ReactionT5: a large-scale pre-trained model towards application of
  limited reaction data
Tatsuya Sagawa Ryosuke Kojima
Transformer-based deep neural networks have revolutionized the field of
molecular-related prediction tasks by treating molecules as symbolic sequences.
These models have been successfully applied in various organic chemical
applications by pretraining them with extensive compound libraries and
subsequently fine-tuning them with smaller in-house datasets for specific
tasks. However, many conventional methods primarily focus on single molecules,
with limited exploration of pretraining for reactions involving multiple
molecules. In this paper, we propose ReactionT5, a novel model that leverages
pretraining on the Open Reaction Database (ORD), a publicly available
large-scale resource. We further fine-tune this model for yield prediction and
product prediction tasks, demonstrating its impressive performance even with
limited fine-tuning data compared to traditional models. The pre-trained
ReactionT5 model is publicly accessible on the Hugging Face platform.