MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions-Reference-Cited by-同舟云学术

MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions

Published:2023 Issue:8 Volume:14 Page:2054-2069
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Jiang Dejun¹²³,Ye Zhaofeng²,Hsieh Chang-Yu¹,Yang Ziyi²,Zhang Xujun¹,Kang Yu¹^ORCID,Du Hongyan¹,Wu Zhenxing¹,Wang Jike¹^ORCID,Zeng Yundian¹,Zhang Haotian¹,Wang Xiaorui⁴,Wang Mingyang¹,Yao Xiaojun⁴^ORCID,Zhang Shengyu²,Wu Jian³,Hou Tingjun¹^ORCID

Affiliation:

1. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China

2. Tencent Quantum Laboratory, Tencent, Shenzhen 518057, Guangdong, China

3. College of Computer Science and Technology, Zhejiang University, Hangzhou 310006, Zhejiang, China

4. State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao

Abstract

Metalloproteins play essential roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection,and inflammation.

Funder

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D2SC06576B

Reference56 articles.