Daniel Muthas
Impact in
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- Computational Drug Discovery Methods
- Pharmacology top 10%
- Pharmacogenetics and Drug Metabolism
Papers in
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- Cancer therapeutics and mechanisms 5
- Protein Structure and Dynamics 3
- Biochemical and Molecular Research 3
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- Systemic Lupus Erythematosus Research 5
- Co-authors
- Anders Karlén (8 shared papers)Scott Boyer (3 shared papers)Yogesh Sabnis (2 shared papers)Stefan Schmitt (1 shared paper)Ruth Brenk (1 shared paper)Aurijit Sarkar (1 shared paper)Ib Groth Clausen (1 shared paper)Mikael Nilsson (2 shared papers)
- Journals
- Annals of the Rheumatic Diseases (3 papers)MedChemComm (2 papers)Respiratory Research (2 papers)Journal of Peptide Science (2 papers)Bioorganic & Medicinal Chemistry (2 papers)
- Partner nations
- SwedenUnited KingdomUnited States
In The Last Decade
Daniel Muthas
33 papers receiving 889 citations
Peers
Comparison fields: 5 of 98
- Computational Theory and Mathematics 187
- Pharmacology 66
- Immunology 157
- Organic Chemistry 205
- Infectious Diseases 88
Countries citing papers authored by Daniel Muthas
This map shows the geographic impact of Daniel Muthas'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 Muthas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Muthas more than expected).
Fields of papers citing papers by Daniel Muthas
This network shows the impact of papers produced by Daniel Muthas. 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 Muthas. The network helps show where Daniel Muthas may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Muthas, 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 146 | |
| 2 | 2007 | 81 | |
| 3 | 2009 | 81 | |
| 4 | 2013 | 79 | |
| 5 | 2011 | 74 | |
| 6 | 2019 | 72 | |
| 7 | 2007 | 50 | |
| 8 | 2024 | 39 | |
| 9 | 2020 | 38 | |
| 10 | 2005 | 34 | |
| 11 | 2013 | 33 | |
| 12 | 2012 | 31 | |
| 13 | 2007 | 27 | |
| 14 | 2021 | 24 | |
| 15 | 2011 | 18 | |
| 16 | 2019 | 17 | |
| 17 | 2020 | 13 | |
| 18 | 2013 | 11 | |
| 19 | 2010 | 8 | |
| 20 | 2013 | 8 |
About Daniel Muthas
Daniel Muthas is a scholar working on Molecular Biology, Rheumatology, Infectious Diseases, Computational Theory and Mathematics and Immunology, having authored 35 papers that have together received 915 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), IL-33, ST2, and ILC Pathways (5 papers), Systemic Lupus Erythematosus Research (5 papers), Tuberculosis Research and Epidemiology (5 papers), Cancer therapeutics and mechanisms (5 papers), Protein Structure and Dynamics (3 papers), Biochemical and Molecular Research (3 papers) and Drug-Induced Hepatotoxicity and Protection (2 papers). The work is most often cited by research in Computational Theory and Mathematics (187 citations), Pharmacology (66 citations), Immunology (157 citations), Organic Chemistry (205 citations) and Infectious Diseases (88 citations). Daniel Muthas has collaborated with scholars based in Sweden, United Kingdom and United States. Frequent co-authors include Anders Karlén, Scott Boyer, Yogesh Sabnis, Stefan Schmitt, Ruth Brenk, Aurijit Sarkar, Ib Groth Clausen, Mikael Nilsson, Gerhard Böttcher and Carina Kärrman Mårdh. Their work appears in journals such as Annals of the Rheumatic Diseases, MedChemComm, Respiratory Research, Journal of Peptide Science and Bioorganic & 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.