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Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction

preprint
revised on 30.05.2019 and posted on 30.05.2019 by Philippe Schwaller, Teodoro Laino, Theophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.

Funding

Winton Programme for the Physics of Sustainability

History

Email Address of Submitting Author

phs@zurich.ibm.com

Institution

University of Cambridge / IBM Research Zurich

Country

UK / Switzerland

ORCID For Submitting Author

0000-0003-3046-6576

Declaration of Conflict of Interest

No conflict of interest

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