Fast prediction of distances between synthetic routes with deep learning. Issue 1 (21st January 2022)

• Record Type: Journal Article
• Authors: Genheden, Samuel; Engkvist, Ola; Bjerrum, Esben
• Is Part Of: Machine learning: science and technology. Volume 3:Issue 1(2022)
• Publication Date: 2022-01-21
• Subjects: synthetic routes -- machine learning -- tree edit distance -- reaction informatics; 006.31
• DOI: 10.1088/2632-2153/ac4a91
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: 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.