David de Graaf
Impact in
- Pharmacology top 2%
- Drug-Induced Hepatotoxicity and Protection
- Pharmacogenetics and Drug Metabolism
- Hepatology top 5%
- Liver physiology and pathology
Papers in
-
- Melanoma and MAPK Pathways 3
- Gene Regulatory Network Analysis 3
- Oncology 6
- Drug Transport and Resistance Mechanisms 4
- Cytokine Signaling Pathways and Interactions 2
- Co-authors
- Jinghai J. Xu (2 shared papers)Peter Henstock (1 shared paper)J Chabot (1 shared paper)Arthur R. Smith (1 shared paper)Bart S. Hendriks (4 shared papers)Igor B. Roninson (4 shared papers)Douglas A. Lauffenburger (3 shared papers)Jie Zhao (1 shared paper)
- Journals
- Drug Discovery Today (3 papers)International Journal of Cancer (2 papers)Toxicological Sciences (1 paper)Advances in experimental medicine and biology (1 paper)FEBS Letters (1 paper)
- Partner nations
- United StatesSwitzerlandAustralia
In The Last Decade
David de Graaf
15 papers receiving 998 citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Pharmacology 263
- Hepatology 124
- Computational Theory and Mathematics 170
- Oncology 240
- Hematology 98
Countries citing papers authored by David de Graaf
This map shows the geographic impact of David de Graaf'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 de Graaf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David de Graaf more than expected).
Fields of papers citing papers by David de Graaf
This network shows the impact of papers produced by David de Graaf. 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 de Graaf. The network helps show where David de Graaf may publish in the future.
Co-authors
The 25 scholars most cited alongside David de Graaf, 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 | Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury Hit paper breakdown → | 2008 | 378 |
| 2 | 2002 | 140 | |
| 3 | 1995 | 98 | |
| 4 | 2006 | 91 | |
| 5 | 2008 | 75 | |
| 6 | 1996 | 70 | |
| 7 | 2011 | 64 | |
| 8 | 2007 | 36 | |
| 9 | 2015 | 29 | |
| 10 | 2011 | 16 | |
| 11 | 2013 | 11 | |
| 12 | 2010 | 10 | |
| 13 | 2010 | 4 | |
| 14 | 1998 | 2 | |
| 15 | 1996 | 2 |
About David de Graaf
David de Graaf is a scholar working on Molecular Biology, Oncology, Computational Theory and Mathematics, Infectious Diseases and Rheumatology, having authored 15 papers that have together received 1.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Drug Transport and Resistance Mechanisms (4 papers), Melanoma and MAPK Pathways (3 papers), Gene Regulatory Network Analysis (3 papers), Cytokine Signaling Pathways and Interactions (2 papers), Rheumatoid Arthritis Research and Therapies (2 papers), Drug-Induced Hepatotoxicity and Protection (2 papers) and Acute Lymphoblastic Leukemia research (2 papers). The work is most often cited by research in Pharmacology (263 citations), Hepatology (124 citations), Computational Theory and Mathematics (170 citations), Oncology (240 citations) and Hematology (98 citations). David de Graaf has collaborated with scholars based in United States, Switzerland and Australia. Frequent co-authors include Jinghai J. Xu, Peter Henstock, J Chabot, Arthur R. Smith, Bart S. Hendriks, Igor B. Roninson, Douglas A. Lauffenburger, Jie Zhao, Kevin A. Janes and Neil Kumar. Their work appears in journals such as Drug Discovery Today, International Journal of Cancer, Toxicological Sciences, Advances in experimental medicine and biology and FEBS Letters.
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.