A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
[article]
2022
arXiv
pre-print
Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we propose a new graph-to-graph transformation model, G2GT, in which the graph encoder and graph decoder are built upon the standard transformer structure. We also show that self-training, a powerful data augmentation method that utilizes unlabeled molecule data, can significantly improve the
arXiv:2204.08608v1
fatcat:e4b7pjtqezbi3nwdcfz7ljijku