Innovations in Molecular Docking: A Detailed Analysis of Methodological Developments and Their Applications in Drug Discovery

Abstract

An in-depth analysis of recent developments in molecular docking is presented in this review. Particular attention is paid to the influence that these methodological breakthroughs have had on the process of drug discovery. Our investigation focuses on the development of docking algorithms, which includes the enhancement of scoring functions, sampling strategies, and flexibility modeling systems. In this article, we address the incorporation of machine learning and artificial intelligence into docking procedures, focusing on the role that these technologies play in improving the accuracy of predictions and the efficiency of computation processes. When it comes to protein flexibility and dynamics, we investigate the evolution of ensemble-based approaches and their capacity to take these factors into account. The paper also discusses recent developments in fragment-based docking and how it might be applied to optimizing leads. To develop docking techniques that provide a more precise depiction of molecular interactions, we investigate the possibility of including quantum mechanical calculations. It discusses how cloud-based platforms and high-performance computer solutions have become increasingly popular for large-scale virtual screening. In addition to this, we assess the development of techniques for protein-protein and protein-peptide docking methodologies. This review investigates how these developments have been utilized to work with difficult therapeutic targets, such as proteins that are intrinsically disordered and allosteric sites. In conclusion, we will talk about the incorporation of docking with many other computational and experimental methods in drug development pipelines. This will provide a forward-looking view of the potential and future directions of the discipline, as well as its potential impact on pharmaceutical research.