Mengqiu Song

1.7k citations
38 papers · 1.3k indexed · 1 hit paper · h-index 16
Topics
Computational Drug Discovery Methods (7 papers)Cholinesterase and Neurodegenerative Diseases (7 papers)Synthesis and biological activity (6 papers)

In The Last Decade

Mengqiu Song

37 papers receiving 1.3k citations

Hit Papers

AKT as a Therapeutic Target for Cancer20192026202120232019200400600

Peers

Mengqiu Song
Comparison fields: 5 of 97
  • Molecular Biology 799
  • Oncology 263
  • Cancer Research 231
  • Pathology and Forensic Medicine 199
  • Organic Chemistry 165
Replace Shilpa Kuttikrishnan with:
Shilpa Kuttikrishnan Qatar
Shuwen Yu China
Yayun Liang United States
Pei‐Ming Yang Taiwan
Sung Hoo Jung South Korea
Bilal Rah India
Parham Jabbarzadeh Kaboli China
Xi Zou China
Yean Kee Lee Malaysia
Sheryl Phung United States
Mengqiu Song relative to Shilpa Kuttikrishnan Qatar Shilpa Kuttikrishnan's profile →
Citations per field
00.5×
Shilpa Kuttikrishnan · 1×
Citations per year

Countries citing papers authored by Mengqiu Song

Since Specialization
Citations

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

Fields of papers citing papers by Mengqiu Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengqiu Song

This figure shows the co-authorship network connecting the top 25 collaborators of Mengqiu Song. A scholar is included among the top collaborators of Mengqiu Song 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 Mengqiu Song. Mengqiu Song 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
#WorkIndexed citations
1 4
2 1
3 0
4 3
5 2
6 35
7 6
8 6
9 44
10 25
11 16
12 29
13 19
14 73
15 35
16 1
17 23
18 53
19 6
20 8

About Mengqiu Song

Mengqiu Song is a scholar working on Pharmacology, Pharmacology and Computational Theory and Mathematics, having authored 38 papers that have together received 1.3k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Cholinesterase and Neurodegenerative Diseases (7 papers) and Synthesis and biological activity (6 papers). The work is most often cited by research in Cancer Research (231 citations), Molecular Biology (799 citations) and Pathology and Forensic Medicine (199 citations). Mengqiu Song has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Mee‐Hyun Lee, Zigang Dong, Ann M. Bode, Kangdong Liu, Ran Zhao, Joydeb Kumar Kundu, Xinning Zhang, Zhenzhen Liu, Hanyong Chen and Jung‐Hyun Shim. Their work appears in journals such as Cancer Research, Science Advances and International Journal of Cancer.

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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