Jun Zou

7.7k total citations
53 papers, 1.2k citations indexed

About

Jun Zou is a scholar working on Molecular Biology, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Jun Zou has authored 53 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 9 papers in Information Systems. Recurrent topics in Jun Zou's work include Computational Drug Discovery Methods (12 papers), Recommender Systems and Techniques (7 papers) and Protein Structure and Dynamics (5 papers). Jun Zou is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Recommender Systems and Techniques (7 papers) and Protein Structure and Dynamics (5 papers). Jun Zou collaborates with scholars based in China, United States and Switzerland. Jun Zou's co-authors include Shengyong Yang, Yuquan Wei, Faramarz Fekri, Linli Li, Haixia Qi, Huanzhang Xie, Melvyn S. Tockman, Hong Tang, Robert A. Clark and Lihua Li and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Jun Zou

51 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jun Zou China 19 603 271 121 104 102 53 1.2k
Neil Swainston United Kingdom 26 2.3k 3.9× 383 1.4× 76 0.6× 81 0.8× 89 0.9× 50 2.8k
Kirill Degtyarenko United Kingdom 13 1.0k 1.7× 238 0.9× 66 0.5× 31 0.3× 52 0.5× 20 1.3k
Paul D. Dobson United Kingdom 18 1.2k 1.9× 302 1.1× 290 2.4× 68 0.7× 39 0.4× 24 1.9k
Xuan Hong China 17 340 0.6× 110 0.4× 133 1.1× 54 0.5× 35 0.3× 63 1.2k
Juan Liu China 26 1.2k 2.0× 305 1.1× 90 0.7× 55 0.5× 55 0.5× 168 2.2k
Jennifer Fostel United States 30 2.0k 3.4× 243 0.9× 333 2.8× 111 1.1× 241 2.4× 54 3.1k
Anna Gambin Poland 22 894 1.5× 190 0.7× 70 0.6× 38 0.4× 348 3.4× 95 1.7k
Thomas Kelder Netherlands 18 1.6k 2.7× 225 0.8× 126 1.0× 33 0.3× 57 0.6× 30 2.3k

Countries citing papers authored by Jun Zou

Since Specialization
Citations

This map shows the geographic impact of Jun Zou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Zou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Zou more than expected).

Fields of papers citing papers by Jun Zou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Zou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Zou. The network helps show where Jun Zou may publish in the future.

Co-authorship network of co-authors of Jun Zou

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Zou. A scholar is included among the top collaborators of Jun Zou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jun Zou. Jun Zou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Huang, Luyi & Jun Zou. (2023). Small‐molecule drugs as RNA‐targeted degraders. SHILAP Revista de lepidopterología. 2(3). 1 indexed citations
2.
Li, Xin, Jiaqiang Wang, Yuanxu Gao, et al.. (2022). Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduction and Targeted Therapy. 7(1). 162–162. 76 indexed citations
3.
Yang, Xin, Xuehui Wang, Zheng Xu, et al.. (2022). Molecular mechanism of allosteric modulation for the cannabinoid receptor CB1. Nature Chemical Biology. 18(8). 831–840. 67 indexed citations
4.
Li, Yueshan, Yifei Wang, Jun Zou, et al.. (2022). Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nature Communications. 13(1). 6891–6891. 55 indexed citations
5.
Zhu, Jing, Jun Zou, Fei Li, et al.. (2022). Evaluation of neuroprotective agents acting via the BDNF–TrkB pathway using AI‐enabled predictions of ligand–receptor interactions. SHILAP Revista de lepidopterología. 1(1). 2 indexed citations
6.
Suo, Tao, Huaying Fan, Kailiang Zhao, et al.. (2021). Clinical efficacy of Lopinavir-Ritonavir combined with interferon alpha in COVID-19. 42(4). 594–598.
7.
Zou, Jun, Hao Yu, Dawei Song, Junjie Niu, & Huilin Yang. (2020). Advice on Standardized Diagnosis and Treatment for Spinal Diseases during the Coronavirus Disease 2019 Pandemic. Asian Spine Journal. 14(2). 258–263. 36 indexed citations
8.
Wang, Yiwei, Lei Huang, Yifei Wang, et al.. (2020). Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers. Frontiers in Pharmacology. 10. 1631–1631. 36 indexed citations
9.
Wang, Yiwei, et al.. (2019). CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens. Food and Chemical Toxicology. 135. 110921–110921. 37 indexed citations
10.
Zou, Jun & Faramarz Fekri. (2014). Exploiting Popularity and Similarity for Link Recommendation in Twitter Networks. Conference on Recommender Systems. 3 indexed citations
11.
Wang, Huijuan, Liang Wang, Hailong Zhang, et al.. (2013). ¹H NMR-based metabolic profiling of human rectal cancer tissue. Molecular Cancer. 12(1). 121–121. 91 indexed citations
12.
Zou, Jun, Yinglan Zhao, Linli Li, et al.. (2012). Neighbor communities in drug combination networks characterize synergistic effect. Molecular BioSystems. 8(12). 3185–3196. 31 indexed citations
13.
14.
Xie, Huanzhang, Jun Zou, Linli Li, et al.. (2012). Identification of Novel Anaplastic Lymphoma Kinase (ALK) Inhibitors Using a Common Feature Pharmacophore Model Derived from Known Ligands Crystallized with ALK. Chemical Biology & Drug Design. 81(2). 175–184. 2 indexed citations
15.
Yang, Lingling, Guo‐Bo Li, Hengxiu Yan, et al.. (2012). Discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors using common-feature pharmacophore model based virtual screening and hit-to-lead optimization. European Journal of Medicinal Chemistry. 56. 30–38. 39 indexed citations
16.
Zou, Jun, et al.. (2010). Integrated computational model of cell cycle and checkpoint reveals different essential roles of Aurora-A and Plk1 in mitotic entry. Molecular BioSystems. 7(1). 169–179. 12 indexed citations
17.
Xie, Huanzhang, Linli Li, Ji-Xia Ren, et al.. (2009). Pharmacophore modeling study based on known Spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors. Bioorganic & Medicinal Chemistry Letters. 19(7). 1944–1949. 34 indexed citations
18.
Zou, Jun, et al.. (2008). Detailed conformational dynamics of juxtamembrane region and activation loop in c‐Kit kinase activation process. Proteins Structure Function and Bioinformatics. 72(1). 323–332. 23 indexed citations
19.
Zou, Jun, et al.. (2008). Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. Journal of Molecular Graphics and Modelling. 27(4). 430–438. 58 indexed citations
20.
Prosise, W.W., T. Yarosh-Tomaine, Richard Ingram, et al.. (2004). Protease domain of human ADAM33 produced by Drosophila S2 cells. Protein Expression and Purification. 38(2). 292–301. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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