  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 model's performance. Inspired by
the reaction type label and ensemble learning, we proposed a novel weak
ensemble method to enhance diversity. We combined beam search, nucleus, and
top-k sampling methods to further improve inference diversity and proposed a
simple ranking algorithm to retrieve the final top-10 results. We achieved new
state-of-the-art results on both the USPTO-50K dataset, with top1 accuracy of
54%, and the larger data set USPTO-full, with top1 accuracy of 50%, and
competitive top-10 results.
