Jarmo Huuskonen

1.2k total citations
18 papers, 915 citations indexed

About

Jarmo Huuskonen is a scholar working on Computational Theory and Mathematics, Electrochemistry and Spectroscopy. According to data from OpenAlex, Jarmo Huuskonen has authored 18 papers receiving a total of 915 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Theory and Mathematics, 10 papers in Electrochemistry and 7 papers in Spectroscopy. Recurrent topics in Jarmo Huuskonen's work include Computational Drug Discovery Methods (12 papers), Electrochemical Analysis and Applications (10 papers) and Analytical Chemistry and Chromatography (7 papers). Jarmo Huuskonen is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Electrochemical Analysis and Applications (10 papers) and Analytical Chemistry and Chromatography (7 papers). Jarmo Huuskonen collaborates with scholars based in Finland, United Kingdom and Australia. Jarmo Huuskonen's co-authors include David J. Livingstone, Marja Salo, Igor V. Tetko, Jyrki Taskinen, Alessandro E. P. Villa, Jukka Rantanen, David T. Manallack, Martyn G. Ford, David W. Salt and K. Rahkamaa-Tolonen and has published in prestigious journals such as Chemosphere, Journal of Pharmaceutical Sciences and European Journal of Medicinal Chemistry.

In The Last Decade

Jarmo Huuskonen

18 papers receiving 865 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jarmo Huuskonen Finland 13 642 369 308 192 162 18 915
Dan C. Fara United States 21 466 0.7× 293 0.8× 235 0.8× 417 2.2× 302 1.9× 25 1.2k
Svetoslav Slavov United States 17 442 0.7× 190 0.5× 196 0.6× 336 1.8× 242 1.5× 42 1.1k
Annick Panaye France 18 510 0.8× 361 1.0× 146 0.5× 394 2.1× 337 2.1× 62 1.2k
Marina Shalaeva United States 16 549 0.9× 526 1.4× 121 0.4× 243 1.3× 506 3.1× 17 1.4k
Minati Kuanar United States 11 399 0.6× 198 0.5× 222 0.7× 290 1.5× 153 0.9× 28 866
IKUO MORIGUCHI Japan 17 512 0.8× 375 1.0× 143 0.5× 444 2.3× 514 3.2× 81 1.4k
Maykel Pérez González Cuba 28 1.1k 1.7× 210 0.6× 117 0.4× 588 3.1× 626 3.9× 58 1.7k
Ana Gallegos Spain 23 425 0.7× 151 0.4× 122 0.4× 284 1.5× 196 1.2× 48 1.3k
Mohammad Goodarzi Iran 20 588 0.9× 176 0.5× 108 0.4× 228 1.2× 280 1.7× 42 948
M. C. Liu China 14 420 0.7× 208 0.6× 104 0.3× 129 0.7× 268 1.7× 21 799

Countries citing papers authored by Jarmo Huuskonen

Since Specialization
Citations

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

Fields of papers citing papers by Jarmo Huuskonen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jarmo Huuskonen

This figure shows the co-authorship network connecting the top 25 collaborators of Jarmo Huuskonen. A scholar is included among the top collaborators of Jarmo Huuskonen 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 Jarmo Huuskonen. Jarmo Huuskonen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Uusitalo, Sanna, et al.. (2020). Online analysis of minerals from sulfide ore using near‐infrared Raman spectroscopy. Journal of Raman Spectroscopy. 51(6). 978–988. 11 indexed citations
2.
Huuskonen, Jarmo, David J. Livingstone, & David T. Manallack. (2008). Prediction of drug solubility from molecular structure using a drug-like training set. SAR and QSAR in environmental research. 19(3-4). 191–212. 47 indexed citations
3.
Huuskonen, Jarmo. (2003). PREDICTION OF SOIL SORPTION COEFFICIENT OF ORGANIC PESTICIDES FROM THE ATOM-TYPE ELECTROTOPOLOGICAL STATE INDICES. Environmental Toxicology and Chemistry. 22(4). 816–816. 2 indexed citations
4.
Huuskonen, Jarmo. (2003). Prediction of soil sorption coefficient of organic pesticides from the atom-type electrotopological state indices. Environmental Toxicology and Chemistry. 22(4). 816–820. 12 indexed citations
5.
Huuskonen, Jarmo. (2003). Prediction of Soil Sorption Coefficient of a Diverse Set of Organic Chemicals From Molecular Structure. Journal of Chemical Information and Computer Sciences. 43(5). 1457–1462. 30 indexed citations
6.
Huuskonen, Jarmo. (2002). QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals. Chemosphere. 50(7). 949–953. 38 indexed citations
7.
Huuskonen, Jarmo. (2001). Estimation of Aqueous Solubility in Drug Design. Combinatorial Chemistry & High Throughput Screening. 4(3). 311–316. 45 indexed citations
8.
Livingstone, David J., Martyn G. Ford, Jarmo Huuskonen, & David W. Salt. (2001). Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure. Journal of Computer-Aided Molecular Design. 15(8). 741–752. 50 indexed citations
9.
Huuskonen, Jarmo. (2001). QSAR Modeling with the Electrotopological State:  TIBO Derivatives. Journal of Chemical Information and Computer Sciences. 41(2). 425–429. 46 indexed citations
10.
Huuskonen, Jarmo. (2001). ESTIMATION OF WATER SOLUBILITY FROM ATOM-TYPE ELECTROTOPOLOGICAL STATE INDICES. Environmental Toxicology and Chemistry. 20(3). 491–491. 3 indexed citations
11.
Huuskonen, Jarmo. (2001). Estimation of water solubility from atom-type electrotopological state indices. Environmental Toxicology and Chemistry. 20(3). 491–497. 22 indexed citations
12.
Huuskonen, Jarmo. (2001). Prediction of biodegradation from the atom-type electrotopological state indices. Environmental Toxicology and Chemistry. 20(10). 2152–2157. 8 indexed citations
13.
Huuskonen, Jarmo. (2000). Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology. Journal of Chemical Information and Computer Sciences. 40(3). 773–777. 216 indexed citations
14.
Huuskonen, Jarmo, Jukka Rantanen, & David J. Livingstone. (2000). Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices. European Journal of Medicinal Chemistry. 35(12). 1081–1088. 50 indexed citations
15.
Huuskonen, Jarmo, David J. Livingstone, & Igor V. Tetko. (2000). Neural Network Modeling for Estimation of Partition Coefficient Based on Atom-Type Electrotopological State Indices. Journal of Chemical Information and Computer Sciences. 40(4). 947–955. 83 indexed citations
16.
Huuskonen, Jarmo, Alessandro E. P. Villa, & Igor V. Tetko. (1999). Prediction of partition coefficient based on atom‐type electrotopological state indices. Journal of Pharmaceutical Sciences. 88(2). 229–233. 52 indexed citations
17.
Huuskonen, Jarmo, Marja Salo, & Jyrki Taskinen. (1998). Aqueous Solubility Prediction of Drugs Based on Molecular Topology and Neural Network Modeling. Journal of Chemical Information and Computer Sciences. 38(3). 450–456. 129 indexed citations
18.
Huuskonen, Jarmo, et al.. (1997). Neural Network Modeling for Estimation of the Aqueous Solubility of Structurally Related Drugs. Journal of Pharmaceutical Sciences. 86(4). 450–454. 71 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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