A Graph to Graphs Framework for Retrosynthesis Prediction
This addresses the computational expense and coverage issues in retrosynthesis prediction for computational chemistry, offering a more scalable solution without requiring domain knowledge.
The paper tackles retrosynthesis prediction by proposing a template-free approach called G2Gs that transforms a target molecular graph into reactant graphs, achieving up to 63% higher top-1 accuracy than existing template-free methods and performance close to state-of-the-art template-based approaches.
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.