Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates-Reference-Cited by-同舟云学术

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Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates

Published:2024-03-05 Issue:6 Volume:20 Page:2655-2665
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Zhao Dongbo¹^ORCID,Zhao Yilin²,Xu Enhua³,Liu Wenqi¹,Ayers Paul W.²^ORCID,Liu Shubin⁴⁵^ORCID,Chen Dahua¹

Affiliation:

1. Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China

2. Department of Chemistry and Chemical Biology, McMaster University, Hamilton ONL8S4M1, Canada

3. Graduate School of System Informatics, Kobe University, Nada-ku, Kobe, Hyogo 657-8501, Japan

4. Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, United States

5. Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290, United States

Funder

Yunnan University

Canada Research Chairs

Natural Sciences and Engineering Research Council of Canada

National Natural Science Foundation of China

Alliance de recherche num?rique du Canada

Yunnan Fundamental Research Projects

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c01415

Reference130 articles.

1. When Is a Bond Broken? The Polarizability Perspective.

2. Fine-Structure Constant Connects Electronic Polarizability and Geometric van-der-Waals Radius of Atoms

3. Electronic Polarizability as the Fundamental Variable in the Dielectric Properties of Two-Dimensional Materials

4. Mechanical Properties of a Series of Macro- and Nanodimensional Organic Cocrystals Correlate with Atomic Polarizability

5. Ultrahigh resolution protein structures using NMR chemical shift tensors

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