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.

利用深度神经网络中的基于 Transformer 的方法，在分子相关的预测任务中，通过将分子视为符号序列，解决了多种有机化学应用问题，并利用公开资源 Open Reaction Database (ORD) 对预训练进行了改进，进一步通过有限的微调数据展示了预训练模型的出色性能，ReactionT5 模型的预训练模型在 Hugging Face 平台上公开可用。