Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks-Reference-Cited by-同舟云学术

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks

Published:2020-11-09 Issue:12 Volume:60 Page:5936-5945
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Hwang Doyeong¹,Yang Soojung¹²,Kwon Yongchan³,Lee Kyung Hoon²,Lee Grace¹,Jo Hanseok¹,Yoon Seyeol¹,Ryu Seongok¹^ORCID

Affiliation:

1. AITRICS, Hyoryoung-ro 77-gil, Seocho-gu, Seoul, Republic of Korea

2. Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

3. Department of Biomedical Data Science, Stanford University, Stanford, California 94305, United States

Funder

National Research Foundation of Korea

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.0c00416

Reference45 articles.

1. Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S. How powerful are graph neural networks? 2018, arXiv:1810.00826. arXiv.org e-Print archive. https://arxiv.org/abs/1810.00826.

2. QSAR Modeling: Where Have You Been? Where Are You Going To?

3. Extended-Connectivity Fingerprints

4. Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. In Convolutional Networks on Graphs For Learning Molecular Fingerprints, Advances in Neural Information Processing Systems, 2015; pp 2224–2232.

5. Molecular graph convolutions: moving beyond fingerprints

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