David A. Thorner
Impact in
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- Computational Drug Discovery Methods
- Spectroscopy top 10%
- Analytical Chemistry and Chromatography
- Mass Spectrometry Techniques and Applications
Papers in
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- Computational Drug Discovery Methods 8
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- Metabolomics and Mass Spectrometry Studies 3
- Protein Structure and Dynamics 2
- Co-authors
- Peter Willett (3 shared papers)Richard A. Lewis (2 shared papers)Michael J. Bodkin (3 shared papers)Thompson N. Doman (2 shared papers)David A. Evans (2 shared papers)Rajendra Kristam (1 shared paper)David Wild (2 shared papers)Valerie J. Gillet (1 shared paper)
- Journals
- Rapid Communications in Mass Spectrometry (3 papers)Journal of Chemical Information and Modeling (2 papers)Perspectives in Drug Discovery and Design (1 paper)Journal of Computer-Aided Molecular Design (1 paper)Bioinformatics (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
David A. Thorner
12 papers receiving 283 citations
Peers
Comparison fields: 5 of 65
- Computational Theory and Mathematics 172
- Spectroscopy 78
- Pharmacology 25
- Toxicology 9
- Molecular Biology 146
Countries citing papers authored by David A. Thorner
This map shows the geographic impact of David A. Thorner'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 David A. Thorner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David A. Thorner more than expected).
Fields of papers citing papers by David A. Thorner
This network shows the impact of papers produced by David A. Thorner. 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 David A. Thorner. The network helps show where David A. Thorner may publish in the future.
Co-authors
The 25 scholars most cited alongside David A. Thorner, 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 | 2007 | 61 | |
| 2 | 2005 | 61 | |
| 3 | 1996 | 49 | |
| 4 | 1997 | 32 | |
| 5 | 2001 | 31 | |
| 6 | 1990 | 23 | |
| 7 | 1992 | 22 | |
| 8 | 1990 | 12 | |
| 9 | 2012 | 9 | |
| 10 | 1995 | 2 | |
| 11 | 1998 | 2 | |
| 12 | 2007 | 1 |
About David A. Thorner
David A. Thorner is a scholar working on Computational Theory and Mathematics, Molecular Biology, Spectroscopy, Materials Chemistry and Organic Chemistry, having authored 12 papers that have together received 305 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), Analytical Chemistry and Chromatography (5 papers), Machine Learning in Materials Science (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers), Mass Spectrometry Techniques and Applications (3 papers), Protein Structure and Dynamics (2 papers), Advanced Proteomics Techniques and Applications (1 paper) and Spectroscopy and Quantum Chemical Studies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (172 citations), Spectroscopy (78 citations), Pharmacology (25 citations), Toxicology (9 citations) and Molecular Biology (146 citations). David A. Thorner has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Peter Willett, Richard A. Lewis, Michael J. Bodkin, Thompson N. Doman, David A. Evans, Rajendra Kristam, David Wild, Valerie J. Gillet, John F. J. Todd and Richard Smith. Their work appears in journals such as Rapid Communications in Mass Spectrometry, Journal of Chemical Information and Modeling, Perspectives in Drug Discovery and Design, Journal of Computer-Aided Molecular Design and Bioinformatics.
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.