RetroComposer: Discovering Novel Reactions by Composing Templates for Retrosynthesis Prediction
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The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from the limited training templates, which prevents them from discovering novel reactions. To overcome the limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. So far as we know, this is the first method that can find novel templates for retrosynthesis prediction. Besides, we propose an effective reactant candidates scoring model that can capture atom-level transformation information, and it helps our method outperform existing methods by a large margin. Experimental results show that our method can produce novel templates for 328 test reactions in the USPTO-50K dataset, including 21 test reactions that are not covered by the training templates.
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