Interpretable Retrosynthesis Prediction in Two Steps
2020-02-25T07:59:01Z (GMT) by
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by ﬁrst identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identiﬁed disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a signiﬁcant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation.