In this work, we propose a novel forward reaction prediction method. It is an end-to-end neural machine translation method which receives as input the molecular graphs of the reactants and reagents and outputs the SMILES representation of the products. Named Hybrid Graph-SMILES Transformer (HGST), the method addresses the weaknesses of existing reaction predictors while taking advantage of their strengths. In fact, compared to sequence-based models, HGST does not need SMILES randomization and data augmentation to achieves new state-of-the-art results on USPTO benchmarks. This is due to the permutation invariance property of molecular graphs which are taking as inputs, making the training and inference faster. Although stereo-isomeric information encoded in SMILES could be lost in graphs, our model solves the issue by directly incorporating the necessary stereo-isomeric information into the atom and bond features of the molecular graphs. Compared to graph-based models, there is no need to predefine an atom-mapping and solve the permutation invariance problem associated with graph prediction because the method predicts a SMILES.<br/>The main ingredient of HGST is its molecular graph attention encoder that allows intra- and inter-molecule, as well as long and short range, information exchange between atoms. This aspect is crucial for elucidating the reaction mechanism. In fact, HGST comprises an explainer that is able to automatically highlight atoms attended to, allowing an interpretability from the model perspective and showing atoms that are important and involved in the reactions. These reaction centres are often provided as inputs for the graph-based models and very difficult to identify in sequence-based models due to the complex syntax of SMILES. However, in our model, we get probable reaction centre identifications and potential electron movements simply as a byproduct of our modelling choices.

Chemical reaction prediction with hybrid graph-SMILES transformers

ACS Spring Meeting 2021

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