John A. Arnott
Impact in
-
- Computational Drug Discovery Methods
- Pharmacology top 5%
Papers in
-
- Connective Tissue Growth Factor Research 7
- TGF-β signaling in diseases 4
- Oncology 5
- Bone health and treatments 3
- Cytokine Signaling Pathways and Interactions 2
- Co-authors
- Sonia Lobo Planey (9 shared papers)Darshan Shah (1 shared paper)Steven N. Popoff (5 shared papers)Fayez Safadi (2 shared papers)Thomas A. Owen (2 shared papers)Fayez F. Safadi (2 shared papers)Christina Mundy (1 shared paper)Robin A. Pixley (1 shared paper)
- Journals
- PLoS ONE (3 papers)Expert Opinion on Drug Discovery (2 papers)Journal of Cellular Physiology (2 papers)Current Molecular Pharmacology (1 paper)Bone (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
John A. Arnott
18 papers receiving 1.3k citations
John A. Arnott's Hit Papers
Peers
Comparison fields: 5 of 126
- Computational Theory and Mathematics 234
- Pharmacology 75
- Toxicology 28
- Molecular Biology 545
- Organic Chemistry 235
Countries citing papers authored by John A. Arnott
This map shows the geographic impact of John A. Arnott'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 John A. Arnott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John A. Arnott more than expected).
Fields of papers citing papers by John A. Arnott
This network shows the impact of papers produced by John A. Arnott. 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 John A. Arnott. The network helps show where John A. Arnott may publish in the future.
Co-authors
The 25 scholars most cited alongside John A. Arnott, 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 | The influence of lipophilicity in drug discovery and design Hit paper breakdown → | 2012 | 640 |
| 2 | 2014 | 189 | |
| 3 | 2011 | 96 | |
| 4 | 2021 | 64 | |
| 5 | 2006 | 56 | |
| 6 | 2014 | 50 | |
| 7 | 2008 | 45 | |
| 8 | 2010 | 32 | |
| 9 | 2013 | 25 | |
| 10 | 2012 | 22 | |
| 11 | 2019 | 17 | |
| 12 | 2011 | 17 | |
| 13 | 2012 | 15 | |
| 14 | 2017 | 10 | |
| 15 | 2015 | 9 | |
| 16 | 2003 | 8 | |
| 17 | 2014 | 4 | |
| 18 | 2020 | 1 |
About John A. Arnott
John A. Arnott is a scholar working on Molecular Biology, Oncology, Genetics, Public Health, Environmental and Occupational Health and Infectious Diseases, having authored 18 papers that have together received 1.3k indexed citations. Recurring topics across this work include Connective Tissue Growth Factor Research (7 papers), TGF-β signaling in diseases (4 papers), Estrogen and related hormone effects (3 papers), Bone health and treatments (3 papers), Cytokine Signaling Pathways and Interactions (2 papers), Innovations in Medical Education (2 papers), Click Chemistry and Applications (1 paper) and Bioactive Compounds and Antitumor Agents (1 paper). The work is most often cited by research in Computational Theory and Mathematics (234 citations), Pharmacology (75 citations), Toxicology (28 citations), Molecular Biology (545 citations) and Organic Chemistry (235 citations). John A. Arnott has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Sonia Lobo Planey, Darshan Shah, Steven N. Popoff, Fayez Safadi, Thomas A. Owen, Fayez F. Safadi, Christina Mundy, Robin A. Pixley, William G. DeLong and Saqib Rehman. Their work appears in journals such as PLoS ONE, Expert Opinion on Drug Discovery, Journal of Cellular Physiology, Current Molecular Pharmacology and Bone.
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.