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Transformer-based deep neural networks have revolutionized the field ofmolecular-related prediction tasks by treating molecules as symbolic sequences.These models have been successfully applied in various organic chemicalapplications by pretraining them with extensive compound libraries andsubsequently fine-tuning them with smaller in-house datasets for specifictasks. However, many conventional methods primarily focus on single molecules,with limited exploration of pretraining for reactions involving multiplemolecules. In this paper, we propose ReactionT5, a novel model that leveragespretraining on the Open Reaction Database (ORD), a publicly availablelarge-scale resource. We further fine-tune this model for yield prediction andproduct prediction tasks, demonstrating its impressive performance even withlimited fine-tuning data compared to traditional models. The pre-trainedReactionT5 model is publicly accessible on the Hugging Face platform.

ReactionT5: a large-scale pre-trained model towards application of limited reaction data