How ML transforms drug discovery

The process of drug discovery focuses on identifying novel compounds with specific chemical properties to treat various diseases. In recent years, the field has increasingly incorporated computational techniques, driven by the rapid rise of machine learning technologies and their widespread accessibility. With the goals outlined by the Precision Medicine initiative and the emerging challenges in drug discovery, there is a growing need for robust, standardized, and reproducible computational methodologies to meet these objectives. Machine learning-based predictive models have become particularly significant in the early stages of drug development, prior to preclinical studies, offering the potential to significantly reduce both costs and research timelines. This review article explores the application of these advanced methodologies in recent research, providing insights into the current state of the field. By examining recent advancements, it aims to shed light on the future direction of cheminformatics, its limitations, and the positive outcomes achieved so far.