Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning.

ABSTRACT: Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset con

SUBMITTER: Yin X 

PROVIDER: S-EPMC10777739 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature