Reaction Center

Reaction centers, the key structural components within molecules undergoing chemical reactions, are the focus of intense research aimed at accurately identifying and predicting their behavior. Current efforts leverage graph neural networks and deep reinforcement learning algorithms, often incorporating self-supervised learning techniques trained on large reaction datasets, to improve the identification of single and multiple reaction centers, even in complex reactions. These advancements are crucial for improving retrosynthesis prediction—the process of determining starting materials from a desired product—and have implications for drug discovery, materials science, and other fields reliant on efficient chemical synthesis. The development of more robust and accurate reaction center identification methods promises to significantly accelerate the design and synthesis of novel molecules.