Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis

References

• R. Garg, A. Kurup, and C. Hansch, Comparative QSAR: On the toxicology of the phenolic OH moiety, Crit. Rev. Toxicol. 31 (2001), pp. 223–245.
   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
• H.H.P. Fang, T. Chen, Y.Y. Li, and H.K. Chui, Degradation of phenol in wastewater in an upflow anaerobic sludge blanket reactor, Water Res. 30 (1996), pp. 1353–1360.
   Web of Science ®Google Scholar
• N. Caza, J.K. Bewtra, N. Biswas, and K.E. Taylor, Removal of phenolic compounds from synthetic wastewater using soybean peroxidase, Water Res. 33 (1999), pp. 3012–3018.
   Web of Science ®Google Scholar
• U.G. Ahlborg, and T. Thunberg, Chlorinated phenols: Occurrence, toxicity, metabolism, and environmental impact, Crit. Rev. Toxicol. 7 (1980), pp. 1–35.
   PubMed Web of Science ®Google Scholar
• E. Argese, C. Bettiol, A. Ghelli, R. Todeschini, and P. Miana, Submitochondrial particles as toxicity biosensors of chlorophenols, Environ. Toxicol. Chem. 14 (1995), pp. 363–368.
   Web of Science ®Google Scholar
• M.P. Moulton and T.W. Schultz, Comparisons of several structure-toxicity relationship for chlorophenols, Aquat. Toxicol. 8 (1986), pp. 121–128.
   Web of Science ®Google Scholar
• C.M. Auer, J.V. Nabholz, and K.P. Baetcke, Mode of action and the assessment of chemical hazards in the presence of limited data: Use of structure-activity relationships (SAR) under TSCA, Section 5, Environ. Health Perspect. 87 (1990), pp. 183–197.
   PubMed Web of Science ®Google Scholar
• T. Ivanciuc, O. Ivanciuc, and D.J. Klein, Posetic quantitative superstructure/activity relationships (QSSARs) for chlorobenzenes, J. Chem. Inf. Model. 45 (2005), pp. 870–879.
   PubMed Web of Science ®Google Scholar
• S. Spycher, B.I. Escher, and J. Gasteiger, A quantitative structure–activity relationship model for the intrinsic activity of uncouplers of oxidative phosphorylation, Chem. Res. Toxicol. 18 (2005), pp. 1858–1867.
   PubMed Web of Science ®Google Scholar
• C.L. Russom, S.P. Bradbury, S.J. Broderius, D. Hammermeister, and R.A.E. 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.O. Aptula, T.I. Netzeva, I.V. Valkova, M.T.D. Cronin, and T.W. Schultz, Multivariate discrimination between modes of toxic action of phenols, Quant. Struct. Act. Relat 21 (2002), pp. 12–22.
   Google Scholar
• S. Ren, Determining the mechanisms of toxic action of phenols to Tetrahymena pyriformis, Environ. Toxicol. 17 (2002), pp. 119–127.
   PubMed Web of Science ®Google Scholar
• S. Ren, Use of molecular descriptors in separating phenols by three mechanisms of toxic action, Quant. Struct.-Act. Relat. 21 (2002), pp. 486–492.
   Web of Science ®Google Scholar
• S.P. Bradbury, C.L. Russom, G.T. Ankley, T.W. Schultz, and J.D. Walker, Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals, Environ. Toxicol. Chem. 22 (2003), pp. 1789–1798.
   PubMed Web of Science ®Google Scholar
• G. Schuurmann, A.O. Aptula, R. Kuhne, and R.U. Ebert, Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay, Chem. Res. Toxicol. 16 (2003), pp. 974–987.
   PubMed Web of Science ®Google Scholar
• S. Ren, P.D. Frymier, and T.W. Schultz, An exploratory study of the use of multivariate techniques to determine mechanisms of toxic action, Ecotoxicol. Environ. Saf. 55 (2003), pp. 86–97.
   PubMed Web of Science ®Google Scholar
• S. Ren, and H. Kimand, Comparative assessment of multiresponse regression methods for predicting the mechanisms of toxic action of phenols, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 2106–2110.
   PubMedGoogle Scholar
• S. Spycher, E. Pellegrini, and J. Gasteiger, Use of structure descriptors to discriminate between modes of toxic action of phenols, J. Chem. Inf. Model. 45 (2005), pp. 200–8.
   PubMed Web of Science ®Google Scholar
• S.J. Enoch, M.T.D. Cronin, T.W. Schultz, and J.C. Madden, An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 71 (2008), pp. 1225–1232.
   PubMed Web of Science ®Google Scholar
• G. Melagraki, A. Afantitis, H. Sarimveis, O. Igglessi-Markopoulou, and A. Alexandridis, A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri, Mol. Divers. 10 (2006), pp. 213–221.
   PubMed Web of Science ®Google Scholar
• J. Guo, H. Chen, Z. Sun, and Y. Lin, A novel method for protein secondary structure prediction using dual-layer SVM and profiles, Proteins 54 (2004), pp. 738–743.
   PubMed Web of Science ®Google Scholar
• J. Zupan and J. Gasteiger, Neural Networks for Chemistry and Drug Design, Wiley-VCH Publishers, Weinheim, Germany, 1999.
   Google Scholar
• S. Anzali, G. Barnickel, M. Krug, J. Sadowski, A. Teckentrup, and M. Wagener, The use of self-organizing neural networks in drug design, in 3D QSAR in Drug Design, H. Kubinyi, G. Folkers and Y.C. Martin, eds., Kluwer/ESCOM, Dordrecht, The Nederlands, 1998, pp. 273–299.
   Google Scholar
• J.R. Quinlan, Induction of decision trees, Machine Learn. 1 (1986), pp. 81–106.
   Google Scholar
• T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inform. Theory 13 (1967), pp. 21–27.
   Web of Science ®Google Scholar
• D. Aha and D. Kibler, Instance-based learning algorithms, Machine Learn. 6 (1991), pp. 37–66.
   Web of Science ®Google Scholar
• P. Larrañaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J.A. Lozano, R. Armañanzas, G. Santafé, A. Pérez, and V. Robles, Machine learning in bioinformatics, Brief. Bioinform. 7 (2006), pp. 86–112.
   PubMed Web of Science ®Google Scholar
• Y. Marrero-Ponce, E. Martínez-Albelo, G. Casañola-Martín, J.A. Castillo-Garit, Y. Echevería-Díaz, V. Zaldivar, J. Tygat, J. Borges, R. García-Domenech, F. Torrens, and F. Pérez-Giménez, Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules, Mol. Div. 14 (2010), pp. 731–753.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, O. Del Toro-Cortés, V.V. Kouznetsov, C.O. Puentes, A.R. Romero Bohórquez, M.C. Vega, M. Rolón, J.A. Escario, A. Gómez-Barrio, Y. Marrero-Ponce, F. Torrens, and C. Abad, Identification in silico and in vitro of novel trypanosomicidal drug-like compounds, Chem. Biol. Drug Des. 80 (2012), pp. 38–45.
   PubMed Web of Science ®Google Scholar
• Y. Brito-Sánchez, J.A. Castillo-Garit, H. Le-Thi-Thu, Y. González-Madariaga, F. Torrens, Y. Marrero-Ponce, and J.E. Rodríguez-Borges, Comparative study to predict toxic modes of action of phenols from molecular structures, SAR QSAR Environ. Res. 24 (2013), pp. 235–251.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, O. del Toro-Cortés, M.C. Vega, M. Rolón, A. Rojas de Arias, G.M. Casañola-Martin, J.A. Escario, A. Gómez-Barrio, Y. Marrero-Ponce, F. Torrens, and C. Abad, Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening, Eur. J. Med. Chem. 96 (2015), pp. 238–244.
   PubMed Web of Science ®Google Scholar
• G.M. Casanola-Martin, H. Le-Thi-Thu, Y. Marrero-Ponce, J.A. Castillo-Garit, F. Torrens, F. Perez-Gimenez, and C. Abad, Analysis of proteasome inhibition prediction using atom-based quadratic indices enhanced by machine learning classification techniques, Lett. Drug Des. Discovery 11 (2014), pp. 705–711.
   Web of Science ®Google Scholar
• J.A. Castillo-Garit, Y. Marrero-Ponce, S.J. Barigye, R. Medina-Marrero, M.G. Bernal, J.M.G.d.l. Vega, F. Torrens, V.J. Arán, F. Pérez-Giménez, R. García-Domenech, and R. Acevedo-Barrios, In silico antibacterial activity modeling based on the TOMOCOMD-CARDD approach, J. Braz. Chem. Soc. 26 (2015), pp. 1218–1226.
   Web of Science ®Google Scholar
• Y. Cañizares-Carmenate, K. Mena-Ulecia, Y. Perera-Sardiña, F. Torrens, and J.A. Castillo-Garit, An approach to identify new antihypertensive agents using Thermolysin as model: In silico study based on QSARINS and docking, Arab. J. Chem. Available at https://doi.org/10.1016/j.arabjc.2016.10.003.
   Web of Science ®Google Scholar
• J.A. Castillo-Garit, C. Abad, G.M. Casañola-Martin, S.J. Barigye, F. Torrens, and A. Torreblanca, Prediction of aquatic toxicity of benzene derivatives to Tetrahymena pyriformis according to OECD principles, Curr. Pharm. Des. 22 (2016), pp. 5085–5094.
   PubMed Web of Science ®Google Scholar
• G.M. Casañola-Martin, H. Le-Thi-Thu, Y. Marrero-Ponce, J.A. Castillo-Garit, F. Torrens, A. Rescigno, C. Abad, and M. Tareq, Hassan Khan, Tyrosinase enzyme: 1. An overview on a pharmacological target, Curr. Top. Med. Chem. 14 (2014), pp. 1494–1501.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, G.M. Casañola-Martin, H. Le-Thi-Thu, H. Pham-The, and S.J. Barigye, A simple method to predict blood-brain barrier permeability of drug- like compounds using classification trees, Med. Chem. 13 (2017), p.https://doi.org/10.2174/1573406413666170209124302. Available online since October 10, 2016.
   PubMed Web of Science ®Google Scholar
• T.W. Schultz, TERATOX: Tetrahymena pyriformis population grow impairment endpoint- A surrogate for fish lethality, Toxicol. Meth. 7 (1997), pp. 289–309.
   Google Scholar
• C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, The Chemistry Development Kit (CDK): An open-source java library for chemo- and bioinformatics, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 493–500.
   PubMedGoogle Scholar
• D.H. Wolpert and W.G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput. 1 (1997), pp. 67–82.
   Google Scholar
• John C. Platt, Fast training of support vector machines using sequential minimal optimization, in Advances in Kernel Methods, Bernhard Schölkopf, Christophe J. C. Burges and Alexander J. Smola, eds., MIT Press, Cambridge, MA, USA, 1999, pp. 185–208.
   Google Scholar
• R.P. Lippmann, An introduction to computing with neural nets, ACM SIGARCH Comput. Arch. News 16 (1988), pp. 7–25.
   Google Scholar
• R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
   Google Scholar
• K. Vanhoof, Performance measures, in Conceptual Course on Data Mining, Universidad Central ‘Martha Abreu’ de Las Villas, Santa Clara, Cuba, 2006, p. 101.
   Google Scholar
• I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2005.
   Google Scholar
• G. John, R. Kohavi, and K. Pfleger, Irrelevant features and the subset selection problem, in Machine Learning: Proceeding of Eleventh International Conference William W. Cohen and Haym Hirsh, Morgan Kaufman, San Francisco, CA, USA, 1994, pp. 121–129.
   Google Scholar
• B. Pierre, S. Brunak, Y. Chauvin, C.A.F. Andersen, and N. Henrick, Assessing the accuracy of prediction algorithms for classification: An overview, Bioinformatics 16 (2000), pp. 412–424.
   PubMed Web of Science ®Google Scholar
• J.W. Shavlik (ed.), Analysis and Visualization of Classifier Performance: Comparison Under Imprecise Class and Cost Distribution, AAAI Press, Menlo Park, CA, August 14–17, 1997.
   Google Scholar
• J.W. Shavlik (ed.), The Case Against Accuracy Estimation for Comparing Induction Algorithms, Morgan Kaufmann, San Francisco, CA, USA, 1998.
   Google Scholar
• T. Fawcett, ROC graphs: Notes and practical considerations for researchers, Machine Learn 31 (2004), pp. 1–38.
   Google Scholar
• C.X. Ling, J. Huang, and H. Zhang, AUC: A statistically consistent and more discriminating measure than accuracy, Proceedings of 18th International Conference on Artificial Intelligence (IJCAI-2003), Acapulco, Mexico, August 09–15, (2003), pp. 519–524.
   Google Scholar
• A.P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn. 30 (1997), pp. 1145–1159.
   Web of Science ®Google Scholar
• J.A. Hanley and B.J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143 (1982), pp. 29–36.
   PubMed Web of Science ®Google Scholar
• M.T.D. Cronin, A.O. Aptula, J.C. Duffy, T.I. Netzeva, P.H. Rowe, I.V. Valkova, and T.W. Schultz, Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 49 (2002), pp. 1201–1221.
   PubMed Web of Science ®Google Scholar
• N. Nikolova-Jeliazkova, AMBIT: Building blocks for a future (Q)SAR decision support system (2006). Available at https://ambit.acad.bg.
   Google Scholar
• J. Jaworska, N. Nikolova-Jeliazkova, and T. Aldenberg, QSAR applicability domain estimation by projection of the training set in descriptor space: A review, ATLA 33 (2005), pp. 445–459.
   Google Scholar
• T.W. Schultz and T.I. Netzeva, Development and evaluation of QSARs for ecotoxic endpoints: the benzene response-surface model for Tetrahymena toxicity., in Modelling Enviromental Fate and Toxicity, M.T. Cronin and D. Livingstone, eds., CRC Press, Boca Raton, FL, 2004, pp. 265–284.
   Google Scholar
• L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees, Wadsworth, Monterey, CA, 1984.
   Google Scholar
• G. Gates, The reduced nearest neighbor rule, IEEE Trans. Inform. Theory 18 (1972), pp. 431–433.
   Web of Science ®Google Scholar
• P. Hart, The condensed nearest neighbor rule, IEEE Trans. Inform. Theory 14 (1968), pp. 515–516.
   Web of Science ®Google Scholar
• B.V. Dasarathy, Nosing around the neighborhood: A new system structure and classification rule for recognition in partially exposed environments, Pattern Anal. Machine Intell. 2 (1980), pp. 67–71.
   PubMed Web of Science ®Google Scholar
• A. Golbraikh, and A. Tropsha, Beware of q2!, J. Mol. Graphics Model. 20 (2002), pp. 269–76.
   PubMed Web of Science ®Google Scholar
• M.T. Cronin, A.O. Aptula, J.C. Duffy, T.I. Netzeva, P.H. Rowe, I.V. Valkova, and T.W. Schultz, Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 49 (2002), pp. 1201–1221.
   PubMed Web of Science ®Google Scholar