A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network-Reference-Cited by-同舟云学术

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network

Published:2021-11-01 Issue:11 Volume:61 Page:5414-5424
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Xiang Yan¹^ORCID,Tang Yu-Hang²^ORCID,Lin Guang³^ORCID,Sun Huai¹^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

2. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

3. Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, United States

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c01118

Reference47 articles.

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4. Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry. In Proceedings of the 34th International Conference on Machine Learning; PMLR, 2017; pp 1263–1272.

5. Klicpera, J.; Gross, J.; Günnemann, S. Directional Message Passing for Molecular Graphs. ArXiv200303123 Phys. Stat. 2020.

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