques involved, pose generation, scoring function advantages, disadvantages, and comparison with other types of docking tools to conduct the molecular docking with steps involved will be helpful to learn computer-aided drug design and artificial intelligence. The mechanism involved and the step-by-step procedure of molecular docking are well elaborated and understandable. The applications of molecular docking to finding new ligands and affinities, optimizing drug candidates, and understanding molecular interactions with different receptors like proteins and enzymes for the treatment of many diseases, viz. cancer, SARS-COVID, inflammation, gravis, glaucoma, Alzheimer’s disease, and bacterial infections, make it faster and cheaper than traditional screening. The almost or maximum molecular docking software, as applicable to the specialization in this study of drug discovery, design, and development, includes DOCK, GOLD, GLIDE, MOE, Schrödinger, FlexX, AutoDock, Hammerhead, AutoDock Vina, SwissDock, PyMol, MVD, BIOVIA, MEGADOCK, etc.

Short Conclusion

Overall, in the field of computational chemistry and pharmaceutical biotechnology, i.e., computational-aided drug design, molecular docking plays an important role in designing and developing a drug molecule to save time, cost, energy, and the environment. With its origins firmly rooted in the developments in computational chemistry and structural biology, molecular docking has evolved from a theoretical concept into an intricate and vital tool in contemporary drug discovery.