Steven Do
Impact in
- Pathology and Forensic Medicine top 10%
- Cancer Mechanisms and Therapy
- Hepatology top 10%
- Liver physiology and pathology
Papers in
-
- PI3K/AKT/mTOR signaling in cancer 6
- Protein Kinase Regulation and GTPase Signaling 5
- Protein Tyrosine Phosphatases 1
-
- Synthesis and biological activity 6
- Co-authors
- Xueyan Wang (1 shared paper)Jingrong Jean Cui (1 shared paper)Jon Burrows (1 shared paper)Emily Chan (1 shared paper)Julie M. Cherrington (1 shared paper)Jeffrey Chen (1 shared paper)Lany Ruslim (1 shared paper)John Y. Ramphal (1 shared paper)
- Journals
- Journal of Medicinal Chemistry (8 papers)Bioorganic & Medicinal Chemistry Letters (7 papers)ACS Medicinal Chemistry Letters (2 papers)Science Signaling (1 paper)Steroids (1 paper)
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Steven Do
20 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 62
- Pathology and Forensic Medicine 165
- Hepatology 79
- Molecular Biology 476
- Computational Theory and Mathematics 123
- Organic Chemistry 217
Countries citing papers authored by Steven Do
This map shows the geographic impact of Steven Do'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 Steven Do with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Do more than expected).
Fields of papers citing papers by Steven Do
This network shows the impact of papers produced by Steven Do. 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 Steven Do. The network helps show where Steven Do may publish in the future.
Co-authors
The 25 scholars most cited alongside Steven Do, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo. | 2003 | 449 |
| 2 | 2012 | 168 | |
| 3 | 2013 | 168 | |
| 4 | 2016 | 99 | |
| 5 | 2011 | 64 | |
| 6 | 2018 | 36 | |
| 7 | 2011 | 34 | |
| 8 | 2019 | 32 | |
| 9 | 2017 | 26 | |
| 10 | 2020 | 26 | |
| 11 | 2015 | 25 | |
| 12 | 2019 | 21 | |
| 13 | 2015 | 13 | |
| 14 | 2012 | 12 | |
| 15 | 2001 | 9 | |
| 16 | 2023 | 7 | |
| 17 | 2008 | 6 | |
| 18 | 2023 | 5 | |
| 19 | 2010 | 3 | |
| 20 | 2026 | 1 |
About Steven Do
Steven Do is a scholar working on Molecular Biology, Organic Chemistry, Sensory Systems, Computational Theory and Mathematics and Cancer Research, having authored 21 papers that have together received 1.2k indexed citations. Recurring topics across this work include Synthesis and biological activity (6 papers), PI3K/AKT/mTOR signaling in cancer (6 papers), Protein Kinase Regulation and GTPase Signaling (5 papers), Computational Drug Discovery Methods (3 papers), NF-κB Signaling Pathways (2 papers), Ion Channels and Receptors (2 papers), Lung Cancer Treatments and Mutations (1 paper) and Protein Tyrosine Phosphatases (1 paper). The work is most often cited by research in Pathology and Forensic Medicine (165 citations), Hepatology (79 citations), Molecular Biology (476 citations), Computational Theory and Mathematics (123 citations) and Organic Chemistry (217 citations). Steven Do has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Xueyan Wang, Jingrong Jean Cui, Jon Burrows, Emily Chan, Julie M. Cherrington, Jeffrey Chen, Lany Ruslim, John Y. Ramphal, Dirk B. Mendel and James G. Christensen. Their work appears in journals such as Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry Letters, ACS Medicinal Chemistry Letters, Science Signaling and Steroids.
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.