Lanlan Jing

20 papers receiving 615 citations

Peers

Lanlan Jing
Comparison fields: 5 of 64
  • Virology 206
  • Infectious Diseases 258
  • Organic Chemistry 325
  • Computational Theory and Mathematics 69
  • Pharmacology 61
Replace Xiangyi Jiang with:
Xiangyi Jiang China
Emanuela Nizi Italy
Joseph W. Strohbach United States
Ryohei Kato Japan
Alessia Petrocchi Italy
Roger R. Hinshaw United States
Vandna Munshi United States
Lucien M. H. Koymans Belgium
Miao-Miao Horng United States
Sanjeev Kumar V. Vernekar United States
Lanlan Jing relative to Xiangyi Jiang China Xiangyi Jiang's profile →
Citations per field
00.5×4.1×
Xiangyi Jiang · 1×
Citations per year

Countries citing papers authored by Lanlan Jing

Since Specialization
Citations

This map shows the geographic impact of Lanlan Jing'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 Lanlan Jing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lanlan Jing more than expected).

Fields of papers citing papers by Lanlan Jing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lanlan Jing. 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 Lanlan Jing. The network helps show where Lanlan Jing may publish in the future.

Co-authors

The 25 scholars most cited alongside Lanlan Jing, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lanlan Jing Line = papers co-authored together Lanlan Jing links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019201
2 201855
3 201953
4 201845
5 202140
6 201833
7 201829
8 201929
9 202027
10 201822
11 202016
12 201815
13 202214
14 202310
15 20229
16 20237
17 20226
18 20244
19 20234
20 20234

About Lanlan Jing

Lanlan Jing is a scholar working on Infectious Diseases, Virology, Organic Chemistry, Molecular Biology and Computational Theory and Mathematics, having authored 22 papers that have together received 623 indexed citations. Recurring topics across this work include HIV/AIDS drug development and treatment (14 papers), HIV Research and Treatment (13 papers), Computational Drug Discovery Methods (6 papers), Click Chemistry and Applications (6 papers), Biochemical and Molecular Research (4 papers), Cholinesterase and Neurodegenerative Diseases (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and Synthesis and biological activity (3 papers). The work is most often cited by research in Virology (206 citations), Infectious Diseases (258 citations), Organic Chemistry (325 citations), Computational Theory and Mathematics (69 citations) and Pharmacology (61 citations). Lanlan Jing has collaborated with scholars based in China, Belgium and United States. Frequent co-authors include Xinyong Liu, Dongwei Kang, Peng Zhan, Gaochan Wu, Xiangyi Jiang, Xia Hao, Erik De Clercq, Christophe Pannecouque, Zhongxia Zhou and Da Feng. Their work appears in journals such as European Journal of Medicinal Chemistry, Journal of Medicinal Chemistry, ACS Medicinal Chemistry Letters, Bioorganic & Medicinal Chemistry and International Journal of Molecular Sciences.

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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