Reagent prediction with a molecular transformer improves reaction data quality-Reference-Cited by-同舟云学术

Reagent prediction with a molecular transformer improves reaction data quality

Published:2023 Issue:12 Volume:14 Page:3235-3246
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Andronov Mikhail¹²^ORCID,Voinarovska Varvara³^ORCID,Andronova Natalia⁴^ORCID,Wand Michael¹⁵^ORCID,Clevert Djork-Arné²^ORCID,Schmidhuber Jürgen⁶

Affiliation:

1. IDSIA, USI, SUPSI, 6900 Lugano, Switzerland

2. Machine Learning Research, Pfizer Worldwide Research Development and Medical, Linkstr.10, Berlin, Germany

3. Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich – Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany

4. Via Berna 9, 6900 Lugano, Switzerland

5. Institute for Digital Technologies for Personalized Healthcare, SUPSI, 6900 Lugano, Switzerland

6. AI Initiative, KAUST, 23955 Thuwal, Saudi Arabia

Abstract

A molecular transformer predicts reagents for organic reactions. It is also able to replace questionable reagents in reaction data, e.g. USPTO, to enable better product prediction models to be trained on these new data.

Funder

H2020 Marie Skłodowska-Curie Actions

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D2SC06798F

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