Machine Learning Application in Drug Discovery

Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on its transformative impact across various stages of the drug development pipeline. We delve into key ML methodologies, including supervised and unsupervised learning, neural networks, and reinforcement learning, examining their underlying principles and specific contributions to drug discovery processes. By exploring case studies and recent advancements, this review illustrates how ML algorithms have been utilized to predict drug-target interactions, optimize drug design, and streamline clinical trial processes. Furthermore, we discuss the challenges and limitations of implementing ML techniques in this field and highlight emerging trends and future directions. This review aims to offer researchers a thorough understanding of ML's potential to revolutionize drug discovery and equip them with the insights needed to leverage these technologies effectively.