Kexin Su

478 total citations · 1 hit paper
18 papers, 312 citations indexed

About

Kexin Su is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Kexin Su has authored 18 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 4 papers in Oncology and 3 papers in Immunology. Recurrent topics in Kexin Su's work include RNA Interference and Gene Delivery (11 papers), Advanced biosensing and bioanalysis techniques (7 papers) and Lipid Membrane Structure and Behavior (2 papers). Kexin Su is often cited by papers focused on RNA Interference and Gene Delivery (11 papers), Advanced biosensing and bioanalysis techniques (7 papers) and Lipid Membrane Structure and Behavior (2 papers). Kexin Su collaborates with scholars based in China, United States and Saudi Arabia. Kexin Su's co-authors include Shuai Liu, Lixin Lin, Lu Shi, Xinxin Yan, Tao Sheng, Yao Zhang, Yuxuan Chen, Tengfei Xu, Shun He and Chaoyang Meng and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Kexin Su

14 papers receiving 307 citations

Hit Papers

Reformulating lipid nanoparticles for organ-targeted mRNA... 2024 2026 2025 2024 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Reformulating lipid nanoparticles for organ-targeted mRNA accumulation and translation
    2024 125 citations Kexin Su, Lu Shi et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kexin Su China 9 247 37 35 34 29 18 312
Joshua Robinson United States 4 276 1.1× 55 1.5× 52 1.5× 50 1.5× 48 1.7× 6 347
Binyan Zhao China 12 202 0.8× 58 1.6× 63 1.8× 28 0.8× 41 1.4× 19 326
Jicheng Wu China 10 199 0.8× 46 1.2× 45 1.3× 14 0.4× 39 1.3× 22 319
Minglei Yang China 13 205 0.8× 24 0.6× 44 1.3× 20 0.6× 33 1.1× 18 353
Yusi Liu China 12 185 0.7× 24 0.6× 28 0.8× 21 0.6× 33 1.1× 25 332
Qurrat Ul Ain South Korea 10 249 1.0× 37 1.0× 45 1.3× 64 1.9× 30 1.0× 16 388
Ryann A. Joseph United States 6 186 0.8× 42 1.1× 64 1.8× 34 1.0× 26 0.9× 9 271
Jie Peng China 11 203 0.8× 32 0.9× 42 1.2× 83 2.4× 23 0.8× 22 315
Zhiqing Li China 9 230 0.9× 37 1.0× 93 2.7× 24 0.7× 23 0.8× 26 409
Feng Sun China 9 176 0.7× 31 0.8× 37 1.1× 34 1.0× 18 0.6× 32 354

Countries citing papers authored by Kexin Su

Since Specialization
Citations

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

Fields of papers citing papers by Kexin Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kexin Su

This figure shows the co-authorship network connecting the top 25 collaborators of Kexin Su. A scholar is included among the top collaborators of Kexin Su 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 Kexin Su. Kexin Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Lin, Lixin, Kexin Su, Xinyue Zhang, et al.. (2025). A Versatile Strategy to Transform Cationic Polymers for Efficient and Organ‐Selective mRNA Delivery. Angewandte Chemie International Edition. 64(17). e202500306–e202500306. 9 indexed citations
2.
Lin, Lixin, Kexin Su, Xinyue Zhang, et al.. (2025). A Versatile Strategy to Transform Cationic Polymers for Efficient and Organ‐Selective mRNA Delivery. Angewandte Chemie. 137(17).
3.
Zhang, Yiran, Kexin Su, Lu Shi, et al.. (2025). Zinc‐Coordinated Lipids: Facilitators for Enhanced mRNA Delivery Efficacy. Angewandte Chemie International Edition. 64(50). e202515406–e202515406.
4.
Zhang, Yiran, Kexin Su, Lu Shi, et al.. (2025). Zinc‐Coordinated Lipids: Facilitators for Enhanced mRNA Delivery Efficacy. Angewandte Chemie. 137(50).
5.
Su, Kexin, et al.. (2025). Artificial intelligence-guided design of lipid nanoparticles for mRNA delivery. Acta Pharmaceutica Sinica B. 16(2). 709–727.
6.
Shi, Lu, Kexin Su, Lixin Lin, et al.. (2025). Carbonate-Bearing Ionizable Lipids for mRNA Delivery to Splenic NK Cells. Journal of the American Chemical Society. 147(32). 28665–28673. 10 indexed citations
7.
Su, Kexin, Xinxin Yan, Lu Shi, et al.. (2024). Paracyclophane-based ionizable lipids for efficient mRNA delivery in vivo. Journal of Controlled Release. 376. 395–401. 19 indexed citations
8.
Su, Kexin, Lu Shi, Tao Sheng, et al.. (2024). Reformulating lipid nanoparticles for organ-targeted mRNA accumulation and translation. Nature Communications. 15(1). 5659–5659. 125 indexed citations breakdown →
9.
Zhang, Xinyue, Kexin Su, Lixin Lin, et al.. (2024). One‐Component Cationic Lipids for Systemic mRNA Delivery to Splenic T Cells. Angewandte Chemie. 136(26). 12 indexed citations
10.
Su, Kexin, Shiqi Wu, Lixin Lin, et al.. (2024). One‐Component Cationic Lipids for Systemic mRNA Delivery to Splenic T Cells. Angewandte Chemie International Edition. 63(26). e202405444–e202405444. 40 indexed citations
11.
Su, Kexin, Xuan Yao, Chenxu Guo, et al.. (2023). Solasodine suppresses the metastasis of gastric cancer through claudin-2 via the AMPK/STAT3/NF-κB pathway. Chemico-Biological Interactions. 379. 110520–110520. 15 indexed citations
12.
Xu, Midie, Xing Zhang, Kexin Su, et al.. (2023). Gambogenic acid inhibits proliferation and ferroptosis by targeting the miR‐1291/FOXA2 and AMPKα/SLC7A11/GPX4 axis in colorectal cancer. Cell Biology International. 47(11). 1813–1824. 17 indexed citations
13.
Su, Kexin, et al.. (2023). Targeting materials and strategies for RNA delivery. Theranostics. 13(13). 4667–4693. 34 indexed citations
14.
Wang, Xuan, Neng Wang, Linda L. D. Zhong, et al.. (2022). Development and Validation of a Risk Prediction Model for Breast Cancer Prognosis Based on Depression-Related Genes. Frontiers in Oncology. 12. 879563–879563. 8 indexed citations
15.
Zhang, Li‐Bing, Rui Guan, Guangli Li, et al.. (2022). Genomic identification of ARF transcription factors and expression analysis in Cannabis sativa L. Industrial Crops and Products. 186. 115118–115118. 8 indexed citations
16.
Su, Kexin, et al.. (2018). [In vivo study of 3D printed porous tantalum implant on osseointegration].. PubMed. 36(3). 291–295. 7 indexed citations
17.
Li, Linlin, et al.. (2018). 3D reconstruction images of cone beam computed tomography applied to maxillofacial fractures: A case study and mini review. Journal of X-Ray Science and Technology. 26(1). 115–123. 6 indexed citations
18.
Wang, Jun, Lifeng Li, Lina Gao, et al.. (2017). Identification of differentially expressed genes in oral squamous cell carcinoma TCA8113 cells. Oncology Letters. 14(6). 7055–7068. 2 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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