Raquel Rodríguez-Pérez
- Computational Theory and Mathematics top 0.5%
- Molecular Biology
- Materials Chemistry top 10%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Co-authors
- Jürgen BajorathMartin VogtFilip MiljkovićGrégori GerebtzoffSantiago MarcoLuis FernándezElena Di LascioNadine Schneider
- Topics
- Computational Drug Discovery Methods (28 papers)Machine Learning in Materials Science (22 papers)Metabolomics and Mass Spectrometry Studies (8 papers)
- Journals
- Nature CommunicationsSHILAP Revista de lepidopterologíaScientific Reports
- Partner nations
- SwitzerlandGermanySweden
In The Last Decade
Raquel Rodríguez-Pérez
39 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 175
- Computational Theory and Mathematics 644
- Molecular Biology 523
- Materials Chemistry 367
- Artificial Intelligence 208
- Biomedical Engineering 125
Countries citing papers authored by Raquel Rodríguez-Pérez
This map shows the geographic impact of Raquel Rodríguez-Pérez'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 Raquel Rodríguez-Pérez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raquel Rodríguez-Pérez more than expected).
Fields of papers citing papers by Raquel Rodríguez-Pérez
This network shows the impact of papers produced by Raquel Rodríguez-Pérez. 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 Raquel Rodríguez-Pérez. The network helps show where Raquel Rodríguez-Pérez may publish in the future.
Co-authorship network of co-authors of Raquel Rodríguez-Pérez
This figure shows the co-authorship network connecting the top 25 collaborators of Raquel Rodríguez-Pérez. A scholar is included among the top collaborators of Raquel Rodríguez-Pérez 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 Raquel Rodríguez-Pérez. Raquel Rodríguez-Pérez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 2 | |
| 7 | 16 | |
| 8 | 2 | |
| 9 | 8 | |
| 10 | 29 | |
| 11 | 27 | |
| 12 | 30 | |
| 13 | 26 | |
| 14 | 11 | |
| 15 | 14 | |
| 16 | 16 | |
| 17 | Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictionsbreakdown → | 434 |
| 18 | 9 | |
| 19 | 31 | |
| 20 | 48 |
About Raquel Rodríguez-Pérez
Raquel Rodríguez-Pérez is a scholar working on Computational Theory and Mathematics, Pharmacology and Spectroscopy, having authored 39 papers that have together received 1.6k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (28 papers), Machine Learning in Materials Science (22 papers) and Metabolomics and Mass Spectrometry Studies (8 papers). The work is most often cited by research in Computational Theory and Mathematics (644 citations), Health Informatics (25 citations) and Biophysics (50 citations). Raquel Rodríguez-Pérez has collaborated with scholars based in Switzerland, Germany and Sweden. Frequent co-authors include Jürgen Bajorath, Martin Vogt, Filip Miljković, Grégori Gerebtzoff, Santiago Marco, Luis Fernández, Elena Di Lascio, Nadine Schneider, Giuseppe Pasculli and Christian Feldmann. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.
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.