Wei-Ven Tee
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Protein Interaction Studies and Fluorescence Analysis
- Receptor Mechanisms and Signaling
- RNA and protein synthesis mechanisms
Papers in
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- Computational Drug Discovery Methods 11
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- Protein Structure and Dynamics 12
- RNA and protein synthesis mechanisms 7
- Receptor Mechanisms and Signaling 6
- Protein Interaction Studies and Fluorescence Analysis 3
- Co-authors
- Igor N. Berezovsky (18 shared papers)Enrico Guarnera (13 shared papers)Zhen Wah Tan (8 shared papers)Saharuddin Bin Mohamad (4 shared papers)Zazali Alias (3 shared papers)Md. Zahirul Kabır (3 shared papers)Saad Tayyab (3 shared papers)Shevin Rizal Feroz (1 shared paper)
In The Last Decade
Wei-Ven Tee
22 papers receiving 668 citations
Peers
Comparison fields: 5 of 71
- Computational Theory and Mathematics 208
- Molecular Biology 548
- Pharmacology 51
- Oncology 99
- Physical and Theoretical Chemistry 32
Countries citing papers authored by Wei-Ven Tee
This map shows the geographic impact of Wei-Ven Tee'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 Wei-Ven Tee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei-Ven Tee more than expected).
Fields of papers citing papers by Wei-Ven Tee
This network shows the impact of papers produced by Wei-Ven Tee. 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 Wei-Ven Tee. The network helps show where Wei-Ven Tee may publish in the future.
Co-authors
The 13 scholars most cited alongside Wei-Ven Tee, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 82 | |
| 2 | 2018 | 73 | |
| 3 | 2020 | 71 | |
| 4 | 2018 | 56 | |
| 5 | 2016 | 52 | |
| 6 | 2019 | 46 | |
| 7 | 2021 | 38 | |
| 8 | 2020 | 33 | |
| 9 | 2024 | 32 | |
| 10 | 2020 | 30 | |
| 11 | 2022 | 29 | |
| 12 | 2022 | 27 | |
| 13 | 2017 | 26 | |
| 14 | 2022 | 23 | |
| 15 | 2022 | 23 | |
| 16 | 2024 | 16 | |
| 17 | 2025 | 5 | |
| 18 | 2025 | 4 | |
| 19 | 2016 | 3 | |
| 20 | 2025 | 1 |
About Wei-Ven Tee
Wei-Ven Tee is a scholar working on Computational Theory and Mathematics, Molecular Biology, Pharmacology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 672 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (12 papers), Computational Drug Discovery Methods (11 papers), RNA and protein synthesis mechanisms (7 papers), Receptor Mechanisms and Signaling (6 papers), Protein Interaction Studies and Fluorescence Analysis (3 papers), Drug Transport and Resistance Mechanisms (3 papers), Pharmacogenetics and Drug Metabolism (2 papers) and Monoclonal and Polyclonal Antibodies Research (2 papers). The work is most often cited by research in Computational Theory and Mathematics (208 citations), Molecular Biology (548 citations), Pharmacology (51 citations), Oncology (99 citations) and Physical and Theoretical Chemistry (32 citations). Wei-Ven Tee has collaborated with scholars based in Singapore, Malaysia and Australia. Frequent co-authors include Igor N. Berezovsky, Enrico Guarnera, Zhen Wah Tan, Saharuddin Bin Mohamad, Zazali Alias, Md. Zahirul Kabır, Saad Tayyab, Shevin Rizal Feroz, Firdaus Samsudin and Peter J. Bond. Their work appears in journals such as Journal of Molecular Biology, Nucleic Acids Research, Current Opinion in Structural Biology, Biophysical Journal and Structure.
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.