A Graph to Graphs Framework for Retrosynthesis Prediction

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang


<p>A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computational expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches with up to 63% improvement in terms of the top-1 accuracy and is close to the performance of state-of-the-art template-based approaches, but does not require domain knowledge and is much more scalable. </p>