Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts-Reference-Cited by-同舟云学术

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Published:2021 Issue:20 Volume:12 Page:6879-6889
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Gallarati Simone¹²³⁴⁵^ORCID,Fabregat Raimon¹²³⁴⁵^ORCID,Laplaza Rubén¹²³⁴⁵^ORCID,Bhattacharjee Sinjini¹²³⁴⁵^ORCID,Wodrich Matthew D.¹²³⁴⁵^ORCID,Corminboeuf Clemence¹²³⁴⁵^ORCID

Affiliation:

1. Laboratory for Computational Molecular Design

2. Institute of Chemical Sciences and Engineering

3. Ecole Polytechnique Fédérale de Lausanne (EPFL)

4. 1015 Lausanne

5. Switzerland

Abstract

A machine learning model for enantioselectivity prediction using reaction-based molecular representations.

Funder

H2020 European Research Council

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials

École Polytechnique Fédérale de Lausanne

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC00482D

Reference115 articles.

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2. R. E. Gawley and J.Aubé , in Principles of Asymmetric Synthesis , ed. R. E. Gawley and J. Aubé , Elsevier , Oxford , 2nd edn, 2012 , pp. 63–95

3. The advent and development of organocatalysis

4. Enantioselective Organocatalysis

5. P. I. Dalko , in Enantioselective Organocatalysis , John Wiley & Sons, Ltd , 2007 , pp. 1–17

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