Ailsa Powell
Impact in
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- Computational Drug Discovery Methods
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
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- Computational Drug Discovery Methods 5
-
- Antibiotic Resistance in Bacteria 2
- Co-authors
- Christopher Davies (3 shared papers)Robert A. Nicholas (2 shared papers)Ashley M. Deacon (1 shared paper)Joshua Tomberg (1 shared paper)Joyce W. Lustbader (1 shared paper)R.L. Brady (1 shared paper)Jon A. Read (1 shared paper)Shi-Du Yan (1 shared paper)
- Journals
- Journal of Molecular Biology (3 papers)Science (1 paper)Journal of Biological Chemistry (1 paper)Nucleic Acids Research (1 paper)Science Advances (1 paper)
- Partner nations
- United KingdomUnited StatesHungary
In The Last Decade
Ailsa Powell
12 papers receiving 1.2k citations
Ailsa Powell's Hit Papers
Peers
Comparison fields: 5 of 109
- Computational Theory and Mathematics 416
- Infectious Diseases 352
- Molecular Medicine 51
- Structural Biology 14
- Molecular Biology 561
Countries citing papers authored by Ailsa Powell
This map shows the geographic impact of Ailsa Powell'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 Ailsa Powell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ailsa Powell more than expected).
Fields of papers citing papers by Ailsa Powell
This network shows the impact of papers produced by Ailsa Powell. 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 Ailsa Powell. The network helps show where Ailsa Powell may publish in the future.
Co-authors
The 25 scholars most cited alongside Ailsa Powell, 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 | Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease Hit paper breakdown → | 2020 | 416 |
| 2 | 2021 | 195 | |
| 3 | 2021 | 137 | |
| 4 | Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors Hit paper breakdown → | 2023 | 118 |
| 5 | 2000 | 84 | |
| 6 | 2008 | 78 | |
| 7 | 2021 | 51 | |
| 8 | 2008 | 40 | |
| 9 | 2006 | 23 | |
| 10 | 2024 | 22 | |
| 11 | 2023 | 18 | |
| 12 | 2023 | 17 |
About Ailsa Powell
Ailsa Powell is a scholar working on Computational Theory and Mathematics, Molecular Medicine, Molecular Biology, Oncology and Materials Chemistry, having authored 12 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Protein Structure and Dynamics (5 papers), Enzyme Structure and Function (5 papers), RNA and protein synthesis mechanisms (3 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Antibiotic Resistance in Bacteria (2 papers), Glycosylation and Glycoproteins Research (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (416 citations), Infectious Diseases (352 citations), Molecular Medicine (51 citations), Structural Biology (14 citations) and Molecular Biology (561 citations). Ailsa Powell has collaborated with scholars based in United Kingdom, United States and Hungary. Frequent co-authors include Christopher Davies, Robert A. Nicholas, Ashley M. Deacon, Joshua Tomberg, Joyce W. Lustbader, R.L. Brady, Jon A. Read, Shi-Du Yan, Mark J. Banfield and Frank J. Gunn‐Moore. Their work appears in journals such as Journal of Molecular Biology, Science, Journal of Biological Chemistry, Nucleic Acids Research and Science Advances.
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.