DU8ML: Machine Learning-Augmented Density Functional Theory Nuclear Magnetic Resonance Computations for High-Throughput In Silico Solution Structure Validation and Revision of Complex Alkaloids-Reference-Cited by-同舟云学术

DU8ML: Machine Learning-Augmented Density Functional Theory Nuclear Magnetic Resonance Computations for High-Throughput In Silico Solution Structure Validation and Revision of Complex Alkaloids

Published:2022-03-18 Issue:7 Volume:87 Page:4818-4828
ISSN:0022-3263
Container-title:The Journal of Organic Chemistry
language:en
Short-container-title:J. Org. Chem.

Author:

Novitskiy Ivan M.¹^ORCID,Kutateladze Andrei G.¹^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, University of Denver, Denver, Colorado 80208, United States

Funder

Division of Chemistry

Publisher

American Chemical Society (ACS)

Subject

Organic Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.joc.2c00169

Reference68 articles.

1. Computational Prediction of 1H and 13C Chemical Shifts: A Useful Tool for Natural Product, Mechanistic, and Synthetic Organic Chemistry

2. See also Tantillo’s excellent compilation of various NMR computational approaches. 2022, http://cheshirenmr.info (accessed March 1, 2022).

3. Computer Assisted Structure Elucidation (CASE): Current and future perspectives

4. Structural characterisation of natural products by means of quantum chemical calculations of NMR parameters: new insights

5. Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

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