A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data.

ABSTRACT: In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the p

SUBMITTER: Li B 

PROVIDER: S-EPMC10422736 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature