Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR)

Abstract

The procedure for learning and creating a new medicine is widely seen as a drawn-out and costly endeavor. Different rational strategies are considered, depending on their requirements, as potential ways; nevertheless, techniques to designing drugs based on structure and ligands are well acknowledged as very practical and potent tactics in drug discovery. Computational approaches help decrease the need for Medicinal research with animals, helping to develop fresh, safe therapeutic concepts via rational design and positioning of existing products and supporting pharmaceutical scientists and medicinal chemists during the medication development process. Computer-aided drug discovery (CADD) methods are useful for reducing the time and cost of drug discovery and development and understanding the molecular mechanisms of drug action and toxicity. Molecular docking is a technique that predicts a ligand's binding mode and affinity to a target protein. At the same time, QSAR is a technique that establishes mathematical relationships between the structural features and biological activities of a series of compounds. This study reviews the current state and applications of CADD methods, focusing on molecular docking and quantitative structure–activity relationship (QSAR) techniques. This study reviews the principles, advantages, limitations, and challenges of these methods, as well as some recent advances and examples of their applications in drug discovery for various diseases. The study also discusses the future prospects and directions of CADD methods in the era of big data and artificial intelligence.