MACHINE LEARNING STRATEGIES FOR DRUG DISCOVERY AND DEVELOPMENT

This review paper provides a comprehensive overview of the role of machine learning (ML) in drug discovery and development within the pharmaceutical industry. It begins by outlining the foundational concepts of machine learning, highlighting its ability to enhance decision-making and improve accuracy through data analysis. The paper emphasizes the growing adoption of ML techniques across the pharmaceutical sector, showcasing their potential to streamline drug discovery processes, reduce costs, and minimize reliance on animal testing. It categorizes various machine learning methods, such as supervised, unsupervised, semi-supervised, and reinforcement learning, and discusses their applications in drug discovery, including predicting drug efficacy, optimizing lead compounds, and validating safety biomarkers. Furthermore, the paper delves into advanced ML techniques like transfer learning, multitask learning, and active learning, which address challenges related to data scarcity and enhance model performance. The discussion also covers specific algorithms such as Support Vector Machines, Decision Trees, and Artificial Neural Networks, illustrating their utility in predicting biological properties and improving drug design. Ultimately, the paper concludes that the integration of machine learning in drug discovery promises to enhance efficiency and accuracy and heralds a new era of innovation in pharmaceutical research and development.