Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.

ABSTRACT: Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions o

SUBMITTER: Li SW 

PROVIDER: S-EPMC10272164 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature