Sulev Sild

2.0k total citations
40 papers, 1.4k citations indexed

About

Sulev Sild is a scholar working on Computational Theory and Mathematics, Molecular Biology and Spectroscopy. According to data from OpenAlex, Sulev Sild has authored 40 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computational Theory and Mathematics, 11 papers in Molecular Biology and 11 papers in Spectroscopy. Recurrent topics in Sulev Sild's work include Computational Drug Discovery Methods (27 papers), Analytical Chemistry and Chromatography (9 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Sulev Sild is often cited by papers focused on Computational Drug Discovery Methods (27 papers), Analytical Chemistry and Chromatography (9 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Sulev Sild collaborates with scholars based in Estonia, United States and United Kingdom. Sulev Sild's co-authors include Mati Karelson, Alan R. Katritzky, Uko Maran, Karl Jug, Tadeusz M. Krygowski, Geven Piir, Alfonso T. García‐Sosa, Tarmo Tamm, Yilin Wang and Victor S. Lobanov and has published in prestigious journals such as Journal of Molecular Biology, The Journal of Physical Chemistry B and Langmuir.

In The Last Decade

Sulev Sild

39 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
Sulev Sild Estonia 21 558 471 268 265 263 40 1.4k
Dimitar A. Dobchev Estonia 22 668 1.2× 473 1.0× 294 1.1× 432 1.6× 274 1.0× 46 1.6k
Svetoslav Slavov United States 17 442 0.8× 336 0.7× 196 0.7× 242 0.9× 190 0.7× 42 1.1k
Dan C. Fara United States 21 466 0.8× 417 0.9× 235 0.9× 302 1.1× 293 1.1× 25 1.2k
Jesus Vicente de Julián‐Ortiz Spain 21 927 1.7× 576 1.2× 165 0.6× 472 1.8× 295 1.1× 86 1.7k
В. В. Прокопенко Germany 4 490 0.9× 374 0.8× 138 0.5× 525 2.0× 258 1.0× 4 1.4k
A. G. Artemenko Ukraine 19 824 1.5× 416 0.9× 313 1.2× 444 1.7× 151 0.6× 56 1.3k
Alexander Makarenko Ukraine 4 432 0.8× 348 0.7× 122 0.5× 495 1.9× 237 0.9× 24 1.3k
A. J. Hopfinger United States 17 899 1.6× 564 1.2× 180 0.7× 643 2.4× 276 1.0× 36 1.7k
Minati Kuanar United States 11 399 0.7× 290 0.6× 222 0.8× 153 0.6× 198 0.8× 28 866
Andre Lomaka Estonia 13 298 0.5× 254 0.5× 216 0.8× 128 0.5× 170 0.6× 21 971

Countries citing papers authored by Sulev Sild

Since Specialization
Citations

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

Fields of papers citing papers by Sulev Sild

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sulev Sild

This figure shows the co-authorship network connecting the top 25 collaborators of Sulev Sild. A scholar is included among the top collaborators of Sulev Sild 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 Sulev Sild. Sulev Sild 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
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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.
Sild, Sulev, et al.. (2022). Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons. International Journal of Molecular Sciences. 23(14). 7534–7534. 14 indexed citations
5.
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
6.
Sild, Sulev, et al.. (2019). Logistic Classification Models for pH–Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. Journal of Chemical Information and Modeling. 59(5). 2442–2455. 18 indexed citations
7.
Piir, Geven, et al.. (2018). Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints. Environmental Health Perspectives. 126(12). 126001–126001. 61 indexed citations
8.
García‐Sosa, Alfonso T., et al.. (2015). Virtual Screening for HIV Protease Inhibitors Using a Novel Database Filtering Procedure. Molecular Informatics. 34(6-7). 485–492. 2 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
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Sild, Sulev, et al.. (2014). QSAR DataBank - an approach for the digital organization and archiving of QSAR model information. Journal of Cheminformatics. 6(1). 25–25. 38 indexed citations
12.
Piir, Geven, Sulev Sild, & Uko Maran. (2013). Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor. SAR and QSAR in environmental research. 24(3). 175–199. 9 indexed citations
13.
Kipper, Kalle, Sulev Sild, Csaba Hetényi, Jaanus Rèmme, & Aivar Liiv. (2011). Pseudouridylation of 23S rRNA helix 69 promotes peptide release by release factor RF2 but not by release factor RF1. Biochimie. 93(5). 834–844. 12 indexed citations
14.
Piir, Geven, Sulev Sild, Alessandra Roncaglioni, Emilio Benfenati, & Uko Maran. (2010). QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR and QSAR in environmental research. 21(7-8). 711–729. 21 indexed citations
15.
Schuller, Bernd, et al.. (2010). The UNICORE Rich Client: Facilitating the Automated Execution of Scientific Workflows. 238–245. 15 indexed citations
16.
Kipper, Kalle, Csaba Hetényi, Sulev Sild, Jaanus Rèmme, & Aivar Liiv. (2008). Ribosomal Intersubunit Bridge B2a Is Involved in Factor-Dependent Translation Initiation and Translational Processivity. Journal of Molecular Biology. 385(2). 405–422. 43 indexed citations
17.
Mazzatorta, Paolo, Emilio Benfenati, Bernd Schuller, et al.. (2004). OpenMolGRIND: Molecular Science and Engineering in a Grid Context.. Parallel and Distributed Processing Techniques and Applications. 775–779. 5 indexed citations
18.
Katritzky, Alan R., Tarmo Tamm, Yilin Wang, Sulev Sild, & Mati Karelson. (1999). QSPR Treatment of Solvent Scales. Journal of Chemical Information and Computer Sciences. 39(4). 684–691. 32 indexed citations
19.
Katritzky, Alan R., Yilin Wang, Sulev Sild, Tarmo Tamm, & Mati Karelson. (1998). QSPR Studies on Vapor Pressure, Aqueous Solubility, and the Prediction of Water−Air Partition Coefficients. Journal of Chemical Information and Computer Sciences. 38(4). 720–725. 129 indexed citations
20.
Katritzky, Alan R., Mati Karelson, Sulev Sild, Tadeusz M. Krygowski, & Karl Jug. (1998). Aromaticity as a Quantitative Concept. 7. Aromaticity Reaffirmed as a Multidimensional Characteristic. The Journal of Organic Chemistry. 63(15). 5228–5231. 209 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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