Chapter 5 : AI-Powered Reaction Prediction and Retrosynthesis: A Paradigm Shift in Synthetic Chemistry

Background: Reaction prediction and retrosynthetic analysis have traditionally relied on expert knowledge and rule-based systems, which are limited in their ability to navigate the vast and complex chemical reaction space. Growing demands in drug discovery, materials science, and sustainable chemistry require faster, more scalable approaches. Artificial intelligence has emerged as a transformative framework for data-driven reaction modeling and synthesis planning. Objective: This chapter aims to examine how AI-driven models reshape reaction prediction and retrosynthetic planning in modern synthetic chemistry. Method: A conceptual and analytical synthesis is presented, integrating chemical reaction theory with contemporary AI architectures. The chapter reviews neural network models, graph-based representations, reinforcement learning strategies, and benchmark datasets used for reaction prediction and retrosynthesis. Key findings: The analysis shows that AI models significantly improve prediction accuracy and route optimization compared to traditional rule-based methods. Transformer-based and graph neural network approaches enable effective reaction outcome prediction and reaction-center identification. Key limitations related to data quality, generalization, interpretability, and synthetic feasibility are critically discussed. Implications: The findings support the development of intelligent and potentially autonomous synthesis planning systems, with important implications for pharmaceutical development, green chemistry, and automated chemical manufacturing. How to cite: Kodiyatar, N. (2025). AI-Powered Reaction Prediction and Retrosynthesis: A Paradigm Shift in Synthetic Chemistry. Zenodo. https://doi.org/10.5281/zenodo.15504308