Igor Tetko

Dr. Igor V. Tetko

Helmholtz Zentrum München

Chemoinformatics, Group Leader
Institute of Bioinformatics and Systems Biology
Helmholtz Zentrum München
German Research Center for Environmental Health (GmbH)
Ingolstaedter Landstrasse 1,
D-85764 Neuherberg, Germany

Dr. Igor Tetko is group leader at the Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München- German Research Center for Environmental Health (GmbH). He coordinates the Virtual Computational Chemistry Laboratory, http://www.vcclab.org, which provides on-line tools for QSAR/QSPR model development and prediction of biological &physico-chemical properties of molecules. His current interests cover development of in silico ADME/T models, alternative non-animal testing methods for risk assessment with respect to REACH, estimation of the accuracy of predictions. Prior to his current position he was postdoc and then First Assistant at the University of Lausanne, Switzerland. Dr Tetko received his MSc (cum laude) at the Moscow Institute of Physics and Technology, Russia and PhD at the Institute of Bioorganic & Petrochemistry, Kyiv, Ukraine.

2001 September - present time: Senior Research Scientist/Group Leader, GSF - National Research Centre for Environment and Health, Institute for Bioinformatics, MIPS
2000 January - August 2001: Premier Assistant, Laboratory of Neuroheuristic, Institut de Physiologie, Universite de Lausanne, Switzerland
1997 - 1999: Assistant Diplome, Laboratory of Neuroheuristics, Institut de Physiologie, Universite de Lausanne, Switzerland
1996 - 1997: Postdoc, Laboratoire de Neuroheuristique, Institut de Physiologie, Universite de Lausanne, Switzerland
1995 - Senior Research Scientist, Biomedical Department, Institute of Bioorganic & Petrochemistry, Kyiv, Ukraine
1994 - 1995: SNSF grant 7-UKPJ-041507, Laboratoire de Neuro-Heuristique, Institut de Physiologie, Universite de Lausanne, Switzerland
1994 - 1995: Research Scientist, Biomedical Department, Institute of Bioorganic & Petrochemistry, Kyiv, Ukraine
1994: Candidate of Sciences (Ph.D.) in Chemistry, Doctoral thesis "Application of Artificial Neural Networks in Structure-Activity Relationship Studies"
1989 - 1994: Ph.D. student, supervisor Prof. A.I. Luik, Biomedical Department, Institute of Bioorganic & Petrochemistry, Kyiv, Ukraine
1989: M.S. in Physics Computer Sciences (cum laude), Faculty of Physical & Chemical Biology, Moscow Institute of Physics and Technology
1983 - 1989: student, Faculty of Physical & Chemical Biology, Moscow Institute of Physics and Technology

2009
1. Varnek, A.; Gaudin, C.; Marcou, G.; Baskin, I.; Pandey, A. K.; Tetko, I. V. Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients J. Chem. Inf. Model. 2009, 49 (1), 133-44.
2. Mannhold, R.; Poda, G. I.; Ostermann, C.; Tetko, I. V. Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds J. Pharm. Sci. 2009, 98 (3), 861-93.

2008
1. Zhu, H.; Tropsha, A.; Fourches, D.; Varnek, A.; Papa, E.; Gramatica, P.; Oberg, T.; Dao, P.; Cherkasov, A.; Tetko, I. V. Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis J. Chem. Inf. Model. 2008, 48 (4), 766-784.
2. Tetko, I. V.; Sushko, I.; Pandey, A. K.; Zhu, H.; Tropsha, A.; Papa, E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A., Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection J. Chem. Inf. Model. 2008, 48 (9), 1733-46.
3. Tetko, I. V.; Rodchenkov, I. V.; Walter, M. C.; Rattei, T.; Mewes, H. W., Beyond the 'best' match: machine learning annotation of protein sequences by integration of different sources of information Bioinformatics 2008, 24 (5), 621-628.
4. Tetko, I. V.; Jaroszewicz, I.; Platts, J. A.; Kuduk-Jaworska, J., Calculation of lipophilicity for Pt(II) complexes: Experimental comparison of several methods J. Inorg. Biochem. 2008, 102 (7), 1424-1437.
5. Tetko, I. V., Associative neural network Methods Mol Biol 2008, 458, 185-202.
6. Tetko, I. V., Internet in Drug Design and Discovery The Open Applied Informatics Journal 2008, 2 (1).
7. Surmeli, D.; Ratmann, O.; Mewes, H. W.; Tetko, I. V., FunCat functional inference with belief propagation and feature integration Comput Biol Chem 2008, 32 (5), 375-7.
8. Elstner, M.; Andreoli, C.; Ahting, U.; Tetko, I.; Klopstock, T.; Meitinger, T.; Prokisch, H., MitoP2: an integrative tool for the analysis of the mitochondrial proteome Mol Biotechnol 2008, 40 (3), 306-15.

2007
1. Varnek, A.; Kireeva, N.; Tetko, I. V.; Baskin, I. I.; Solov'ev, V. P., Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points? J. Chem. Inf. Model. 2007, 47 (3), 1111-1122.
2. Kovalishyn, V. V.; Kholodovych, V.; Tetko, I. V.; Welsh, W. J., Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens J. Mol. Graph. Model. 2007, 26 (2), 591-4.
3. Farre-Castany, M. A.; Schwaller, B.; Gregory, P.; Barski, J.; Mariethoz, C.; Eriksson, J. L.; Tetko, I. V.; Wolfer, D.; Celio, M. R.; Schmutz, I.; Albrecht, U.; Villa, A. E., Differences in locomotor behavior revealed in mice deficient for the calcium-binding proteins parvalbumin, calbindin D-28k or both Behav Brain Res 2007, 178 (2), 250-261.
4. Emmersen, J.; Rudd, S.; Mewes, H. W.; Tetko, I. V., Separation of sequences from host-pathogen interface using triplet nucleotide frequencies Fungal Genet Biol 2007, 44 (4), 231-241.
5. Cuomo, C. A.; Guldener, U.; Xu, J. R.; Trail, F.; Turgeon, B. G.; Di Pietro, A.; Walton, J. D.; Ma, L. J.; Baker, S. E.; Rep, M.; Adam, G.; Antoniw, J.; Baldwin, T.; Calvo, S.; Chang, Y. L.; Decaprio, D.; Gale, L. R.; Gnerre, S.; Goswami, R. S.; Hammond-Kosack, K.; Harris, L. J.; Hilburn, K.; Kennell, J. C.; Kroken, S.; Magnuson, J. K.; Mannhaupt, G.; Mauceli, E.; Mewes, H. W.; Mitterbauer, R.; Muehlbauer, G.; Munsterkotter, M.; Nelson, D.; O'Donnell, K.; Ouellet, T.; Qi, W.; Quesneville, H.; Roncero, M. I.; Seong, K. Y.; Tetko, I. V.; Urban, M.; Waalwijk, C.; Ward, T. J.; Yao, J.; Birren, B. W.; Kistler, H. C., The Fusarium graminearum genome reveals a link between localized polymorphism and pathogen specialization Science 2007, 317 (5843), 1400-2.

2006
1. Antonov, A. V.; Tetko, I. V.; Mewes, H. W. A systematic approach to infer biological relevance and biases of gene network structures, Nucleic Acids Res, 2006, 34, e6,
2. Balakin, K. V.; Savchuk, N. P.; Tetko, I. V. In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions, Curr Med Chem, 2006, 13, 223-41,
3. Ruepp, A.; Doudieu, O. N.; van den Oever, J.; Brauner, B.; Dunger-Kaltenbach, I.; Fobo, G.; Frishman, G.; Montrone, C.; Skornia, C.; Wanka, S.; Rattei, T.; Pagel, P.; Riley, L.; Frishman, D.; Surmeli, D.; Tetko, I. V.; Oesterheld, M.; Stumpflen, V.; Mewes, H. W. The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context, Nucleic Acids Res, 2006, 34, D568-71,
4. Tetko, I. V.; Bruneau, P.; Mewes, H. W.; Rohrer, D. C.; Poda, G. I. Can we estimate the accuracy of ADME-Tox predictions?, Drug Discov. Today, 2006, 11, 700-7,
5. Tetko, I. V.; Haberer, G.; Rudd, S.; Meyers, B.; Mewes, H. W.; Mayer, K. F. Spatiotemporal expression control correlates with intragenic scaffold matrix attachment regions (S/MARs) in Arabidopsis thaliana, PLoS Comput Biol, 2006, 2, e21,
6. Tetko, I. V.; Solov'ev, V. P.; Antonov, A. V.; Yao, X.; Doucet, J. P.; Fan, B.; Hoonakker, F.; Fourches, D.; Jost, P.; Lachiche, N.; Varnek, A. Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores, J Chem Inf Model, 2006, 46, 808-19,

2005
1. Antonov, A. V.; Tetko, I. V.; Kosykh, D.; Surmeli, D.; Mewes, H. W. Exploiting scale-free information from expression data for cancer classification, Comput Biol Chem, 2005, 29, 288-293,
2. Friedel, C. C.; Jahn, K. H.; Sommer, S.; Rudd, S.; Mewes, H. W.; Tetko, I. V. Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage, Bioinformatics, 2005, 21, 1383-8,
3. Poda, G. I.; Tetko, I. V.; Rohrer, D. C. Towards predictive ADME profiling of drug candidates: Lipophilicity and solubility, In 229th American Chemical Society National Meeting & Exposition; ACS: San Diego, CA, 2005, MEDI 514.
4. Rudd, S.; Tetko, I. V. Eclair--a web service for unravelling species origin of sequences sampled from mixed host interfaces, Nucleic Acids Res, 2005, 33, W724-7,.
5. Tetko, I. V. Computing chemistry on the web, Drug Discov. Today, 2005, 10, 1497-500,.
6. Tetko, I. V.; Abagyan, R.; Oprea, T. I. Surrogate data - a secure way to share corporate data, J. Comput. Aid. Mol. Des., 2005, 19, 749-64,.
7. Tetko, I. V.; Facius, A.; Ruepp, A.; Mewes, H. W. Super paramagnetic clustering of protein sequences, BMC Bioinformatics, 2005, 6, 82,.
8. Tetko, I. V.; Brauner, B.; Dunger-Kaltenbach, I.; Frishman, G.; Montrone, C.; Fobo, G.; Ruepp, A.; Antonov, A. V.; Surmeli, D.; Mewes, H. W. MIPS bacterial genomes functional annotation benchmark dataset, Bioinformatics, 2005, 21, 2520-1.
9. Tetko, I. V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V. A.; Radchenko, E. V.; Zefirov, N. S.; Makarenko, A. S.; Tanchuk, V. Y.; Prokopenko, V. V. Virtual computational chemistry laboratory - design and description, J. Comput. Aid. Mol. Des., 2005, 19, 453-63.
10. Wang, Y.; Tetko, I. V.; Hall, M. A.; Frank, E.; Facius, A.; Mayer, K. F.; Mewes, H. W. Gene selection from microarray data for cancer classification--a machine learning approach, Comput Biol Chem, 2005, 29, 37-46.

2004
1. Antonov, A. V.; Tetko, I. V.; Prokopenko, V. V.; Kosykh, D.; Mewes, H. W. A web portal for classification of expression data using maximal margin linear programming, Bioinformatics, 2004, 20, 3284-5.
2. Antonov, A. V.; Tetko, I. V.; Mader, M. T.; Budczies, J.; Mewes, H. W. Optimization models for cancer classification: extracting gene interaction information from microarray expression data, Bioinformatics, 2004, 20, 644-52.
3. Ruepp, A.; Zollner, A.; Maier, D.; Albermann, K.; Hani, J.; Mokrejs, M.; Tetko, I.; Guldener, U.; Mannhaupt, G.; Munsterkotter, M.; Mewes, H. W. The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes, Nucleic Acids Res, 2004, 32, 5539-45.
4. Schwaller, B.; Tetko, I. V.; Tandon, P.; Silveira, D. C.; Vreugdenhil, M.; Henzi, T.; Potier, M. C.; Celio, M. R.; Villa, A. E. Parvalbumin deficiency affects network properties resulting in increased susceptibility to epileptic seizures, Mol Cell Neurosci, 2004, 25, 650-63.
5. Storozhuk, V. M.; Khorevin, V. I.; Rozumna, N. M.; Villa, A. E.; Tetko, I. V. Dopamine modulation of glutamate metabotropic receptors in conditioned reaction of sensory motor cortex neurons of the cat, Neurosci Lett, 2004, 356, 127-30.
6. Tetko, I. V.; Poda, G. I. Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds, J. Med. Chem., 2004, 47, 5601-4.
7. Tetko, I. V.; Bruneau, P. Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database, J Pharm Sci, 2004, 93, 3103-10.

2003
1. Aksenova, T. I.; Chibirova, O. K.; Dryga, O. A.; Tetko, I. V.; Benabid, A. L.; Villa, A. E. An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals, Methods, 2003, 30, 178-87.
2. Aksyonova, T. I.; Volkovich, V. V.; Tetko, I. V. Robust Polynomial Neural Networks in Quantitative-Structure Activity Relationship Studies, SAMS, 2003, 43, 1331-1339.
3. Storozhuk, V. M.; Khorevin, V. I.; Razumna, N. N.; Tetko, I. V.; Villa, A. P. The effects of activation of glutamate ionotropic connections of neurons in the sensorimotor cortex in a conditioned reflex, Neurosci Behav Physiol, 2003, 33, 479-88.
4. Tetko, I. V. The WWW as a tool to obtain molecular parameters, Mini Rev Med Chem, 2003, 3, 809-20.

2002 and before
1. Tetko, I. V. Associative neural network, Neural Processing Letters, 2002, 16, 187-199.
2. Tetko, I. V. Neural network studies. 4. Introduction to associative neural networks, J. Chem. Inf. Comput. Sci., 2002, 42, 717-28.
3. Tetko, I. V.; Tanchuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program, J. Chem. Inf. Comput. Sci., 2002, 42, 1136-45.
4. Dimoglo, A. S.; Shvets, N. M.; Tetko, I. V.; Livingstone, D. J. Electronic-topologic investigation of the structure-acetylcholinesterase inhibitor activity relationship in the series of N-benzylpiperidine derivatives, Quant. Struct.-Activ. Rel., 2001, 20, 31-45.
5. Tetko, I. V.; Kovalishyn, V. V.; Livingstone, D. J. Volume learning algorithm artificial neural networks for 3D QSAR studies, J. Med. Chem., 2001, 44, 2411-20.
6. Tetko, I. V.; Tanchuk, V. Y.; Kasheva, T. N.; Villa, A. E. Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds, J. Chem. Inf. Comput. Sci., 2001, 41, 246-52.
7. Tetko, I. V.; Tanchuk, V. Y.; Kasheva, T. N.; Villa, A. E. Estimation of aqueous solubility of chemical compounds using E-state indices, J. Chem. Inf. Comput. Sci., 2001, 41, 1488-93.
8. Tetko, I. V.; Tanchuk, V. Y.; Villa, A. E. Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices, J. Chem. Inf. Comput. Sci., 2001, 41, 1407-21.
9. Tetko, I. V. Associative neural network, http://cogprints.org/1441, 2001.
10. Aksenova, T. I.; Tetko, I. V.; Ivakhnenko, A. G.; Villa, A. E.; Welsh, W. J.; Zielinski, W. L. Pharmaceutical fingerprinting in phase space. 1. Construction of phase fingerprints, Anal Chem, 1999, 71, 2423-30.
11. Tetko, I. V.; Aksenova, T. I.; Patiokha, A. A.; Villa, A. E.; Welsh, W. J.; Zielinski, W. L.; Livingstone, D. J. Pharmaceutical fingerprinting in phase space. 2. Pattern recognition, Anal Chem, 1999, 71, 2431-9.
12. Kovalishyn, V. V.; Tetko, I. V.; Luik, A. I.; Kholodovych, V. V.; Villa, A. E. P.; Livingstone, D. J. Neural network studies. 3. Variable selection in the cascade-correlation learning architecture, J. Chem. Inf. Comput. Sci., 1998, 38, 651-659.
13. Tetko, I. V.; Villa, A. E.; Aksenova, T. I.; Zielinski, W. L.; Brower, J.; Collantes, E. R.; Welsh, W. J. Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting, J. Chem. Inf. Comput. Sci., 1998, 38, 660-8.
14. Tetko, I. V.; Villa, A. E. P. Efficient partition of learning data sets for neural network training, Neural Networks, 1997, 10, 1361-1374.
15. Tetko, I. V.; Villa, A. E.; Livingstone, D. J. Neural network studies. 2. Variable selection, J. Chem. Inf. Comput. Sci., 1996, 36, 794-803.
16. Tetko, I. V.; Livingstone, D. J.; Luik, A. I. Neural network studies. 1. Comparison of overfitting and overtraining, J. Chem. Inf. Comput. Sci., 1995, 35, 826-833.
17. Tetko, I. V.; Tanchuk, V.; Chentsova, N. P.; Antonenko, S. V.; Poda, G. I.; Kukhar, V. P.; Luik, A. I. HIV-1 reverse transcriptase inhibitor design using artificial neural networks, J. Med. Chem., 1994, 37, 2520-6.
18. Tetko, I. V.; Tanchuk, V. Y.; Luik, A. I. Application of an Evolutionary Algorithm to the Structure-Activity Relationship, In Proceedings 3rd Annual Conference on Evolutionary Programming; Sebald, A. V., Fogel, L. J., Eds.; World Scientific: River Edge, NJ, 1994, p 109-119.
19. Tetko, I. V.; Luik, A. I.; Poda, G. I. Applications of neural networks in structure-activity relationships of a small number of molecules, J. Med. Chem., 1993, 36, 811-4.