Timezone: »

 
Poster
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi · Minkai Xu · Hongyu Guo · Ming Zhang · Jian Tang

Thu Jul 16 05:00 PM -- 05:45 PM & Fri Jul 17 04:00 AM -- 04:45 AM (PDT) @
in Poster Session 49 »

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 computationally 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 by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template-based approaches, but does not require domain knowledge and is much more scalable.

Keywords: Algorithms · Computational Biology and Genomics · Deep Generative Models · Applications - Neuroscience, Cognitive Science, Biology and Health

Author Information

Chence Shi (Peking University)
Minkai Xu (Shanghai Jiao Tong university)
Hongyu Guo (National Research Council Canada)
Ming Zhang (Peking University)
Jian Tang (HEC Montreal & MILA)

More from the Same Authors