Qingsong Liu

20.0k total citations · 5 hit papers
217 papers, 10.3k citations indexed

About

Qingsong Liu is a scholar working on Molecular Biology, Oncology and Hematology. According to data from OpenAlex, Qingsong Liu has authored 217 papers receiving a total of 10.3k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Molecular Biology, 30 papers in Oncology and 27 papers in Hematology. Recurrent topics in Qingsong Liu's work include PI3K/AKT/mTOR signaling in cancer (17 papers), Chronic Myeloid Leukemia Treatments (17 papers) and Chronic Lymphocytic Leukemia Research (14 papers). Qingsong Liu is often cited by papers focused on PI3K/AKT/mTOR signaling in cancer (17 papers), Chronic Myeloid Leukemia Treatments (17 papers) and Chronic Lymphocytic Leukemia Research (14 papers). Qingsong Liu collaborates with scholars based in China, United States and United Kingdom. Qingsong Liu's co-authors include Nathanael S. Gray, Carson C. Thoreen, David M. Sabatini, Seong A. Kang, Jianming Zhang, Taebo Sim, Jae Won Chang, Zheng Zhao, Yi Gao and Laurie J. Reichling and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Advanced Materials.

In The Last Decade

Qingsong Liu

211 papers receiving 10.1k citations

Hit Papers

An ATP-competitive Mammalian Target of Rapamycin Inhibito... 2009 2026 2014 2020 2009 2018 2017 2013 2022 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingsong Liu China 44 5.9k 1.3k 1.0k 971 917 217 10.3k
Christopher Gerner Austria 45 3.7k 0.6× 904 0.7× 1.4k 1.4× 521 0.5× 582 0.6× 227 7.4k
Si Zhang China 49 4.2k 0.7× 903 0.7× 714 0.7× 1.0k 1.0× 621 0.7× 502 10.7k
Xiaoyan Zhang China 57 5.6k 1.0× 1.1k 0.9× 1.4k 1.3× 311 0.3× 1.2k 1.3× 534 14.6k
Jing Liu China 59 8.9k 1.5× 1.3k 1.0× 1.8k 1.8× 1.3k 1.3× 1.3k 1.4× 564 15.4k
Hiroyuki Suzuki Japan 56 5.4k 0.9× 1.0k 0.8× 1.5k 1.5× 1.2k 1.2× 351 0.4× 382 11.6k
Xiaomin Wang China 53 3.9k 0.7× 647 0.5× 1.2k 1.2× 320 0.3× 850 0.9× 525 10.0k
Albert Sickmann Germany 72 11.0k 1.9× 914 0.7× 839 0.8× 390 0.4× 789 0.9× 320 15.9k
Yong J. Lee United States 57 6.6k 1.1× 1.3k 1.0× 1.6k 1.6× 482 0.5× 758 0.8× 253 10.6k
Jacek R. Wiśniewski Germany 51 12.4k 2.1× 1.3k 1.0× 1.9k 1.9× 370 0.4× 1.1k 1.2× 174 18.2k
Giorgio Federici Italy 55 6.8k 1.2× 536 0.4× 947 0.9× 527 0.5× 433 0.5× 308 12.2k

Countries citing papers authored by Qingsong Liu

Since Specialization
Citations

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

Fields of papers citing papers by Qingsong Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingsong Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Qingsong Liu. A scholar is included among the top collaborators of Qingsong Liu 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 Qingsong Liu. Qingsong Liu 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
2.
Xu, Yanchao, Hao Liu, Qingsong Liu, et al.. (2025). Genome-wide association study revealed candidate genes associated with leaf size in alfalfa (Medicago sativa L.). BMC Plant Biology. 25(1). 180–180. 1 indexed citations
3.
Hu, Chen, Yan Li, Fei Xu, et al.. (2025). Targeting METTL3 mitigates venetoclax resistance via proteasome-mediated modulation of MCL1 in acute myeloid leukemia. Cell Death and Disease. 16(1). 233–233. 3 indexed citations
4.
Li, Xixiang, Hongwei Yu, Fengming Zou, et al.. (2025). Discovery of a 1 H -Pyrazol-3-Amine Derivative as a Novel, Selective, and Orally Available RIPK1 Inhibitor for the Treatment of Inflammatory Disease. Journal of Medicinal Chemistry. 68(20). 21766–21785. 1 indexed citations
5.
Li, Chengdong, Qingsong Liu, Guihua Zhang, Liangliang Lin, & Kostya Ostrikov. (2023). Rapid synthesis of MTES-derived silica aerogel monoliths in Cetyltrimethylammonium bromide/water solvent system by ambient pressure drying. Powder Technology. 418. 118314–118314. 21 indexed citations
6.
Wang, Junjie, Ziping Qi, Yun Wu, et al.. (2023). Discovery of IHMT-MST1-39 as a novel MST1 kinase inhibitor and AMPK activator for the treatment of diabetes mellitus. Signal Transduction and Targeted Therapy. 8(1). 143–143. 12 indexed citations
7.
Wang, Aoli, Juan Liu, Xixiang Li, et al.. (2023). Discovery of a highly potent pan-RAF inhibitor IHMT-RAF-128 for cancer treatment. European Journal of Pharmacology. 952. 175752–175752. 2 indexed citations
8.
Wu, Yun, Ziping Qi, Beilei Wang, et al.. (2022). Discovery of IHMT-MST1-58 as a Novel, Potent, and Selective MST1 Inhibitor for the Treatment of Type 1/2 Diabetes. Journal of Medicinal Chemistry. 65(17). 11818–11839. 9 indexed citations
9.
Lu, Wen‐Qiang, et al.. (2022). Copper‐Promoted Cascade Radical Reaction of [60]Fullerene with Arylglyoxals and Further Derivatization. Asian Journal of Organic Chemistry. 11(3). 4 indexed citations
10.
Zhou, Bin, Xiaofei Liang, Husheng Mei, et al.. (2021). Discovery of IHMT-EZH2-115 as a Potent and Selective Enhancer of Zeste Homolog 2 (EZH2) Inhibitor for the Treatment of B-Cell Lymphomas. Journal of Medicinal Chemistry. 64(20). 15170–15188. 18 indexed citations
11.
Wang, Beilei, Hong Wu, Chen Hu, et al.. (2021). An overview of kinase downregulators and recent advances in discovery approaches. Signal Transduction and Targeted Therapy. 6(1). 423–423. 45 indexed citations
15.
Liu, Yan, Yuyang Li, Xiaoen Wang, et al.. (2017). Gemcitabine and Chk1 Inhibitor AZD7762 Synergistically Suppress the Growth of Lkb1-Deficient Lung Adenocarcinoma. Cancer Research. 77(18). 5068–5076. 25 indexed citations
16.
Weisberg, Ellen, Atsushi Nonami, Chen Zhao, et al.. (2014). Upregulation of IGF1R by Mutant RAS in Leukemia and Potentiation of RAS Signaling Inhibitors by Small-Molecule Inhibition of IGF1R. Clinical Cancer Research. 20(21). 5483–5495. 14 indexed citations
17.
Liao, Rachel G., Joonil Jung, Jeremy H. Tchaicha, et al.. (2013). Inhibitor-Sensitive FGFR2 and FGFR3 Mutations in Lung Squamous Cell Carcinoma. Cancer Research. 73(16). 5195–5205. 134 indexed citations
18.
Rodrigues, Frederico S. L. M., Xueyan Yang, Masataka Nikaido, Qingsong Liu, & Robert N. Kelsh. (2012). A Simple, Highly Visual in Vivo Screen for Anaplastic Lymphoma Kinase Inhibitors. ACS Chemical Biology. 7(12). 1968–1974. 11 indexed citations
19.
Ni, Jing, Qingsong Liu, Shaozhen Xie, et al.. (2012). Functional Characterization of an Isoform-Selective Inhibitor of PI3K-p110β as a Potential Anticancer Agent. Cancer Discovery. 2(5). 425–433. 125 indexed citations
20.
Liu, Qingsong, Jae Won Chang, Jinhua Wang, et al.. (2010). Discovery of 1-(4-(4-Propionylpiperazin-1-yl)-3-(trifluoromethyl)phenyl)-9-(quinolin-3-yl)benzo[h][1,6]naphthyridin-2(1H)-one as a Highly Potent, Selective Mammalian Target of Rapamycin (mTOR) Inhibitor for the Treatment of Cancer. DSpace@MIT (Massachusetts Institute of Technology). 11 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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