Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.

Chemical Science
Philippe SchwallerTeodoro Laino

Abstract

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.

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Citations

Jun 15, 2021·ACS Omega·Kan Hatakeyama-Sato, Kenichi Oyaizu
Jul 19, 2020·Nature Communications·Alain C VaucherTeodoro Laino
Aug 19, 2020·Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry·Johann Gasteiger
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Oct 14, 2020·Nature·Barbara Mikulak-KlucznikBartosz A Grzybowski
Dec 30, 2020·Journal of Cheminformatics·Fernanda I Saldívar-GonzálezJosé L Medina-Franco
Jun 8, 2021·Chemical Science·Jonas Verhellen, Jeriek Van den Abeele
Jan 13, 2021·Journal of Cheminformatics·Umit V UcakJuyong Lee
Jun 13, 2021·Science Advances·Francesca GrisoniGisbert Schneider
Feb 21, 2021·Drug Discovery Today·Jiacheng XiongMingyue Zheng
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Mar 30, 2021·Expert Opinion on Drug Discovery·José Jiménez-LunaGisbert Schneider
Apr 9, 2021·Science Advances·Philippe SchwallerTeodoro Laino
Apr 22, 2021·Journal of Chemical Information and Modeling·Anatoliy Kuznetsov, Nikolaos V Sahinidis
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Jan 8, 2021·Journal of Chemical Information and Modeling·Eunji KimYoun-Suk Choi
Sep 28, 2021·Briefings in Bioinformatics·Jingxin DongXiangxiang Zeng

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Software Mentioned

RDKit
Molecular Transformer
NameRXN

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