A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions-Reference-Cited by-同舟云学术

A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions

Published:2019-06-07 Issue:7 Volume:5 Page:1199-1210
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Jinich Adrian¹²,Sanchez-Lengeling Benjamin¹,Ren Haniu¹,Harman Rebecca¹,Aspuru-Guzik Alán³⁴⁵^ORCID

Affiliation:

1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States

2. Division of Infectious Diseases, Weill Department of Medicine, Weill−Cornell Medical College, New York, New York 10065, United States

3. Department of Chemistry and Department of Computer Science, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada

4. Vector Institute, Toronto, Ontario M5G 1M1, Canada

5. Biologically-Inspired Solar Energy Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5S 1M1, Canada

Funder

Canada 150 Research Chairs Program

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acscentsci.9b00297

Reference80 articles.

1. Pathway Thermodynamics Highlights Kinetic Obstacles in Central Metabolism

2. Thermodynamics-Based Metabolic Flux Analysis

3. Thermodynamic constraints shape the structure of carbon fixation pathways

4. Thermodynamic Constraints Improve Metabolic Networks

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