ReactionT5: a large-scale pre-trained model towards application of limited reaction data
This work addresses the problem of applying deep learning to chemical reaction prediction with limited data, offering a domain-specific advancement for computational chemistry.
The paper tackled the limited exploration of pretraining for chemical reactions involving multiple molecules by proposing ReactionT5, a model pretrained on the Open Reaction Database, and demonstrated impressive performance in yield and product prediction tasks with limited fine-tuning data compared to traditional models.
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.