Rafał Kurczab

1.8k total citations
89 papers, 1.4k citations indexed

About

Rafał Kurczab is a scholar working on Molecular Biology, Organic Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Rafał Kurczab has authored 89 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Molecular Biology, 36 papers in Organic Chemistry and 30 papers in Computational Theory and Mathematics. Recurrent topics in Rafał Kurczab's work include Computational Drug Discovery Methods (30 papers), Receptor Mechanisms and Signaling (27 papers) and Synthesis and Biological Evaluation (20 papers). Rafał Kurczab is often cited by papers focused on Computational Drug Discovery Methods (30 papers), Receptor Mechanisms and Signaling (27 papers) and Synthesis and Biological Evaluation (20 papers). Rafał Kurczab collaborates with scholars based in Poland, France and Germany. Rafał Kurczab's co-authors include Andrzej J. Bojarski, Grzegorz Satała, Paweł Zajdel, Paweł Śliwa, Mariusz P. Mitoraj, Artur Michalak, Vittorio Canale, Tom Ziegler, Anna Wesołowska and Magdalena Jastrzębska‐Więsek and has published in prestigious journals such as PLoS ONE, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Rafał Kurczab

86 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rafał Kurczab Poland 23 739 460 384 199 154 89 1.4k
Cristiano R. W. Guimarães United States 19 878 1.2× 670 1.5× 280 0.7× 116 0.6× 318 2.1× 56 1.8k
Michael C. Hutter Germany 24 1.0k 1.4× 210 0.5× 297 0.8× 99 0.5× 127 0.8× 67 1.6k
María‐Jesús Blanco United States 20 732 1.0× 724 1.6× 169 0.4× 141 0.7× 133 0.9× 36 1.7k
Anders Hogner Sweden 14 583 0.8× 477 1.0× 234 0.6× 222 1.1× 77 0.5× 19 1.2k
Paul Beroza United States 21 1.2k 1.7× 267 0.6× 362 0.9× 124 0.6× 193 1.3× 32 1.8k
Sarel F. Malan South Africa 28 864 1.2× 1.1k 2.5× 271 0.7× 224 1.1× 471 3.1× 104 2.3k
Jeffrey S. Albert United States 18 588 0.8× 370 0.8× 228 0.6× 117 0.6× 104 0.7× 35 1.0k
Daquan Gao United States 19 651 0.9× 219 0.5× 325 0.8× 186 0.9× 440 2.9× 34 1.2k
Simone Sciabola United States 21 1.1k 1.5× 240 0.5× 613 1.6× 132 0.7× 127 0.8× 36 1.6k
Wolfgang Guba Switzerland 26 1.2k 1.6× 504 1.1× 646 1.7× 280 1.4× 322 2.1× 60 2.0k

Countries citing papers authored by Rafał Kurczab

Since Specialization
Citations

This map shows the geographic impact of Rafał Kurczab'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 Rafał Kurczab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rafał Kurczab more than expected).

Fields of papers citing papers by Rafał Kurczab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rafał Kurczab. 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 Rafał Kurczab. The network helps show where Rafał Kurczab may publish in the future.

Co-authorship network of co-authors of Rafał Kurczab

This figure shows the co-authorship network connecting the top 25 collaborators of Rafał Kurczab. A scholar is included among the top collaborators of Rafał Kurczab 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 Rafał Kurczab. Rafał Kurczab 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.
Kurczab, Rafał, et al.. (2025). From NMR to AI: Do We Need 1H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?. Journal of Chemical Information and Modeling. 65(6). 2924–2939. 2 indexed citations
2.
Kurczab, Rafał, et al.. (2025). From NMR to AI: Fusing 1H and 13C Representations for Enhanced QSPR Modeling. Journal of Chemical Information and Modeling. 65(19). 10323–10337.
3.
Kurczab, Rafał, et al.. (2023). Isomeric Activity Cliffs—A Case Study for Fluorine Substitution of Aminergic G Protein-Coupled Receptor Ligands. Molecules. 28(2). 490–490. 3 indexed citations
4.
Maruszak, Wioleta, et al.. (2023). Tuning the Biological Activity of PI3Kδ Inhibitor by the Introduction of a Fluorine Atom Using the Computational Workflow. Molecules. 28(8). 3531–3531. 2 indexed citations
5.
Kurczab, Rafał, et al.. (2022). Fast and Noninvasive Hair Test for Preliminary Diagnosis of Mood Disorders. Molecules. 27(16). 5318–5318. 4 indexed citations
6.
Kurczab, Rafał, et al.. (2022). Mining anion–aromatic interactions in the Protein Data Bank. Chemical Science. 13(14). 3984–3998. 10 indexed citations
7.
Kurczab, Rafał, et al.. (2021). Data-Driven Analysis of Fluorination of Ligands of Aminergic G Protein Coupled Receptors. Biomolecules. 11(11). 1647–1647. 2 indexed citations
8.
Zajdel, Paweł, Katarzyna Grychowska, Szczepan Mogilski, et al.. (2021). Structure-Based Design and Optimization of FPPQ, a Dual-Acting 5-HT3 and 5-HT6 Receptor Antagonist with Antipsychotic and Procognitive Properties. Journal of Medicinal Chemistry. 64(18). 13279–13298. 11 indexed citations
9.
Staroń, Jakub, Ryszard Bugno, Rafał Kurczab, et al.. (2021). Tuning the activity of known drugs via the introduction of halogen atoms, a case study of SERT ligands – Fluoxetine and fluvoxamine. European Journal of Medicinal Chemistry. 220. 113533–113533. 20 indexed citations
10.
Canale, Vittorio, Séverine Chaumont‐Dubel, Rafał Kurczab, et al.. (2021). Imidazopyridine-Based 5-HT6 Receptor Neutral Antagonists: Impact of N1-Benzyl and N1-Phenylsulfonyl Fragments on Different Receptor Conformational States. Journal of Medicinal Chemistry. 64(2). 1180–1196. 15 indexed citations
11.
Hogendorf, Adam S., Rafał Kurczab, Grzegorz Satała, et al.. (2021). N-Skatyltryptamines—Dual 5-HT6R/D2R Ligands with Antipsychotic and Procognitive Potential. Molecules. 26(15). 4605–4605. 4 indexed citations
12.
Canale, Vittorio, Katarzyna Grychowska, Rafał Kurczab, et al.. (2020). A dual-acting 5-HT6 receptor inverse agonist/MAO-B inhibitor displays glioprotective and pro-cognitive properties. European Journal of Medicinal Chemistry. 208. 112765–112765. 18 indexed citations
13.
14.
Lubelska, Annamaria, Gniewomir Latacz, Magdalena Jastrzębska‐Więsek, et al.. (2019). Are the Hydantoin-1,3,5-triazine 5-HT6R Ligands a Hope to a Find New Procognitive and Anti-Obesity Drug? Considerations Based on Primary In Vivo Assays and ADME-Tox Profile In Vitro. Molecules. 24(24). 4472–4472. 17 indexed citations
15.
Grychowska, Katarzyna, Rafał Kurczab, Paweł Śliwa, et al.. (2018). Pyrroloquinoline scaffold-based 5-HT6R ligands: Synthesis, quantum chemical and molecular dynamic studies, and influence of nitrogen atom position in the scaffold on affinity. Bioorganic & Medicinal Chemistry. 26(12). 3588–3595. 13 indexed citations
16.
Cofta, Grzegorz, et al.. (2018). 7-Deacetyl-10-alkylthiocolchicine derivatives – new compounds with potent anticancer and fungicidal activity. MedChemComm. 9(10). 1708–1714. 12 indexed citations
17.
Partyka, Anna, Rafał Kurczab, Vittorio Canale, et al.. (2017). The impact of the halogen bonding on D2 and 5-HT1A/5-HT7 receptor activity of azinesulfonamides of 4-[(2-ethyl)piperidinyl-1-yl]phenylpiperazines with antipsychotic and antidepressant properties. Bioorganic & Medicinal Chemistry. 25(14). 3638–3648. 25 indexed citations
18.
Kurczab, Rafał. (2017). The evaluation of QM/MM-driven molecular docking combined with MM/GBSA calculations as a halogen-bond scoring strategy. Acta Crystallographica Section B Structural Science Crystal Engineering and Materials. 73(2). 188–194. 25 indexed citations
19.
Canale, Vittorio, Rafał Kurczab, Anna Partyka, et al.. (2014). Towards novel 5-HT7 versus 5-HT1A receptor ligands among LCAPs with cyclic amino acid amide fragments: Design, synthesis, and antidepressant properties. Part II. European Journal of Medicinal Chemistry. 92. 202–211. 18 indexed citations
20.
Kurczab, Rafał, et al.. (2013). The influence of the inactives subset generation on the performance of machine learning methods. Journal of Cheminformatics. 5(1). 17–17. 32 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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