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List of peer reviewed publications

1. Brenke, J. K.; Salmina, E. S.; Ringelstetter, L.; Dornauer, S.; Kuzikov, M.; Rothenaigner, I.; Schorpp, K.; Giehler, F.; Gopalakrishnan, J.; Kieser, A.; Gul, S.; Tetko, I. V.; Hadian, K., Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase-Glutathione Interaction. J. Biomol. Screen. 2016, 21 (6), 596-607.

2. Chavan, S. Towards new computational tools for predicting toxicity. Linnaeus University, http://lnu.diva-portal.org/smash/record.jsf?pid=diva2:914669, 2016.

3. Chavan, S.; Abdelaziz, A.; Wiklander, J. G.; Nicholls, I. A., A k-nearest neighbor classification of hERG K(+) channel blockers. J. Comput. Aided. Mol. Des. 2016, 30 (3), 229-36.

4. Dimzon, I. K.; Fromel, T.; Knepper, T. P., Characterization of 3-Aminopropyl Oligosilsesquioxane. Anal. Chem. 2016, 88 (9), 4894-902.

5. Dimzon, I. K.; Trier, X.; Fromel, T.; Helmus, R.; Knepper, T. P.; de Voogt, P., High Resolution Mass Spectrometry of Polyfluorinated Polyether-Based Formulation. J. Am. Soc. Mass Spectrom. 2016, 27 (2), 309-18.

6. Pirovano, A.; Brandmaier, S.; Huijbregts, M. A.; Ragas, A. M.; Veltman, K.; Hendriks, A. J., QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans. Environ. Toxicol. Pharmacol. 2016, 42, 190-197.

7. Rybacka, A. A step forward in using QSARs for regulatory hazard and exposure assessment of chemicals. Umeå University, http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120223, 2016.

8. Salmina, E. S.; Haider, N.; Tetko, I. V., Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds. Molecules 2016, 21 (1), 1.

9. Abdelaziz, A.; Sushko, Y.; Novotarskyi, S.; Korner, R.; Brandmaier, S.; Tetko, I. V., Using Online Tool (iPrior) for Modeling ToxCast Assays Towards Prioritization of Animal Toxicity Testing. Comb. Chem. High T. Scr. 2015, 18 (4), 420-38.

10. Cassotti, M. QSAR study of aquatic toxicity by chemometrics methods in the framework of REACH regulation. Universita' Degli Studi di Milano- Bicocca, https://boa.unimib.it/retrieve/handle/10281/77501/114693, 2015.

11. Chavan, S.; Friedman, R.; Nicholls, I. A., Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy. Int. J. Mol. Sci. 2015, 16 (5), 11659-77.

12. Connolly, M.; Fernandez-Cruz, M. L.; Navas, J. M., Recovery of redox homeostasis altered by CuNPs in H4IIE liver cells does not reduce the cytotoxic effects of these NPs: an investigation using aryl hydrocarbon receptor (AhR) dependent antioxidant activity. Chem. Biol. Interact. 2015, 228, 57-68.

13. Dimzon, I. K. D. Analytical and Statistical Approaches in the Characterization of Synthetic Polymers. University of Amsterdam, http://hdl.handle.net/11245/1.473920, 2015.

14. Gajewska, M. A. Physiologically-based toxicokinetic and toxicodynamic modelling of single and repeated dose toxicity Technische Universitaet Muenchen, https://mediatum.ub.tum.de/node?id=1232350, 2015.

15. Ieromina, O. Effects of pesticides on aquatic macrofauna in the field. Leiden University, https://openaccess.leidenuniv.nl/handle/1887/33016, 2015.

16. Lammel, T.; Boisseaux, P.; Navas, J. M., Potentiating effect of graphene nanomaterials on aromatic environmental pollutant-induced cytochrome P450 1A expression in the topminnow fish hepatoma cell line PLHC-1. Environ Toxicol 2015, 30 (10), 1192-204.

17. Nicholls, I. A.; Chavan, S.; Golker, K.; Karlsson, B. C.; Olsson, G. D.; Rosengren, A. M.; Suriyanarayanan, S.; Wiklander, J. G., Theoretical and Computational Strategies for the Study of the Molecular Imprinting Process and Polymer Performance. Adv. Biochem. Eng. Biotechnol. 2015, 150, 25-50.

18. Pirovano, A. Quantifying biotransformation of xenobiotics in mammals. Radboud University, http://repository.ubn.ru.nl/handle/2066/147195, 2015.

19. Pirovano, A.; Brandmaier, S.; Huijbregts, M. A.; Ragas, A. M.; Veltman, K.; Hendriks, A. J., The utilisation of structural descriptors to predict metabolic constants of xenobiotics in mammals. Environ. Toxicol. Pharmacol. 2015, 39 (1), 247-58.

20. Rybacka, A.; Ruden, C.; Tetko, I. V.; Andersson, P. L., Identifying potential endocrine disruptors among industrial chemicals and their metabolites - development and evaluation of in silico tools. Chemosphere 2015, 139, 372-378.

21. Song, L. Towards understanding the toxicity of copper nanoparticles in aquatic ecosystems Leiden University, https://openaccess.leidenuniv.nl/handle/1887/33238, 2015.

22. Song, L.; Vijver, M. G.; de Snoo, G. R.; Peijnenburg, W. J., Assessing toxicity of copper nanoparticles across five cladoceran species. Environ. Toxicol. Chem. 2015, 34 (8), 1863-9.

23. Song, L.; Vijver, M. G.; Peijnenburg, W. J., Comparative toxicity of copper nanoparticles across three Lemnaceae species. Sci. Total. Environ. 2015, 518-519, 217-24.

24. Song, L.; Vijver, M. G.; Peijnenburg, W. J.; Galloway, T. S.; Tyler, C. R., A comparative analysis on the in vivo toxicity of copper nanoparticles in three species of freshwater fish. Chemosphere 2015, 139, 181-9.

25. Switnicki, M. Integrative cancer genomics analysis of gene expression and DNA methylation. Aarhus University, http://www.eco-itn.eu/sites/eco-itn.eu/files/news/PhD_thesis_Michal_Swit..., 2015.

26. Wang, Z.; Quik, J. T.; Song, L.; Van Den Brandhof, E. J.; Wouterse, M.; Peijnenburg, W. J., Humic substances alleviate the aquatic toxicity of polyvinylpyrrolidone-coated silver nanoparticles to organisms of different trophic levels. Environ. Toxicol. Chem. 2015, 34 (6), 1239-45.

27. Brandmaier, S. Experimental design methods to increase the accuracy of in silico models. Technische Universitaet Muenchen, http://mediatum.ub.tum.de?id=1187595 2014.

28. Brandmaier, S.; Peijnenburg, W.; Durjava, M. K.; Kolar, B.; Gramatica, P.; Papa, E.; Bhhatarai, B.; Kovarich, S.; Cassani, S.; Roy, P. P.; Rahmberg, M.; Oberg, T.; Jeliazkova, N.; Golsteijn, L.; Comber, M.; Charochkina, L.; Novotarskyi, S.; Sushko, I.; Abdelaziz, A.; D'Onofrio, E.; Kunwar, P.; Ruggiu, F.; Tetko, I. V., The QSPR-THESAURUS: the online platform of the CADASTER project. Altern. Lab. Anim. 2014, 42 (1), 13-24.

29. Cassotti, M.; Ballabio, D.; Consonni, V.; Mauri, A.; Tetko, I. V.; Todeschini, R., Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method. Altern. Lab. Anim. 2014, 42 (1), 31-41.

30. Chavan, S.; Nicholls, I. A.; Karlsson, B. C.; Rosengren, A. M.; Ballabio, D.; Consonni, V.; Todeschini, R., Towards global QSAR model building for acute toxicity: munro database case study. Int. J. Mol. Sci. 2014, 15 (10), 18162-74.

31. de la Casa-Resino, I.; Navas, J. M.; Fernandez-Cruz, M. L., Chlorotriazines do not activate the aryl hydrocarbon receptor, the oestrogen receptor or the thyroid receptor in in vitro assays. Altern. Lab. Anim. 2014, 42 (1), 25-30.

32. Dimzon, I. K.; Knepper, T. P., Degree of Deacetylation of Chitosan by Infrared Spectroscopy and Partial Least Squares. Int J Biol Macromol 2014, 72, 939-945.

33. Ehret, J.; Vijver, M.; Peijnenburg, W., The application of QSAR approaches to nanoparticles. Altern. Lab. Anim. 2014, 42 (1), 43-50.

34. Ieromina, O.; Peijnenburg, W. J.; de Snoo, G.; Muller, J.; Knepper, T. P.; Vijver, M. G., Impact of imidacloprid on Daphnia magna under different food quality regimes. Environ. Toxicol. Chem. 2014, 33 (3), 621-31.

35. Ieromina, O.; Peijnenburg, W. J.; de Snoo, G. R.; Vijver, M. G., Population responses of Daphnia magna, Chydorus sphaericus and Asellus aquaticus in pesticide contaminated ditches around bulb fields. Environ. Pollut. 2014, 192, 196-203.

36. Knepper, T. P.; Frömel, T.; Gremmel, C.; van Driezum, I.; Weil, H.; Vestergren, R.; Cousins, I., Understanding the exposure pathways of per- and polyfluoralkyl substances (PFASs) via use of PFASs-Containing products – risk estimation for man and environment. Section IV 2.3 Chemicals, C. S., Lena Vierke, Ed. Federal Environment Agency (Germany): Dessau-Roßlau, 2014; p. 133. http://www.umweltbundesamt.de/publikationen/understanding-the-exposure-p....
37. Kustov, L.; Tiras, K.; Al-Abed, S.; Golovina, N.; Ananyan, M., Estimation of the toxicity of silver nanoparticles by using planarian flatworms. Altern. Lab. Anim. 2014, 42 (1), 51-8.

38. Lammel, T. New insights into the toxicity of nanomaterials by means of the use of fish and mammalian cell lines. Universidad Autónoma de Madrid, http://hdl.handle.net/10486/662600, 2014.

39. Lammel, T.; Navas, J. M., Graphene nanoplatelets spontaneously translocate into the cytosol and physically interact with cellular organelles in the fish cell line PLHC-1. Aquat Toxicol 2014, 150, 55-65.

40. O'Connor, I. Modelling the oral uptake of chemicals: the role of plastic, passive diffusion and transport proteins. Radboud University, http://repository.ubn.ru.nl/handle/2066/133632, 2014.

41. O'Connor, I. A.; Veltman, K.; Huijbregts, M. A.; Ragas, A. M.; Russel, F. G.; Hendriks, A. J., Including carrier-mediated transport in oral uptake prediction of nutrients and pharmaceuticals in humans. Environ. Toxicol. Pharmacol. 2014, 38 (3), 938-47.

42. Pirovano, A.; Huijbregts, M. A.; Ragas, A. M.; Veltman, K.; Hendriks, A. J., Mechanistically-based QSARs to describe metabolic constants in mammals. Altern. Lab. Anim. 2014, 42 (1), 59-69.

43. Rathore, R. In Vitro Screening Of Selected Organic Nanomaterials With PC12, H4IIE, And T. thermophila Technical University Munich, https://mediatum.ub.tum.de/?id=1179368, 2014.

44. Rathore, R.; Schramm, K. W., Ethoxyresorufin-O-deethylase (EROD) activity modulation of 2,3,7,8-tetrachlorodibenzo-p-dioxin and 3,3',4,4',5-pentachlorobiphenyl (PCB 126) in the presence of aqueous suspensions of nano-C60. Altern. Lab. Anim. 2014, 42 (1), 71-80.

45. Sahigara, F.; Ballabio, D.; Todeschini, R.; Consonni, V., Assessing the validity of QSARs for ready biodegradability of chemicals: an applicability domain perspective. Curr. Comput. Aided Drug Des. 2014, 10 (2), 137-47.

46. Schorpp, K.; Rothenaigner, I.; Salmina, E.; Reinshagen, J.; Low, T.; Brenke, J. K.; Gopalakrishnan, J.; Tetko, I. V.; Gul, S.; Hadian, K., Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens. J. Biomol. Screen. 2014, 19 (5), 715-26.

47. Song, L.; Connolly, M.; Fernandez-Cruz, M. L.; Vijver, M. G.; Fernandez, M.; Conde, E.; de Snoo, G. R.; Peijnenburg, W. J.; Navas, J. M., Species-specific toxicity of copper nanoparticles among mammalian and piscine cell lines. Nanotoxicology 2014, 8 (4), 383-93.

48. Sushko, Y.; Novotarskyi, S.; Korner, R.; Vogt, J.; Abdelaziz, A.; Tetko, I. V., Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process. J. Cheminform. 2014, 6 (1), 48.

49. Tetko, I. V.; Schramm, K. W.; Knepper, T.; Peijnenburg, W. J.; Hendriks, A. J.; Navas, J. M.; Nicholls, I. A.; Oberg, T.; Todeschini, R.; Schlosser, E.; Brandmaier, S., Experimental and theoretical studies in the EU FP7 Marie Curie Initial Training Network Project, Environmental ChemOinformatics (ECO). Altern. Lab. Anim. 2014, 42 (1), 7-11.

50. Vorberg, S.; Tetko, I. V., Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM). Mol. Inform. 2014, 33 (1), 73-85.

51. Brandmaier, S.; Tetko, I. V., Robustness in experimental design: A study on the reliability of selection approaches. Comput. Struct. Biotechnol. J. 2013, 7, e201305002.

52. Connolly, M.; Perez, Y.; Mann, E.; Herradon, B.; Fernandez-Cruz, M. L.; Navas, J. M., Peptide-biphenyl hybrid-capped AuNPs: stability and biocompatibility under cell culture conditions. Nanoscale research letters 2013, 8 (1), 315.

53. Dimzon, I. K.; Ebert, J.; Knepper, T. P., The interaction of chitosan and olive oil: effects of degree of deacetylation and degree of polymerization. Carbohydr. Polym. 2013, 92 (1), 564-70.

54. Eschauzier, C. Perfluoroalkyl acids in drinking water: Sources, fate and removal. University of Amsterdam, http://hdl.handle.net/11245/2.129957, 2013.

55. Fernandez-Cruz, M. L.; Lammel, T.; Connolly, M.; Conde, E.; Barrado, A. I.; Derick, S.; Perez, Y.; Fernandez, M.; Furger, C.; Navas, J. M., Comparative cytotoxicity induced by bulk and nanoparticulated ZnO in the fish and human hepatoma cell lines PLHC-1 and Hep G2. Nanotoxicology 2013, 7 (5), 935-52.

56. Lammel, T.; Boisseaux, P.; Fernandez-Cruz, M. L.; Navas, J. M., Internalization and cytotoxicity of graphene oxide and carboxyl graphene nanoplatelets in the human hepatocellular carcinoma cell line Hep G2. Particle and fibre toxicology 2013, 10, 27.

57. Mansouri, K. New molecular descriptors for estimating degradation and fate of organic pollutants by QSAR/QSPR models within REACH. Università degli Studi di Milano-Bicocca, https://boa.unimib.it/handle/10281/45611, 2013.

58. Mansouri, K.; Ringsted, T.; Ballabio, D.; Todeschini, R.; Consonni, V., Quantitative structure-activity relationship models for ready biodegradability of chemicals. J. Chem. Inf. Model. 2013, 53 (4), 867-78.

59. O'Connor, I. A.; Huijbregts, M. A.; Ragas, A. M.; Hendriks, A. J., Predicting the oral uptake efficiency of chemicals in mammals: combining the hydrophilic and lipophilic range. Toxicol Appl Pharmacol 2013, 266 (1), 150-6.

60. Oprisiu, I.; Novotarskyi, S.; Tetko, I. V., Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM). J. Cheminform. 2013, 5 (1), 4.

61. Sahigara, F. Tools for prediction of environmental properties of chemiclas by QSAR/QSPR within reach. An applicability domain perspective. Università degli Studi di Milano-Bicocca, https://boa.unimib.it/handle/10281/46045, 2013.

62. Sahigara, F.; Ballabio, D.; Todeschini, R.; Consonni, V., Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J. Cheminform. 2013, 5 (1), 27.

63. Sopasakis, P. G. Modelling and Control of Biological and Physiological Systems. National Technical University of Athens, https://dspace.lib.ntua.gr/handle/123456789/7591, 2013.

64. Tetko, I. V.; Novotarskyi, S.; Sushko, I.; Ivanov, V.; Petrenko, A. E.; Dieden, R.; Lebon, F.; Mathieu, B., Development of dimethyl sulfoxide solubility models using 163 000 molecules: using a domain applicability metric to select more reliable predictions. J. Chem. Inf. Model. 2013, 53 (8), 1990-2000.

65. Tetko, I. V.; Sopasakis, P.; Kunwar, P.; Brandmaier, S.; Novotarskyi, S.; Charochkina, L.; Prokopenko, V.; Peijnenburg, W. J., Prioritisation of polybrominated diphenyl ethers (PBDEs) by using the QSPR-THESAURUS web tool. Altern. Lab. Anim. 2013, 41 (1), 127-35.

66. Todeschini, R.; Ballabio, D.; Consonni, V.; Sahigara, F.; Filzmoser, P., Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Anal Chim Acta 2013, 787, 1-9.

67. Brandmaier, S.; Sahlin, U.; Tetko, I. V.; Oberg, T., PLS-Optimal: A Stepwise D-Optimal Design Based on Latent Variables. J. Chem. Inf. Model. 2012, 52, 975-983.

68. Brandmaier, S.; Tetko, I. V.; Oberg, T., An evaluation of experimental design in QSAR modelling utilizing the k-medoid clustering. J. Chemom. 2012, 26 (10), 509-517.

69. Dimzon, I. K. D.; Knepper, T. P., TOF-MS within Food and Environmental Analysis. In Book TOF-MS within Food and Environmental Analysis, 2012; Vol. 58, pp 307-338.

70. Ding, G. H.; Fromel, T.; van den Brandhof, E. J.; Baerselman, R.; Peijnenburg, W. J., Acute toxicity of poly- and perfluorinated compounds to two cladocerans, Daphnia magna and Chydorus sphaericus. Environ. Toxicol. Chem. 2012, 31 (3), 605-10.

71. Frömel, T. Biotransformation, trace analysis and effects of perfluoroalkyl and polyfluoroalkyl substances. Technische Universität Berlin, https://depositonce.tu-berlin.de/handle/11303/3585, 2012.

72. Garcia-Galan, M. J.; Fromel, T.; Muller, J.; Peschka, M.; Knepper, T.; Diaz-Cruz, S.; Barcelo, D., Biodegradation studies of N4-acetylsulfapyridine and N4-acetylsulfamethazine in environmental water by applying mass spectrometry techniques. Anal. Bioanal. Chem. 2012, 402 (9), 2885-96.

73. Llorca, M.; Farre, M.; Pico, Y.; Muller, J.; Knepper, T. P.; Barcelo, D., Analysis of perfluoroalkyl substances in waters from Germany and Spain. Sci. Total. Environ. 2012, 431, 139-50.

74. Llorca-Casamayor, M. Analysis of perfluoroalkyl substances in food and evironmental matrices. Universitat de Barcelona, http://diposit.ub.edu/dspace/handle/2445/42842, 2012.

75. Mansouri, K.; Consonni, V.; Durjava, M. K.; Kolar, B.; Oberg, T.; Todeschini, R., Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling. Chemosphere 2012, 89 (4), 433-44.

76. Oprisiu, I. Modélisation QSPR de mélanges binaires non-additifs : application au comportement azéotropique Université de Strasbourg, http://www.sudoc.fr/171598350, 2012.

77. Pirovano, A.; Borile, N.; Jan Hendriks, A., A comparison of octanol-water partitioning between organic chemicals and their metabolites in mammals. Chemosphere 2012, 88 (8), 1036-41.

78. Pirovano, A.; Huijbregts, M. A.; Ragas, A. M.; Hendriks, A. J., Compound lipophilicity as a descriptor to predict binding affinity (1/K(m)) in mammals. Environ. Sci. Technol. 2012, 46 (9), 5168-74.

79. Sahigara, F.; Mansouri, K.; Ballabio, D.; Mauri, A.; Consonni, V.; Todeschini, R., Comparison of different approaches to define the applicability domain of QSAR models. Molecules 2012, 17 (5), 4791-810.

80. Sushko, I.; Salmina, E.; Potemkin, V. A.; Poda, G.; Tetko, I. V., ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions. J. Chem. Inf. Model. 2012, 52 (8), 2310-6.

81. Song, L.; Vijver, M. G.; Peijnenburg, W. J.; De Snoo, G. R., Smart nanotoxicity testing for biodiversity conservation. Environ. Sci. Technol. 2011, 45 (15), 6229-30.

82. Sushko, I.; Novotarskyi, S.; Korner, R.; Pandey, A. K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V.; Tanchuk, V. Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I. I.; Palyulin, V. A.; Radchenko, E. V.; Welsh, W. J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q. Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I. V., Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Comput. Aided. Mol. Des. 2011, 25 (6), 533-54.

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