Daniel M. Lowe
Impact in
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- Computational Drug Discovery Methods
- Organic Chemistry top 10%
- Catalytic C–H Functionalization Methods
- Catalytic Cross-Coupling Reactions
Papers in
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- Biomedical Text Mining and Ontologies 5
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- Computational Drug Discovery Methods 4
- Co-authors
- Roger A. Sayle (5 shared papers)Nadine Schneider (2 shared papers)Gregory A. Landrum (2 shared papers)Michael A. Tarselli (1 shared paper)Robert C. Glen (1 shared paper)Peter Corbett (1 shared paper)Peter Murray‐Rust (1 shared paper)Antony Williams (1 shared paper)
- Journals
- Journal of Cheminformatics (2 papers)Journal of Chemical Information and Modeling (2 papers)Bioorganic & Medicinal Chemistry (1 paper)Database (1 paper)Journal of Medicinal Chemistry (1 paper)
- Partner nations
- United KingdomSwitzerlandGermany
In The Last Decade
Daniel M. Lowe
10 papers receiving 885 citations
Peers
Comparison fields: 5 of 104
- Computational Theory and Mathematics 372
- Organic Chemistry 286
- Materials Chemistry 274
- Molecular Biology 366
- Inorganic Chemistry 74
Countries citing papers authored by Daniel M. Lowe
This map shows the geographic impact of Daniel M. Lowe'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 Daniel M. Lowe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel M. Lowe more than expected).
Fields of papers citing papers by Daniel M. Lowe
This network shows the impact of papers produced by Daniel M. Lowe. 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 Daniel M. Lowe. The network helps show where Daniel M. Lowe may publish in the future.
Co-authors
The 24 scholars most cited alongside Daniel M. Lowe, 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 | 2016 | 340 | |
| 2 | 2011 | 161 | |
| 3 | 2014 | 136 | |
| 4 | 2009 | 85 | |
| 5 | 2016 | 69 | |
| 6 | 2015 | 53 | |
| 7 | 2014 | 43 | |
| 8 | 2016 | 19 | |
| 9 | Virtual haptic cell model for operator training | 2011 | 8 |
| 10 | Extraction of Reactions from Patents using Grammars. | 2020 | 4 |
About Daniel M. Lowe
Daniel M. Lowe is a scholar working on Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Materials Chemistry and Cellular and Molecular Neuroscience, having authored 10 papers that have together received 918 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (5 papers), Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (2 papers), Intellectual Property and Patents (1 paper), Neuroscience and Neural Engineering (1 paper), Pharmacogenetics and Drug Metabolism (1 paper), Advanced Text Analysis Techniques (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Computational Theory and Mathematics (372 citations), Organic Chemistry (286 citations), Materials Chemistry (274 citations), Molecular Biology (366 citations) and Inorganic Chemistry (74 citations). Daniel M. Lowe has collaborated with scholars based in United Kingdom, Switzerland and Germany. Frequent co-authors include Roger A. Sayle, Nadine Schneider, Gregory A. Landrum, Michael A. Tarselli, Robert C. Glen, Peter Corbett, Peter Murray‐Rust, Antony Williams, Igor V. Tetko and Gianpaolo Bravi. Their work appears in journals such as Journal of Cheminformatics, Journal of Chemical Information and Modeling, Bioorganic & Medicinal Chemistry, Database and Journal of Medicinal 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.