Optimizing Drug Synthesis Driven by Artificial Intelligence
Daizhen Jiang · Theoretical and Natural Science · 2025
Artificial intelligence (AI) is reshaping the landscape of pharmaceutical research, particularly in the area of drug synthesis optimization. This paper explores how AI-powered tools—spanning machine learning, deep learning, and other advanced computational techniques—are revolutionizing traditional approaches to chemical synthesis. These technologies enable researchers to mine large chemical databases, accurately predict reaction outcomes, design synthetic pathways, and fine-tune reaction conditions. In doing so, AI addresses long-standing challenges such as high development costs, lengthy timelines, and low efficiency. However, the implementation of AI in drug synthesis is not without hurdles, including data quality concerns, the opaque nature of many models, and the evolving regulatory environment. This paper provides a comprehensive overview of current advancements, critical challenges, and future directions in AI-assisted drug synthesis.