Predrag Kukić

1.2k total citations
37 papers, 732 citations indexed

About

Predrag Kukić is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Predrag Kukić has authored 37 papers receiving a total of 732 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 9 papers in Materials Chemistry. Recurrent topics in Predrag Kukić's work include Protein Structure and Dynamics (18 papers), Computational Drug Discovery Methods (12 papers) and Enzyme Structure and Function (9 papers). Predrag Kukić is often cited by papers focused on Protein Structure and Dynamics (18 papers), Computational Drug Discovery Methods (12 papers) and Enzyme Structure and Function (9 papers). Predrag Kukić collaborates with scholars based in United Kingdom, United States and Italy. Predrag Kukić's co-authors include Jens Erik Nielsen, Michele Vendruscolo, Carlo Camilloni, Damien Farrell, Bertrand García‐Moreno E., Kaare Teilum, Gianluca Pollastri, Kristine Steen Jensen, Lawrence P. McIntosh and Andrea Cavalli and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Predrag Kukić

36 papers receiving 722 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Predrag Kukić United Kingdom 16 538 155 83 78 76 37 732
Jianyin Shao United States 5 539 1.0× 133 0.9× 69 0.8× 96 1.2× 33 0.4× 6 747
Mojie Duan China 15 435 0.8× 79 0.5× 78 0.9× 94 1.2× 67 0.9× 56 675
Franz Gruswitz United States 8 841 1.6× 112 0.7× 79 1.0× 84 1.1× 61 0.8× 9 1.1k
Jörgen Ådén Sweden 18 655 1.2× 255 1.6× 52 0.6× 27 0.3× 178 2.3× 42 967
Carlos Amero Mexico 15 726 1.3× 195 1.3× 161 1.9× 27 0.3× 71 0.9× 38 918
Qinghua Liao Sweden 13 470 0.9× 120 0.8× 43 0.5× 85 1.1× 235 3.1× 22 655
Emeric Miclet France 17 935 1.7× 112 0.7× 182 2.2× 23 0.3× 64 0.8× 37 1.2k
Eric D. Watt United States 13 700 1.3× 177 1.1× 197 2.4× 133 1.7× 36 0.5× 22 1.1k
George White United States 10 512 1.0× 240 1.5× 58 0.7× 34 0.4× 22 0.3× 20 648
I. V. Uporov Russia 18 469 0.9× 132 0.9× 47 0.6× 24 0.3× 31 0.4× 58 754

Countries citing papers authored by Predrag Kukić

Since Specialization
Citations

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

Fields of papers citing papers by Predrag Kukić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Predrag Kukić

This figure shows the co-authorship network connecting the top 25 collaborators of Predrag Kukić. A scholar is included among the top collaborators of Predrag Kukić 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 Predrag Kukić. Predrag Kukić 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.
Rivetti, Claudia, Manuel Pablo Rubio, Danilo Basili, et al.. (2025). Unlocking the future of environmental safety: a framework for integrating new approach methodologies in decision-making. 1. 100028–100028. 1 indexed citations
2.
Bohnes, Florence Alexia, Predrag Kukić, Giles Rigarlsford, et al.. (2025). Optimizing the implementation of safe and sustainable by design to better enable sustainable innovation. iScience. 28(8). 113116–113116. 2 indexed citations
3.
Baltazar, Maria Teresa, Paul L. Carmichael, Matthew Dent, et al.. (2024). Advancing systemic toxicity risk assessment: Evaluation of a NAM-based toolbox approach. Toxicological Sciences. 204(1). 79–95. 6 indexed citations
4.
Burbank, Matthew J., Predrag Kukić, Gladys Ouédraogo, et al.. (2024). In vitro pharmacologic profiling aids systemic toxicity assessment of chemicals. Toxicology and Applied Pharmacology. 492. 117131–117131. 2 indexed citations
5.
Chen, Peiru, Yuan Li, Zhenpeng Zhang, et al.. (2023). The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration. Chemosphere. 344. 140329–140329. 4 indexed citations
6.
Zhang, Zhenpeng, Feng Xu, Kaixuan Li, et al.. (2022). Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment. Chemosphere. 313. 137359–137359. 16 indexed citations
7.
Baltazar, Maria Teresa, Paul L. Carmichael, Matthew Dent, et al.. (2022). Beyond AOPs: A Mechanistic Evaluation of NAMs in DART Testing. SHILAP Revista de lepidopterología. 4. 838466–838466. 28 indexed citations
8.
Zhang, Zhenpeng, Yao Zhang, Yuan Li, et al.. (2022). Quantitative phosphoproteomics reveal cellular responses from caffeine, coumarin and quercetin in treated HepG2 cells. Toxicology and Applied Pharmacology. 449. 116110–116110. 7 indexed citations
9.
Aleksić, Maja, et al.. (2020). Proteomic analysis of the cellular response to a potent sensitiser unveils the dynamics of haptenation in living cells. Toxicology. 445. 152603–152603. 7 indexed citations
10.
Allen, Timothy E. H., et al.. (2020). Confidence in Inactive and Active Predictions from Structural Alerts. Chemical Research in Toxicology. 33(12). 3010–3022. 3 indexed citations
11.
12.
Yoshimura, Yuichi, M. Holmberg, Predrag Kukić, et al.. (2017). MOAG-4 promotes the aggregation of α-synuclein by competing with self-protective electrostatic interactions. Journal of Biological Chemistry. 292(20). 8269–8278. 33 indexed citations
13.
Gandhi, Neha S., Predrag Kukić, Guy Lippens, & Ricardo L. Mancera. (2016). Molecular Dynamics Simulation of Tau Peptides for the Investigation of Conformational Changes Induced by Specific Phosphorylation Patterns. Methods in molecular biology. 1523. 33–59. 7 indexed citations
14.
Gandhi, Neha S., Isabelle Landrieu, Cillian Byrne, et al.. (2015). A Phosphorylation‐Induced Turn Defines the Alzheimer’s Disease AT8 Antibody Epitope on the Tau Protein. Angewandte Chemie International Edition. 54(23). 6819–6823. 43 indexed citations
15.
Kukić, Predrag, Francesco Bemporad, Francesco A. Aprile, et al.. (2015). Structure and Dynamics of the Integrin LFA-1 I-Domain in the Inactive State Underlie its Inside-Out/Outside-In Signaling and Allosteric Mechanisms. Structure. 23(4). 745–753. 15 indexed citations
16.
Kukić, Predrag, et al.. (2015). Mapping the Protein Fold Universe Using the CamTube Force Field in Molecular Dynamics Simulations. PLoS Computational Biology. 11(10). e1004435–e1004435. 14 indexed citations
17.
Kukić, Predrag, et al.. (2014). MD Simulations of Intrinsically Disordered Proteins with Replica-Averaged Chemical Shift Restraints. Biophysical Journal. 106(2). 481a–481a. 3 indexed citations
18.
Kukić, Predrag, Carlo Camilloni, Andrea Cavalli, & Michele Vendruscolo. (2014). Determination of the Individual Roles of the Linker Residues in the Inter-Domain Motions of Calmodulin using NMR Chemical Shifts. Biophysical Journal. 106(2). 636a–636a. 8 indexed citations
19.
Kukić, Predrag, Carlo Camilloni, Andrea Cavalli, & Michele Vendruscolo. (2014). Determination of the Individual Roles of the Linker Residues in the Interdomain Motions of Calmodulin Using NMR Chemical Shifts. Journal of Molecular Biology. 426(8). 1826–1838. 23 indexed citations
20.
Kukić, Predrag, Claudio Mirabello, Giuseppe Tradigo, et al.. (2014). Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks. BMC Bioinformatics. 15(1). 6–6. 33 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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