Reagent prediction with a molecular transformer improves reaction data quality
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Andronov, Mikhail
Voinarovska, Varvara

Andronova, Natalia

Wand, Michael

Clevert, Djork-Arné

Schmidhuber, Juergen

KAUST Department
AI Initiative, KAUST, 23955 Thuwal, Saudi ArabiaComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computer Science Program
Date
2023-03-01Permanent link to this record
http://hdl.handle.net/10754/690074
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Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions. In particular, reagent prediction for arbitrary reactions, a crucial aspect of condition recommendation, has been largely overlooked in cheminformatics until recently. Here we employ the Molecular Transformer, a state-of-the-art model for reaction prediction and single-step retrosynthesis, to tackle this problem. We train the model on the US patents dataset (USPTO) and test it on Reaxys to demonstrate its out-of-distribution generalization capabilities. Our reagent prediction model also improves the quality of product prediction: the Molecular Transformer is able to substitute the reagents in the noisy USPTO data with reagents that enable product prediction models to outperform those trained on plain USPTO. This makes it possible to improve upon the state-of-the-art in reaction product prediction on the USPTO MIT benchmark.Citation
Andronov, M., Voinarovska, V., Andronova, N., Wand, M., Clevert, D.-A., & Schmidhuber, J. (2023). Reagent prediction with a molecular transformer improves reaction data quality. Chemical Science. https://doi.org/10.1039/d2sc06798fSponsors
This study was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions, grant agreement “Advanced machine learning for Innovative Drug Discovery (AIDD)” No. 956832.Publisher
Royal Society of Chemistry (RSC)Journal
Chemical SciencePubMed Central ID
PMC10034139Additional Links
http://xlink.rsc.org/?DOI=D2SC06798FRelations
Is Supplemented By:- [Software]
Title: Academich/reagents: Reagent prediction with Molecular Transformer. Improvement of data for reaction product prediction in a self-supervised fashion.. Publication Date: 2022-07-22. github: Academich/reagents Handle: 10754/691618
ae974a485f413a2113503eed53cd6c53
10.1039/d2sc06798f
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Except where otherwise noted, this item's license is described as Archived with thanks to Chemical Science under a Creative Commons license, details at: http://creativecommons.org/licenses/by-nc/3.0/