Transformer-based multitask learning for reaction prediction under low-resource circumstances.

ABSTRACT: Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer-assisted predictions based on low-resource chemical datasets generally have low accuracy despite the exceptional ability of deep learning in retrosynthesis and synthesis. To address this issue, we introduce two types of multitask models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT). These models integrate multitask learning with the transformer model to predict low-resource reactions in forward reaction prediction and retrosynthesis. Our results demonstrate that introducing mul

SUBMITTER: Qiao H 

PROVIDER: S-EPMC9641703 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature