  We present an extension of our Molecular Transformer architecture 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 new 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 retrosynthesis examples from literature and
academic exams. Overall, the frameworks has a very good performance with few
weaknesses due to the bias induced during the training process. The use of the
newly introduced metrics opens up the possibility to optimize entire
retrosynthetic frameworks through focusing on the performance of the
single-step model only.
