Optimizing Drug Synthesis: AI-Powered Kinetics Study in Pharmaceutical Research: Characterization, Restoration and Optimization

The landscape of drug discovery has undergone a transformative shift in recent years, with artificial intelligence (AI) and machine learning (ML) emerging as indispensable tools. This book chapter delves into the dynamic and increasingly crucial field of employing AI/ML-based models to unravel the intricate kinetics of drug synthesis routes. Efficient drug synthesis is an essential component of the drug development process. Accurate prediction and analysis of reaction kinetics are fundamental to achieving this goal. Traditional experimental methods are often time-consuming, resource-intensive, and prone to human error. AI and ML, however, offer a powerful alternative, allowing us to expedite the synthesis process, reduce costs, and enhance precision. This chapter explores the diverse applications of AI/ML in the field, from predicting reaction rates to optimizing reaction conditions. It examines the role of deep learning algorithms in understanding reaction mechanisms and how AI/ML-driven kinetic models enable researchers to make informed decisions during drug synthesis. Moreover, it highlights the potential for data-driven drug discovery by harnessing large datasets and the capabilities of AI/ML in predicting optimal synthetic routes, thereby accelerating the development of novel pharmaceuticals. As we navigate the era of data-driven drug discovery, this chapter provides a comprehensive overview of the promises and challenges of AI/ML-driven kinetics in drug synthesis, emphasizing their potential to reshape the future of pharmaceutical research and development.