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Issue 28, 2018
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“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

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Abstract

There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Based on this analogy, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a tokenization, which is arbitrarily extensible with reaction information. Using an attention-based model borrowed from human language translation, we improve the state-of-the-art solutions in reaction prediction on the top-1 accuracy by achieving 80.3% without relying on auxiliary knowledge, such as reaction templates or explicit atomic features. Also, a top-1 accuracy of 65.4% is reached on a larger and noisier dataset.

Graphical abstract: “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

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Supplementary files

Article information


Submitted
28 May 2018
Accepted
20 Jun 2018
First published
22 Jun 2018

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2018,9, 6091-6098
Article type
Edge Article

“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

P. Schwaller, T. Gaudin, D. Lányi, C. Bekas and T. Laino, Chem. Sci., 2018, 9, 6091
DOI: 10.1039/C8SC02339E

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