Our GLN provides a general graphical model to retrosynthesis problem, which is compatible with many reasonable

Neural Information Processing Systems 

We thank the reviewers for their insightful comments, which we will incorporate into the revised version. We adopt the s2v in our paper since it satisfies these requirements. We will elaborate on the details in our revision. The results are presented in Table 2. Despite the noisiness of the full So our GLN could be further improved with better design choices. We emphasize that the proposed GLN is general enough which is compatible with other parametrizations.

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