Chad E. Townsend
Impact in
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- Computational Drug Discovery Methods
- Molecular Biology top 10%
- Chemical Synthesis and Analysis
- Protein Degradation and Inhibitors
- Protein Structure and Dynamics
- Lipid Membrane Structure and Behavior
- RNA Interference and Gene Delivery
Papers in
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- Computational Drug Discovery Methods 4
-
- Chemical Synthesis and Analysis 6
- Protein Structure and Dynamics 3
- Protein Degradation and Inhibitors 1
- Co-authors
- R. Scott Lokey (9 shared papers)Cameron R. Pye (5 shared papers)Joshua A. Schwochert (5 shared papers)Matthew R. Naylor (5 shared papers)Akihiro Furukawa (3 shared papers)Maria A. Bednarek (2 shared papers)Victoria G. Klein (2 shared papers)William M. Hewitt (2 shared papers)
- Journals
- Journal of Medicinal Chemistry (4 papers)Journal of Chemical Information and Modeling (2 papers)Journal of the American Chemical Society (1 paper)ACS Medicinal Chemistry Letters (1 paper)Organic & Biomolecular Chemistry (1 paper)
- Partner nations
- United StatesJapanSwitzerland
In The Last Decade
Chad E. Townsend
10 papers receiving 731 citations
Peers
Comparison fields: 5 of 54
- Computational Theory and Mathematics 173
- Molecular Biology 575
- Pharmaceutical Science 54
- Organic Chemistry 74
- Spectroscopy 32
Countries citing papers authored by Chad E. Townsend
This map shows the geographic impact of Chad E. Townsend'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 Chad E. Townsend with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chad E. Townsend more than expected).
Fields of papers citing papers by Chad E. Townsend
This network shows the impact of papers produced by Chad E. Townsend. 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 Chad E. Townsend. The network helps show where Chad E. Townsend may publish in the future.
Co-authors
The 25 scholars most cited alongside Chad E. Townsend, 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 | 2018 | 158 | |
| 2 | 2017 | 120 | |
| 3 | 2015 | 116 | |
| 4 | 2020 | 109 | |
| 5 | 2016 | 79 | |
| 6 | 2019 | 57 | |
| 7 | 2021 | 51 | |
| 8 | 2023 | 49 | |
| 9 | 2020 | 40 | |
| 10 | 2020 | 14 |
About Chad E. Townsend
Chad E. Townsend is a scholar working on Computational Theory and Mathematics, Molecular Biology, Pharmaceutical Science, Neurology and Nuclear Energy and Engineering, having authored 10 papers that have together received 793 indexed citations. Recurring topics across this work include Chemical Synthesis and Analysis (6 papers), Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), Drug Solubulity and Delivery Systems (1 paper), Protein Degradation and Inhibitors (1 paper) and Barrier Structure and Function Studies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (173 citations), Molecular Biology (575 citations), Pharmaceutical Science (54 citations), Organic Chemistry (74 citations) and Spectroscopy (32 citations). Chad E. Townsend has collaborated with scholars based in United States, Japan and Switzerland. Frequent co-authors include R. Scott Lokey, Cameron R. Pye, Joshua A. Schwochert, Matthew R. Naylor, Akihiro Furukawa, Maria A. Bednarek, Victoria G. Klein, William M. Hewitt, Satoshi Ono and Okimasa Okada. Their work appears in journals such as Journal of Medicinal Chemistry, Journal of Chemical Information and Modeling, Journal of the American Chemical Society, ACS Medicinal Chemistry Letters and Organic & Biomolecular Chemistry.
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.