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We present an extension of our Molecular Transformer architecture combinedwith a hyper-graph exploration strategy for automatic retrosynthesis routeplanning without human intervention. The single-step retrosynthetic model setsa new state of the art for predicting reactants as well as reagents, solventsand catalysts for each retrosynthetic step. We introduce new metrics (coverage,class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluatethe single-step retrosynthetic models, using the forward prediction and areaction classification model always based on the transformer architecture. Thehypergraph is constructed on the fly, and the nodes are filtered and furtherexpanded based on a Bayesian-like probability. We critically assessed theend-to-end framework with several retrosynthesis examples from literature andacademic exams. Overall, the frameworks has a very good performance with fewweaknesses due to the bias induced during the training process. The use of thenewly introduced metrics opens up the possibility to optimize entireretrosynthetic frameworks through focusing on the performance of thesingle-step model only.

Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy