Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.

ABSTRACT: We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several

SUBMITTER: Schwaller P 

PROVIDER: S-EPMC8152799 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature