Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation

Abstract

Chemical retrosynthesis has been a crucial and challenging task in organic chemistry for several decades. In early years, retrosynthesis is accomplished by the disconnection approach which is labor-intensive and requires expert knowledge. Afterward, rule-based methods have dominated in retrosynthesis for years. In this study, we revisit the disconnection approach by leveraging deep learning (DL) to boost its performance and increase the explainability of DL. Concretely, we propose a novel graph-based deep-learning framework, named DeRetro, to predict the set of reactants for a target product by executing the process of disconnection and reactant generation orderly. Experimental results report that DeRetro achieves new state-of-the-art performance in predicting the reactants. In-depth analyses also demonstrate that even without the reaction type as input, DeRetro retains its retrosynthesis performance while other methods show a significant decrease, resulting in a large margin of 19% between DeRetro and previous state-of-the-art rule-based method. These results have established DeRetro as a powerful and useful computational tool in solving the challenging problem of retrosynthetic analysis.