Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.

However, most of them are cumbersome and lack interpretability about their predictions.

In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.

Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model.

While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.

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1 results in Junzhou Huang in Authors: Junzhou Huang

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

1 results in Authors: Junzhou Huang