Retrosynthesis Prediction Based on Graph Relation Network
Zhaoxu Dong, Chen Zhao, Qian Wang · 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2022
Retrosynthetic analysis is one of the most basic and commonly used methods for compound synthesis routes planning. In the process, the single-step synthesis prediction is the basis for predicting the synthesis route of the whole compound. With the wide application of computers in various disciplines, the use of computer-aided retrosynthetic process is becoming more and more common. The rise of artificial intelligence also makes more and more people apply pure data-driven deep learning models to retrosynthetic methods. At present, there are many deep learning-based methods to solve the problem of single-step retrosynthetic prediction. However, there is a lack of an end-to-end method using graph convolutional neural network for prediction. In this paper, we propose a template-based graph relation network for the prediction of single-step synthesis of compounds. The model can learn the coding of molecules and templates to predict whether there is a relationship between them. Therefore, the reactants of target molecules predicted by this model have great interpretability. In addition, in this experiment, we used a new dataset, which has a variety of reaction and template data, and further verified the practicability of the model.