Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm

References

• C.L. Russom, S.P. Bradbury, S.J. Broderius, D.E. Hammermeister, and R.A. Drummond, Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas), Environ. Toxicol. Chem. 16 (1997), pp. 948–967.
   Web of Science ®Google Scholar
• A.K. Madan, S. Bajaj, and H. Dureja, Classification models for safe drug molecules, in Computational Toxicology, Vol. 930, B. Reisfeld and A.N. Mayeno, eds., Humana Press, New York, NY, 2013, pp. 99–124.
   Google Scholar
• European Parliament, Council of the European Union, Regulation (EC) of No 1907/2006 of the European parliament and of the council 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC, Off. J. Eur. Union L 396(2006), pp. 1–520.
   Google Scholar
• A.R. Katritzky, D.B. Tatham, and U. Maran, Theoretical descriptors for the correlation of aquatic toxicity of environmental pollutants by quantitative structure–toxicity relationships, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 1162–1176.
   PubMedGoogle Scholar
• E. Papa, E. Villa, and P. Gramatica, Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow), J. Chem. Inf. Model. 45 (2005), pp. 1256–1266.
   PubMed Web of Science ®Google Scholar
• A. Colombo, E. Benfenati, M. Karelson, and U. Maran, The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity, Chemosphere 72 (2008), pp. 772–780.
   PubMed Web of Science ®Google Scholar
• O.A. Raevsky, V.Y. Grigor’ev, E.E. Weber, and J.C. Dearden, Classification and quantification of the toxicity of chemicals to guppy, fathead minnow and rainbow trout: Part 1. nonpolar narcosis mode of action, QSAR Comb. Sci. 27 (2008), pp. 1274–1281.
   Google Scholar
• O.A. Raevsky, V.Y. Grigor’ev, J.C. Dearden, and E.E. Weber, Classification and quantification of the toxicity of chemicals to guppy, fathead minnow and rainbow trout. Part 2. polar narcosis mode of action, QSAR Comb. Sci. 28 (2009), pp. 163–174.
   Google Scholar
• A.P. Toropova, A.A. Toropov, A. Lombardo, A. Roncaglioni, E. Benfenati, and G. Gini, CORAL: QSAR models for acute toxicity in fathead minnow (Pimephales promelas), J. Comput. Chem. 33 (2012), pp. 1218–1223.
   PubMed Web of Science ®Google Scholar
• L.M. Su, X. Liu, Y. Wang, J.J. Li, X.H. Wang, L.X. Sheng, and Y.H. Zhao, The discrimination of excess toxicity from baseline effect: Effect of bioconcentration, Sci. Total Environ. 484 (2014), pp. 137–145.
   PubMed Web of Science ®Google Scholar
• S.P. Bradbury, Predicting modes of toxic action from chemical structure: An overview, SAR QSAR Environ. Res. 2 (1994), pp. 89–104.
   PubMedGoogle Scholar
• T.I. Netzeva, M. Pavan, and A.P. Worth, Review of (quantitative) structure–activity relationships for acute aquatic toxicity, QSAR Comb. Sci. 27 (2008), pp. 77–90.
   Google Scholar
• A.J. Hendriks, J.A. Awkerman, D. de Zwart, and M.A.J. Huijbregts, Sensitivity of species to chemicals: Dose–response characteristics for various test types (LC50, LR50 and LD50) and modes of action, Ecotoxicol. Environ. Saf. 97 (2013), pp. 10–16.
   PubMed Web of Science ®Google Scholar
• H.J.M. Verhaar, C.J. van Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants, Chemosphere 25 (1992), pp. 471–491.
   Web of Science ®Google Scholar
• M. Cassotti, D. Ballabio, R. Todeschini, and V. Consonni, A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas), SAR QSAR Environ. Res. 26 (2015), pp. 217–243.
   PubMed Web of Science ®Google Scholar
• R. Kohavi and G.H. John, Automatic parameter selection by minimizing estimated error, in Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning, A. Prieditis and S. Russell, eds., Morgan Kaufmann Publishers, San Francisco, 1995, pp. 304–312.
   Google Scholar
• M. Vračko, V. Bandelj, P. Barbieri, E. Benfenati, Q. Chaudhry, M. Cronin, J. Devillers, A. Gallegos, G. Gini, P. Gramatica, C. Helma, P. Mazzatorta, D. Neagu, T. Netzeva, M. Pavan, G. Patlewicz, M. Randić, I. Tsakovska, and A. Worth, Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: A case study, SAR QSAR Environ. Res. 17 (2006), pp. 265–284.
   PubMed Web of Science ®Google Scholar
• OECD (Organization for Economic Cooperation and Development), The report from the expert group on (quantitative) structure-activity relationships [(Q)SARs] on the principles for the validation of (Q)SARs, ENV/JM/MONO(2004)24, Organization for Economic Cooperation and Development, Paris, 2004.
   Google Scholar
• K.P. Singh, S. Gupta, and P. Rai, Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches, Ecotox. Environ. Safe. 95 (2013), pp. 221–233.
   PubMed Web of Science ®Google Scholar
• S. Agatanovic-Kustrin, D.W. Morton, and S. Razic, In silico modelling of pesticide aquatic toxicity, Comb. Chem Hight. T. Scr. 17 (2014), pp. 808–818.
   PubMed Web of Science ®Google Scholar
• Y. In, S.K. Lee, P.J. Kim, and K.T. No, Prediction of acute toxicity to fathead minnow by local model based qsar and global QSAR approaches, Bull. Korean. Chem. Soc. 33 (2012), pp. 613–619.
   Web of Science ®Google Scholar
• P. Mazzatorta, E. Benfenati, C.-D. Neagu, and G. Gini, Tuning neural and fuzzy-neural networks for toxicity modeling, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 513–518.
   PubMedGoogle Scholar
• W.M.S. Russell and R.L. Burch, The Principles of Humane Experimental Technique, Methuen, London, 1959.
   Google Scholar
• U. Maran, S. Sild, P. Mazzatorta, M. Casalegno, E. Benfenati, and M. Romberg, Grid computing for the estimation of toxicity: Acute toxicity on fathead minnow (Pimephales promelas), in Lecture Notes in Computer Science, 4360 Lecture Notes in Bioinformatics, S. Istrail, P. Pevzner, and M. Waterman, eds., Springer-Verlag, Berlin and Heidelberg, 2007, pp. 60–74.
   Google Scholar
• J. Zupan, M. Novič, and J. Gasteiger, Neural networks with counter-propagation learning strategy used for modelling, Chemometr. Intell. Lab. 27 (1995), pp. 175–187.
   Web of Science ®Google Scholar
• J. Zupan and J. Gasteiger, Neural Networks for Chemists, VCH Verlagsgesellschaft, Weinheim, Germany, An Introduction, 1993.
   Google Scholar
• M. Novič and J. Zupan, Investigation of infrared spectra-structure correlation using kohonen and counterpropagation neural network, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 454–466.
   Google Scholar
• MPICH, Argonne National Laboratory, Lemont, IL, USA; software available at http://www.mpich.org/.
   Google Scholar
• OECD Environment Directorate, Guidance document on the validation of (Q)SAR models, Health and Safety Publications, Series on Testing and Assessment No. 69, ENV/JM/MONO(2007)2, Environment Directorate, OECD, Paris, 2007.
   Google Scholar
• N. Minovski, Š. Župerl, V. Drgan, and M. Novič, Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study, Anal. Chim. Acta 759 (2013), pp. 28–42.
   PubMed Web of Science ®Google Scholar
• J. Borišek, V. Drgan, N. Minovski, and M. Novič, Mechanistic interpretation of artificial neural network-based QSAR model for prediction of cathepsin K inhibition potency, J. Chemom. 28 (2014), pp. 272–281.
   Web of Science ®Google Scholar
• P. Gramatica, N. Chirico, E. Papa, S. Cassani, and S. Kovarich, QSARINS: A new software for the development, analysis, and validation of QSAR MLR models, J. Comput. Chem. 34 (2013), pp. 2121–2132; software available at www.qsar.it.
   Google Scholar
• Y. Wang, M. Zheng, J. Xiao, Y. Lu, F. Wang, J. Lu, X. Luo, W. Zhu, H. Jiang, and K. Chen, Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow, SAR QSAR Environ. Res. 21 (2010), pp. 559–570.
   PubMed Web of Science ®Google Scholar