AI-Driven Optimization of Drug Synthesis Pathways

The optimization of drug synthesis pathways is a critical challenge in pharmaceutical research, requiring efficient strategies to enhance yield, reduce costs, and minimize environmental impact. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, leveraging machine learning, reinforcement learning, and generative models to predict optimal reaction conditions, streamline multi-step synthesis, and identify novel synthetic routes. This study explores AI-driven methodologies for optimizing drug synthesis pathways, focusing on data-driven retrosynthetic analysis, reaction prediction models, and high-throughput screening simulations. By integrating AI with cheminformatics and quantum chemistry simulations, the research aims to accelerate the drug development process, improve reaction efficiency, and reduce reliance on trial-and-error experimentation. The findings highlight the potential of AI in revolutionizing pharmaceutical synthesis, ultimately leading to more sustainable and cost-effective drug production.