Raquel Rodríguez-Pérez

2.6k total citations · 2 hit papers
39 papers, 1.6k citations indexed

About

Raquel Rodríguez-Pérez is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Raquel Rodríguez-Pérez has authored 39 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computational Theory and Mathematics, 22 papers in Materials Chemistry and 18 papers in Molecular Biology. Recurrent topics in Raquel Rodríguez-Pérez's work include Computational Drug Discovery Methods (28 papers), Machine Learning in Materials Science (22 papers) and Metabolomics and Mass Spectrometry Studies (8 papers). Raquel Rodríguez-Pérez is often cited by papers focused on Computational Drug Discovery Methods (28 papers), Machine Learning in Materials Science (22 papers) and Metabolomics and Mass Spectrometry Studies (8 papers). Raquel Rodríguez-Pérez collaborates with scholars based in Switzerland, Germany and Sweden. Raquel Rodríguez-Pérez's co-authors include Jürgen Bajorath, Martin Vogt, Filip Miljković, Grégori Gerebtzoff, Santiago Marco, Luis Fernández, Elena Di Lascio, Nadine Schneider, Christian Feldmann and Giuseppe Pasculli and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Raquel Rodríguez-Pérez

39 papers receiving 1.6k citations

Hit Papers

Interpretation of machine learning models using shapley v... 2019 2026 2021 2023 2020 2019 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
    2020 434 citations Raquel Rodríguez-Pérez, Jürgen Bajorath Journal of Computer-Aided Molecular Design profile →
  2. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values
    2019 306 citations Raquel Rodríguez-Pérez, Jürgen Bajorath Journal of Medicinal Chemistry profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Raquel Rodríguez-Pérez Switzerland 20 644 523 367 208 125 39 1.6k
Dávid Bajusz Hungary 21 1.1k 1.7× 1.1k 2.1× 383 1.0× 152 0.7× 202 1.6× 55 2.5k
Yanlin Chen China 17 443 0.7× 473 0.9× 116 0.3× 93 0.4× 183 1.5× 65 1.3k
Siying Zhang China 30 251 0.4× 230 0.4× 356 1.0× 119 0.6× 181 1.4× 289 3.3k
Xiao Li China 30 539 0.8× 736 1.4× 136 0.4× 134 0.6× 93 0.7× 169 2.8k
David E. Leahy United Kingdom 18 338 0.5× 464 0.9× 144 0.4× 179 0.9× 144 1.2× 44 2.5k
Qingfeng Chen China 32 141 0.2× 1.4k 2.7× 184 0.5× 163 0.8× 242 1.9× 148 3.4k
Wei Lan China 35 326 0.5× 1.7k 3.2× 205 0.6× 235 1.1× 289 2.3× 179 3.8k
Yuanyuan Zhang China 27 214 0.3× 770 1.5× 105 0.3× 131 0.6× 108 0.9× 194 2.4k
D. Coomans Belgium 33 384 0.6× 597 1.1× 91 0.2× 393 1.9× 313 2.5× 128 3.4k
Evgeny Byvatov Germany 8 374 0.6× 360 0.7× 99 0.3× 113 0.5× 60 0.5× 8 852

Countries citing papers authored by Raquel Rodríguez-Pérez

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Ash, Jeremy R., Raquel Rodríguez-Pérez, Matteo Aldeghi, et al.. (2025). Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. Journal of Chemical Information and Modeling. 65(18). 9398–9411. 6 indexed citations
2.
Huth, Felix, et al.. (2024). Improving In Vitro–In Vivo Extrapolation of Clearance Using Rat Liver Microsomes for Highly Plasma Protein-Bound Molecules. Drug Metabolism and Disposition. 52(5). 345–354. 2 indexed citations
3.
Beckers, Maximilian, et al.. (2024). DeepCt: Predicting Pharmacokinetic Concentration–Time Curves and Compartmental Models from Chemical Structure Using Deep Learning. Molecular Pharmaceutics. 21(12). 6220–6233. 2 indexed citations
4.
Schneider, Nadine, et al.. (2024). UNIQUE: A Framework for Uncertainty Quantification Benchmarking. Journal of Chemical Information and Modeling. 64(22). 8379–8386. 4 indexed citations
5.
Ash, Jeremy R., Matteo Aldeghi, Raquel Rodríguez-Pérez, et al.. (2024). A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nature Machine Intelligence. 6(10). 1120–1121. 6 indexed citations
6.
Rodríguez-Pérez, Raquel, Birk Poller, Felix Huth, et al.. (2024). Drug-induced cholestasis (DIC) predictions based on in vitro inhibition of major bile acid clearance mechanisms. Archives of Toxicology. 99(1). 377–391. 2 indexed citations
7.
Gerebtzoff, Grégori, et al.. (2024). Application of machine learning models for property prediction to targeted protein degraders. Nature Communications. 15(1). 5764–5764. 16 indexed citations
8.
Volkamer, Andrea, Sereina Riniker, Eva Nittinger, et al.. (2023). Machine learning for small molecule drug discovery in academia and industry. SHILAP Revista de lepidopterología. 3. 100056–100056. 20 indexed citations
9.
Rodríguez-Pérez, Raquel, et al.. (2023). Explaining compound activity predictions with a substructure-aware loss for graph neural networks. Journal of Cheminformatics. 15(1). 8 indexed citations
10.
Pasculli, Giuseppe, et al.. (2022). EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks. iScience. 25(10). 105043–105043. 29 indexed citations
11.
Bajorath, Jürgen, Miquel Duran‐Frigola, Eli Fernández‐de Gortari, et al.. (2022). Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. Journal of Cheminformatics. 14(1). 82–82. 27 indexed citations
12.
Miljković, Filip, Raquel Rodríguez-Pérez, & Jürgen Bajorath. (2021). Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis. ACS Omega. 6(49). 33293–33299. 30 indexed citations
13.
Rodríguez-Pérez, Raquel & Jürgen Bajorath. (2021). Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics. Scientific Reports. 11(1). 14245–14245. 26 indexed citations
14.
Rodríguez-Pérez, Raquel & Jürgen Bajorath. (2021). Explainable Machine Learning for Property Predictions in Compound Optimization. Journal of Medicinal Chemistry. 64(24). 17744–17752. 50 indexed citations
15.
Rodríguez-Pérez, Raquel, et al.. (2021). Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity. ACS Omega. 6(5). 4080–4089. 14 indexed citations
16.
Rodríguez-Pérez, Raquel, Filip Miljković, & Jürgen Bajorath. (2020). Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. Journal of Cheminformatics. 12(1). 36–36. 16 indexed citations
17.
Rodríguez-Pérez, Raquel & Jürgen Bajorath. (2020). Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of Computer-Aided Molecular Design. 34(10). 1013–1026. 434 indexed citations breakdown →
18.
Miljković, Filip, Raquel Rodríguez-Pérez, & Jürgen Bajorath. (2019). Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes. Journal of Medicinal Chemistry. 63(16). 8738–8748. 45 indexed citations
19.
Rodríguez-Pérez, Raquel, R. Cortès, Antonio Pardo, et al.. (2018). Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD. Journal of Breath Research. 12(3). 36007–36007. 9 indexed citations
20.
Rodríguez-Pérez, Raquel, et al.. (2018). Multi-unit calibration rejects inherent device variability of chemical sensor arrays. Sensors and Actuators B Chemical. 265. 142–154. 31 indexed citations

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.

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