Predicting Drug–Target Interactions with Deep-Embedding Learning of Graphs and Sequences-Reference-Cited by-同舟云学术

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Predicting Drug–Target Interactions with Deep-Embedding Learning of Graphs and Sequences

Published:2021-06-18 Issue:25 Volume:125 Page:5633-5642
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Chen Wei¹,Chen Guanxing¹,Zhao Lu¹²,Chen Calvin Yu-Chian¹³⁴^ORCID

Affiliation:

1. Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China

2. Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China

3. Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan

4. Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan

Funder

China Medical University Hospital

Guangzhou Municipal Science and Technology Bureau

Science, Technology and Innovation Commission of Shenzhen Municipality

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.1c02419

Reference44 articles.

1. Molecular Docking

2. Systematic Generation of Chemical Structures for Rational Drug Design Based on QSAR Models

3. Support-vector networks

4. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

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