Advances in drug design and discovery: A Comprehensive Review

Abstract

The process of drug discovery and development is a complex, time-consuming, and costly endeavor. However, the integration of machine learning (ML) and Artificial Intelligence (AI) has revolutionized the pharmaceutical industry by providing innovative solutions to challenging problems. This review highlights the role of ML in drug discovery, including target identification, lead optimization, and drug design. ML-driven approaches, such as deep learning and neural networks, have accelerated the discovery process, reducing the time and expense involved in traditional drug development. Computational tools and software have made the drug research and development process more convenient, enabling the use of online screening, structure-based design, and lead optimization. The application of ML in drug discovery has the potential to transform the pharmaceutical industry, enabling the development of novel and effective therapies for various diseases. This review aims to provide an overview of the current state of ML in drug discovery, highlighting its applications, advantages, and future directions.