EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution-Reference-Cited by-同舟云学术

EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution

Author:

Ran Xinchun¹,Jiang Yaoyukun¹^ORCID,Shao Qianzhen¹^ORCID,Yang Zhongyue J.¹²³⁴⁵^ORCID

Affiliation:

1. Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, USA

2. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, USA

3. Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, USA

4. Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, USA

5. Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA

Abstract

EnzyKR is designed to guide the identification of hydrolase scaffolds for resolving a racemic substrate mixture for stereoselective synthesis.

Funder

Vanderbilt University

National Institute of General Medical Sciences

National Institutes of Health

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

Reference33 articles.

5. U. T.Bornscheuer and R. J.Kazlauskas , Hydrolases in organic synthesis: regio-and stereoselective biotransformations , John Wiley & Sons , 2006

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