Uko Maran

3.8k total citations
91 papers, 2.8k citations indexed

About

Uko Maran is a scholar working on Computational Theory and Mathematics, Molecular Biology and Organic Chemistry. According to data from OpenAlex, Uko Maran has authored 91 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Computational Theory and Mathematics, 27 papers in Molecular Biology and 23 papers in Organic Chemistry. Recurrent topics in Uko Maran's work include Computational Drug Discovery Methods (57 papers), Analytical Chemistry and Chromatography (17 papers) and Free Radicals and Antioxidants (12 papers). Uko Maran is often cited by papers focused on Computational Drug Discovery Methods (57 papers), Analytical Chemistry and Chromatography (17 papers) and Free Radicals and Antioxidants (12 papers). Uko Maran collaborates with scholars based in Estonia, United States and Italy. Uko Maran's co-authors include Alan R. Katritzky, Mati Karelson, Sulev Sild, Alfonso T. García‐Sosa, Csaba Hetényi, Victor S. Lobanov, Ruslan Petrukhin, Douglas B. Tatham, Andre Lomaka and Geven Piir and has published in prestigious journals such as Journal of the American Chemical Society, Bioinformatics and Analytical Chemistry.

In The Last Decade

Uko Maran

90 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Uko Maran Estonia 29 1.3k 778 714 536 418 91 2.8k
Vsevolod Yu. Tanchuk Ukraine 15 1.0k 0.8× 922 1.2× 1.2k 1.6× 554 1.0× 364 0.9× 37 2.9k
James F. Rathman United States 28 1.4k 1.0× 1.1k 1.4× 1.2k 1.7× 396 0.7× 823 2.0× 63 3.9k
V. Е. Kuz’min Ukraine 25 2.0k 1.5× 784 1.0× 1.2k 1.7× 322 0.6× 845 2.0× 109 3.2k
Probir Kumar Ojha India 23 1.7k 1.3× 710 0.9× 797 1.1× 257 0.5× 360 0.9× 79 2.8k
Francisco Torrens Spain 29 1.5k 1.1× 915 1.2× 1.1k 1.6× 431 0.8× 574 1.4× 229 3.1k
Vijay K. Gombar United States 18 1.7k 1.3× 714 0.9× 761 1.1× 458 0.9× 298 0.7× 34 2.7k
Vladimir A. Palyulin Russia 30 1.8k 1.3× 1.7k 2.2× 1.8k 2.5× 659 1.2× 516 1.2× 277 4.9k
Andrea Mauri Italy 14 1.2k 0.9× 581 0.7× 885 1.2× 442 0.8× 371 0.9× 31 2.6k
Hua Gao China 27 1.8k 1.4× 741 1.0× 1.5k 2.2× 346 0.6× 999 2.4× 179 4.5k
Rudra Narayan Das India 20 1.8k 1.4× 777 1.0× 719 1.0× 342 0.6× 385 0.9× 27 2.9k

Countries citing papers authored by Uko Maran

Since Specialization
Citations

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

Fields of papers citing papers by Uko Maran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uko Maran

This figure shows the co-authorship network connecting the top 25 collaborators of Uko Maran. A scholar is included among the top collaborators of Uko Maran 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 Uko Maran. Uko Maran 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.
Käärik, Maike, et al.. (2024). Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon. International Journal of Molecular Sciences. 25(21). 11696–11696. 1 indexed citations
2.
3.
Piir, Geven, Sulev Sild, & Uko Maran. (2023). Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders. Chemosphere. 347. 140671–140671. 6 indexed citations
4.
Piir, Geven, et al.. (2023). Pesticide effect on earthworm lethality via interpretable machine learning. Journal of Hazardous Materials. 461. 132577–132577. 7 indexed citations
5.
Maran, Uko, et al.. (2020). Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives. SAR and QSAR in environmental research. 31(12). 905–921. 9 indexed citations
6.
Piir, Geven, Sulev Sild, & Uko Maran. (2020). Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere. 262. 128313–128313. 26 indexed citations
7.
Sidorov, Pavel, Elisabeth Davioud–Charvet, Uko Maran, et al.. (2017). QSAR modeling and chemical space analysis of antimalarial compounds. Journal of Computer-Aided Molecular Design. 31(5). 441–451. 13 indexed citations
8.
Selyutina, Anastasia, Alfonso T. García‐Sosa, Maarit Karonen, et al.. (2016). Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors. Bioorganic & Medicinal Chemistry. 24(11). 2519–2529. 33 indexed citations
9.
Sild, Sulev, et al.. (2015). QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models. Journal of Cheminformatics. 7(1). 32–32. 62 indexed citations
10.
García‐Sosa, Alfonso T. & Uko Maran. (2013). Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology1. SAR and QSAR in environmental research. 24(4). 319–331. 8 indexed citations
11.
12.
García‐Sosa, Alfonso T., Uko Maran, & Csaba Hetényi. (2012). Molecular Property Filters Describing Pharmacokinetics and Drug Binding. Current Medicinal Chemistry. 19(11). 1646–1662. 35 indexed citations
13.
García‐Sosa, Alfonso T., Csaba Hetényi, & Uko Maran. (2009). Drug efficiency indices for improvement of molecular docking scoring functions. Journal of Computational Chemistry. 31(1). 174–184. 49 indexed citations
14.
Org, Tõnis, Francesca Chignola, Csaba Hetényi, et al.. (2008). The autoimmune regulator PHD finger binds to non-methylated histone H3K4 to activate gene expression. EMBO Reports. 9(4). 370–376. 89 indexed citations
15.
Org, Tõnis, Francesca Chignola, Csaba Hetényi, et al.. (2008). The autoimmune regulator PHD finger binds to non‐methylated histone H3K4 to activate gene expression. EMBO Reports. 9(4). 370–376. 184 indexed citations
16.
Katritzky, Alan R., Alexander A. Oliferenko, Polina V. Oliferenko, et al.. (2003). A General Treatment of Solubility. 2. QSPR Prediction of Free Energies of Solvation of Specified Solutes in Ranges of Solvents. Journal of Chemical Information and Computer Sciences. 43(6). 1806–1814. 38 indexed citations
17.
Katritzky, Alan R., Dan C. Fara, Ruslan Petrukhin, et al.. (2002). The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors. Current Topics in Medicinal Chemistry. 2(12). 1333–1356. 54 indexed citations
18.
Katritzky, Alan R., Ruslan Petrukhin, Douglas B. Tatham, et al.. (2001). Interpretation of Quantitative Structure−Property and −Activity Relationships. Journal of Chemical Information and Computer Sciences. 41(3). 679–685. 99 indexed citations
19.
Karelson, Mati, Uko Maran, Yilin Wang, & Alan R. Katritzky. (1999). QSPR and QSAR Models Derived with CODESSA Multipurpose Statistical Analysis Software. 20(4 Suppl). 1–19. 5 indexed citations
20.
Tuppurainen, Kari, Simo Lötjönen, Reino Laatikainen, et al.. (1991). About the mutagenicity of chlorine-substituted furanones and halopropenals. A QSAR study using molecular orbital indices. Mutation research. Fundamental and molecular mechanisms of mutagenesis. 247(1). 97–102. 55 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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