Predicting glycosylation stereoselectivity using machine learning-Reference-Cited by-同舟云学术

Predicting glycosylation stereoselectivity using machine learning

Published:2021 Issue:8 Volume:12 Page:2931-2939
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Moon Sooyeon¹²³⁴⁵^ORCID,Chatterjee Sourav¹²³⁴^ORCID,Seeberger Peter H.¹²³⁴⁵^ORCID,Gilmore Kerry¹²³⁴^ORCID

Affiliation:

1. Department of Biomolecular Systems

2. Max-Planck-Institute of Colloids and Interfaces

3. 14476 Potsdam

4. Germany

5. Freie Universität Berlin

Abstract

A random forest algorithm, trained on a concise dataset and validated experimentally, accurately predicts the stereoselectivity of a complex organic coupling varying all reaction parameters as well as previously unknown mechanistic influences.

Funder

Deutsche Forschungsgemeinschaft

Max-Planck-Gesellschaft

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D0SC06222G

Reference56 articles.

1. Quantum Mechanical Predictions of the Stereoselectivities of Proline-Catalyzed Asymmetric Intermolecular Aldol Reactions

2. Theory and Modeling of Stereoselective Organic Reactions

3. Prediction of Stereochemistry using Q2MM

4. Defining the SN1 Side of Glycosylation Reactions: Stereoselectivity of Glycopyranosyl Cations

5. Computing organic stereoselectivity – from concepts to quantitative calculations and predictions

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