4.6 Article

Fast prediction of distances between synthetic routes with deep learning

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Publisher

IOP Publishing Ltd
DOI: 10.1088/2632-2153/ac4a91

Keywords

synthetic routes; machine learning; tree edit distance; reaction informatics

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This article expands on recent research on clustering synthetic routes and trains a deep learning model to predict distances between different routes. The machine learning approach used in this study is considerably faster than the traditional tree edit distance method and allows for clustering a greater number of routes with similar results. The developed model is also open-source.
We expand the recent work on clustering of synthetic routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on a long short-term memory representation of a synthetic route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The machine learning approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source.

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