Min Shen
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods 26
- Cancer Research top 1%
- Cancer, Hypoxia, and Metabolism 21
- Infectious Diseases top 1%
- SARS-CoV-2 and COVID-19 Research 16
- COVID-19 Clinical Research Studies 10
- Molecular Biology top 2%
- Biochemical and Molecular Research 11
- Ubiquitin and proteasome pathways 8
- Oncology top 5%
- Cancer-related Molecular Pathways 8
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- Amino Acid Enzymes and Metabolism 8
- Co-authors
- Anton SimeonovDouglas S. AuldMatthew D. HallAlexander TropshaAlexander GolbraikhCraig J. ThomasMatthew B. BoxerKyle R. Brimacombe
- Journals
- Journal of Medicinal Chemistry (9 papers)Bioorganic & Medicinal Chemistry Letters (8 papers)ACS Pharmacology & Translational Science (8 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Min Shen
156 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Computational Theory and Mathematics 1.5k
- Cancer Research 1.2k
- Infectious Diseases 1.1k
- Molecular Biology 3.8k
- Oncology 879
Countries citing papers authored by Min Shen
This map shows the geographic impact of Min Shen'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 Min Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min Shen more than expected).
Fields of papers citing papers by Min Shen
This network shows the impact of papers produced by Min Shen. 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 Min Shen. The network helps show where Min Shen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Min Shen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2023 | 11 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 1 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 8 | |
| 8 | 2021 | 8 | |
| 9 | 2021 | 20 | |
| 10 | 2021 | 10 | |
| 11 | 2020 | 18 | |
| 12 | 2019 | 22 | |
| 13 | 2019 | 22 | |
| 14 | 2019 | 13 | |
| 15 | 2016 | 77 | |
| 16 | 2016 | 16 | |
| 17 | 2014 | 198 | |
| 18 | 2012 | 73 | |
| 19 | Toward Improved Therapy for Classic Galactosemia | 2011 | 2 |
| 20 | 2010 | 148 |
About Min Shen
Min Shen is a scholar working on Cancer Research, Computational Theory and Mathematics and Biochemistry, having authored 162 papers that have together received 7.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (26 papers), Cancer, Hypoxia, and Metabolism (21 papers), SARS-CoV-2 and COVID-19 Research (16 papers), Biochemical and Molecular Research (11 papers), COVID-19 Clinical Research Studies (10 papers), Ubiquitin and proteasome pathways (8 papers), Cancer-related Molecular Pathways (8 papers) and Amino Acid Enzymes and Metabolism (8 papers). The work is most often cited by research in Computational Theory and Mathematics (1.5k citations), Cancer Research (1.2k citations) and Infectious Diseases (1.1k citations). Min Shen has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Anton Simeonov, Douglas S. Auld, Matthew D. Hall, Alexander Tropsha, Alexander Golbraikh, Craig J. Thomas, Matthew B. Boxer, Kyle R. Brimacombe, Richard T. Eastman and Samarjit Patnaik. Their work appears in journals such as Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry Letters, ACS Pharmacology & Translational Science, PLoS ONE and Cancer Research.
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.