Jonathan Lippy
Impact in
-
- Computational Drug Discovery Methods
-
- Cholinesterase and Neurodegenerative Diseases
Papers in ⓘ
- Co-authors
- George L. Trainor (5 shared papers)Catherine R. Burton (2 shared papers)David R. Langley (2 shared papers)Gene M. Dubowchik (2 shared papers)Carol Krause (2 shared papers)Hong Xiao (2 shared papers)John E. Macor (2 shared papers)Kevin Kish (2 shared papers)
- Journals
- Bioorganic & Medicinal Chemistry Letters (8 papers)SLAS DISCOVERY (6 papers)Analytical Biochemistry (4 papers)Drug Discovery Today (2 papers)Journal of Medicinal Chemistry (2 papers)
- Partner nations
- United StatesGermanySweden
In The Last Decade
Jonathan Lippy
25 papers receiving 488 citations
Peers
Comparison fields: 5 of 69
- Computational Theory and Mathematics 118
- Pharmacology 90
- Molecular Biology 349
- Organic Chemistry 124
- Genetics 39
Countries citing papers authored by Jonathan Lippy
This map shows the geographic impact of Jonathan Lippy'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 Jonathan Lippy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Lippy more than expected).
Fields of papers citing papers by Jonathan Lippy
This network shows the impact of papers produced by Jonathan Lippy. 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 Jonathan Lippy. The network helps show where Jonathan Lippy may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonathan Lippy, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 100 | |
| 2 | 2010 | 62 | |
| 3 | 2016 | 56 | |
| 4 | 2000 | 46 | |
| 5 | 2012 | 29 | |
| 6 | 2015 | 23 | |
| 7 | 2007 | 19 | |
| 8 | 2012 | 18 | |
| 9 | 2010 | 15 | |
| 10 | 2001 | 14 | |
| 11 | 2007 | 13 | |
| 12 | 2000 | 13 | |
| 13 | 2018 | 12 | |
| 14 | 2015 | 11 | |
| 15 | 2017 | 10 | |
| 16 | 2015 | 10 | |
| 17 | 2019 | 9 | |
| 18 | 2014 | 9 | |
| 19 | 2012 | 7 | |
| 20 | 2016 | 6 |
About Jonathan Lippy
Jonathan Lippy is a scholar working on Genetics, Oncology, Computational Theory and Mathematics, Molecular Biology and Biophysics, having authored 25 papers that have together received 505 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Protein Degradation and Inhibitors (3 papers), Cytokine Signaling Pathways and Interactions (3 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (3 papers), Receptor Mechanisms and Signaling (3 papers), Single-cell and spatial transcriptomics (2 papers), Melanoma and MAPK Pathways (2 papers) and Wnt/β-catenin signaling in development and cancer (2 papers). The work is most often cited by research in Computational Theory and Mathematics (118 citations), Pharmacology (90 citations), Molecular Biology (349 citations), Organic Chemistry (124 citations) and Genetics (39 citations). Jonathan Lippy has collaborated with scholars based in United States, Germany and Sweden. Frequent co-authors include George L. Trainor, Catherine R. Burton, David R. Langley, Gene M. Dubowchik, Carol Krause, Hong Xiao, John E. Macor, Kevin Kish, H.A. Lewis and Prasanna Sivaprakasam. Their work appears in journals such as Bioorganic & Medicinal Chemistry Letters, SLAS DISCOVERY, Analytical Biochemistry, Drug Discovery Today 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.