Ana C. Puhl
- Molecular Biology
- Computational Theory and Mathematics top 1%
- Materials Chemistry
- Infectious Diseases top 10%
- Pharmacology
- Co-authors
- Sean EkinsThomas R. LaneKimberley M. ZornJennifer J. KleinAnthony J. HickeyDaniel P. RussoAlex M. ClarkFabio Urbina
- Topics
- Computational Drug Discovery Methods (14 papers)SARS-CoV-2 and COVID-19 Research (8 papers)Lysosomal Storage Disorders Research (5 papers)
- Journals
- Nature MaterialsSHILAP Revista de lepidopterologíaPLoS Pathogens
- Partner nations
- United StatesBrazilRussia
In The Last Decade
Ana C. Puhl
47 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Molecular Biology 494
- Computational Theory and Mathematics 378
- Materials Chemistry 165
- Infectious Diseases 141
- Pharmacology 88
Countries citing papers authored by Ana C. Puhl
This map shows the geographic impact of Ana C. Puhl'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 Ana C. Puhl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ana C. Puhl more than expected).
Fields of papers citing papers by Ana C. Puhl
This network shows the impact of papers produced by Ana C. Puhl. 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 Ana C. Puhl. The network helps show where Ana C. Puhl may publish in the future.
Co-authorship network of co-authors of Ana C. Puhl
This figure shows the co-authorship network connecting the top 25 collaborators of Ana C. Puhl. A scholar is included among the top collaborators of Ana C. Puhl based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ana C. Puhl. Ana C. Puhl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 4 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 4 | |
| 10 | 16 | |
| 11 | 11 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 45 | |
| 15 | 66 | |
| 16 | Exploiting machine learning for end-to-end drug discovery and developmentbreakdown → | 352 |
| 17 | 31 | |
| 18 | 2 | |
| 19 | Preparation and characterization of polymeric nanoparticles loaded with the flavonoid luteolin, by using factorial design | 31 |
| 20 | 81 |
About Ana C. Puhl
Ana C. Puhl is a scholar working on Computational Theory and Mathematics, Infectious Diseases and Virology, having authored 48 papers that have together received 1.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (14 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Lysosomal Storage Disorders Research (5 papers). The work is most often cited by research in Computational Theory and Mathematics (378 citations), Health Informatics (27 citations) and Infectious Diseases (141 citations). Ana C. Puhl has collaborated with scholars based in United States, Brazil and Russia. Frequent co-authors include Sean Ekins, Thomas R. Lane, Kimberley M. Zorn, Jennifer J. Klein, Anthony J. Hickey, Daniel P. Russo, Alex M. Clark, Fabio Urbina, Daniel H. Foil and Igor Polikarpov. Their work appears in journals such as Nature Materials, SHILAP Revista de lepidopterología and PLoS Pathogens.
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.