Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing.

ABSTRACT: Retrosynthesis planning, the process of identifying a set of available reactions to synthesize the target molecules, remains a major challenge in organic synthesis. Recently, computer-aided synthesis planning has gained renewed interest and various retrosynthesis prediction algorithms based on deep learning have been proposed. However, most existing methods are limited to the applicability and interpretability of model predictions, and further improvement of predictive accuracy to a more practical level is still required. In this work, inspired by the arrow-pushing formalism in chemical reaction mechanisms, we present an end-to-end architecture for retrosynthesis prediction called Graph2Edits. Specifically, Graph2Edits is based on graph neural network to predict the edits of the product gr

SUBMITTER: Zhong W 

PROVIDER: S-EPMC10209957 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature