Artificial Intelligence for Chemical Synthesis: Tools, Challenges, and Emerging Trends

Abstract

Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial intelligence is transforming the field, helping to predict molecular behavior, find potential designs, and automate certain aspects of the discovery process, introducing a new kind of intuition. Over the past decade, advances in machine learning, natural language processing, robotics, and automation have enabled new areas of research. These are broadening the applications for retrosynthetic analysis, reaction optimization, and computer-aided synthetic planning. This study examines the evolution of computer-aided synthesis, describing its development from rule-based approaches to advanced deep learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid hypothesis generation, reaction screening, and the improvement of synthetic methods. The review encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver discoveries more quickly and minimize waste and environmental impact. It examines current challenges, such as data scarcity, sporadic reporting, model interpretability, and practical applications. More broadly, the need for sustainable, collaborative research has increased, and cross-border work through cloud-based laboratories and shared databases enables chemists worldwide to share resources. The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition, bringing together research that traditional methods would never have allowed, on a scale never before possible without AI.