Wanjing Ding

1.1k total citations
64 papers, 938 citations indexed

About

Wanjing Ding is a scholar working on Pharmacology, Molecular Biology and Biotechnology. According to data from OpenAlex, Wanjing Ding has authored 64 papers receiving a total of 938 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Pharmacology, 35 papers in Molecular Biology and 22 papers in Biotechnology. Recurrent topics in Wanjing Ding's work include Microbial Natural Products and Biosynthesis (41 papers), Marine Sponges and Natural Products (18 papers) and Fungal Biology and Applications (9 papers). Wanjing Ding is often cited by papers focused on Microbial Natural Products and Biosynthesis (41 papers), Marine Sponges and Natural Products (18 papers) and Fungal Biology and Applications (9 papers). Wanjing Ding collaborates with scholars based in China, United States and Czechia. Wanjing Ding's co-authors include Zhongjun Ma, Jiaqi Li, Jinzhong Xu, Yong‐Jun Jiang, Qiaojun He, Biao Zhou, Pinmei Wang, Bo Yang, Hong Zhu and Zhe Chen and has published in prestigious journals such as Blood, PLoS ONE and Journal of Agricultural and Food Chemistry.

In The Last Decade

Wanjing Ding

63 papers receiving 926 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wanjing Ding China 21 432 428 295 235 102 64 938
Gerhard Erkel Germany 21 514 1.2× 442 1.0× 156 0.5× 289 1.2× 39 0.4× 66 1.2k
Henki Rotinsulu Indonesia 22 550 1.3× 466 1.1× 543 1.8× 356 1.5× 28 0.3× 67 1.3k
Yongchun Shen United States 19 498 1.2× 284 0.7× 175 0.6× 313 1.3× 25 0.2× 47 1.0k
Dewu Zhang China 20 459 1.1× 544 1.3× 226 0.8× 136 0.6× 69 0.7× 61 962
Zhihui Zheng China 21 544 1.3× 622 1.5× 294 1.0× 259 1.1× 18 0.2× 69 1.2k
Qunfei Zhao China 14 587 1.4× 579 1.4× 180 0.6× 250 1.1× 62 0.6× 34 921
Karen TenDyke United States 19 549 1.3× 258 0.6× 233 0.8× 274 1.2× 25 0.2× 49 1.2k
Supakarn Chamni Thailand 18 270 0.6× 200 0.5× 157 0.5× 164 0.7× 38 0.4× 56 726
Jean‐Charles Chapuis United States 24 642 1.5× 406 0.9× 393 1.3× 650 2.8× 62 0.6× 54 1.5k
Barbora Orlikova Luxembourg 18 444 1.0× 244 0.6× 63 0.2× 304 1.3× 35 0.3× 23 926

Countries citing papers authored by Wanjing Ding

Since Specialization
Citations

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

Fields of papers citing papers by Wanjing Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wanjing Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Wanjing Ding. A scholar is included among the top collaborators of Wanjing Ding 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 Wanjing Ding. Wanjing Ding 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.
Zhu, Yuchen, Xiaohong Chen, Yun Huang, et al.. (2025). DGMM: A Deep Learning-Genetic Algorithm Framework for Efficient Lead Optimization in Drug Discovery. Journal of Chemical Information and Modeling. 65(15). 8168–8180.
2.
Chen, Xiaohong, et al.. (2024). Natural Derivatives of Selective HDAC8 Inhibitors with Potent in Vivo Antitumor Efficacy against Breast Cancer. Journal of Medicinal Chemistry. 67(16). 14609–14632. 3 indexed citations
3.
Chen, Shuang, et al.. (2024). Bipyridine Derivatives as NOP2/Sun RNA Methyltransferase 3 Inhibitors for the Treatment of Colorectal Cancer. Journal of Medicinal Chemistry. 67(15). 13446–13473. 9 indexed citations
4.
Zou, Shijie, Yun Huang, Xiaohong Chen, et al.. (2024). Discovery of the First-in-Class Dual-Target ROCK/HDAC Inhibitor with Potent Antitumor Efficacy in Vivo That Trigger Antitumor Immunity. Journal of Medicinal Chemistry. 67(22). 20619–20638. 8 indexed citations
5.
Wen, Zhengshun, et al.. (2023). Identification of Novel Sphydrofuran-Derived Derivatives with Lipid-Lowering Activity from the Active Crude Extracts of Nocardiopsis sp. ZHD001. International Journal of Molecular Sciences. 24(3). 2822–2822. 7 indexed citations
6.
Gao, Tingting, Zhiwei Ge, Zhongjun Ma, et al.. (2021). Design, Synthesis and Structure-Activity Relationship Studies of Glycosylated Derivatives of Marine Natural Product Lamellarin D. European Journal of Medicinal Chemistry. 214. 113226–113226. 36 indexed citations
7.
Ding, Wanjing, et al.. (2020). Gephyromycin C, a novel small-molecule inhibitor of heat shock protein Hsp90, induces G2/M cell cycle arrest and apoptosis in PC3 cells in vitro. Biochemical and Biophysical Research Communications. 531(3). 377–382. 15 indexed citations
8.
Ding, Wanjing, et al.. (2020). Synergistic antitumor activity of DHA and JQ1 in colorectal carcinoma. European Journal of Pharmacology. 885. 173500–173500. 10 indexed citations
9.
Jiang, Yong‐Jun, et al.. (2019). Physalinol A, a 1,10-seco-physalin with an epidioxy from Physalis alkekengi L. var. franchetii (Mast.) Makino. Tetrahedron Letters. 60(19). 1330–1332. 10 indexed citations
10.
Zhou, Biao, et al.. (2019). Nitricquinomycins A-C, uncommon naphthopyrrolediones from the Streptomyces sp. ZS-A45. Tetrahedron. 75(30). 3958–3961. 6 indexed citations
11.
Jiang, Yong‐Jun, Yuanyuan Ji, Jiaqi Li, & Wanjing Ding. (2019). A new compound from the marine-derivedStreptomycessp. SS13M. Journal of Asian Natural Products Research. 22(7). 701–706. 2 indexed citations
12.
Zhou, Biao, et al.. (2018). Bioactive staurosporine derivatives from the Streptomyces sp. NB-A13. Bioorganic Chemistry. 82. 33–40. 26 indexed citations
13.
Zhou, Biao, et al.. (2018). Purmedermycins A and B, two novel medermycin derivatives from Streptomyces sp. SS17A. Organic Chemistry Frontiers. 6(3). 399–404. 3 indexed citations
14.
Zhou, Biao, et al.. (2017). Bioactive metabolites from marine-derived Streptomyces sp. A68 and its Rifampicin resistant mutant strain R-M1. Phytochemistry Letters. 23. 46–51. 20 indexed citations
15.
Li, Ning, et al.. (2016). Chemopreventive agents from Physalis minima function as michael reaction acceptors. Pharmacognosy Magazine. 12(46). 231–231. 6 indexed citations
16.
Li, Ning, et al.. (2014). Unprecedent aminophysalin from Physalis angulata. Steroids. 88. 60–65. 22 indexed citations
17.
Zhang, Lei, Qian Zhou, Weixu Li, et al.. (2014). E2F1 impairs all-trans retinoic acid-induced osteogenic differentiation of osteosarcoma via promoting ubiquitination-mediated degradation of RARα. Cell Cycle. 13(8). 1277–1287. 26 indexed citations
18.
Ding, Wanjing, Tianyu Cai, Hong Zhu, et al.. (2010). Synergistic antitumor effect of TRAIL in combination with sunitinib in vitro and in vivo. Cancer Letters. 293(2). 158–166. 29 indexed citations
19.
Wu, Rui, Wanjing Ding, Tao Liu, et al.. (2009). XN05, a novel synthesized microtubule inhibitor, exhibits potent activity against human carcinoma cells in vitro. Cancer Letters. 285(1). 13–22. 25 indexed citations
20.
Zhu, Hong, Wanjing Ding, Rui Wu, et al.. (2009). Synergistic Anti-Cancer Activity by the Combination of TRAIL/APO-2L and Celastrol. Cancer Investigation. 28(1). 23–32. 48 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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