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A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing

  • Conference paper
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4973))

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Abstract

Wrapper methods look for the selection of a subset of features or variables in a data set, in such a way that these features are the most relevant for predicting a target value. In chemoinformatics context, the determination of the most significant set of descriptors is of great importance due to their contribution for improving ADMET prediction models. In this paper, a comprehensive analysis of descriptor selection aimed to physicochemical property prediction is presented. In addition, we propose an evolutionary approach where different fitness functions are compared. The comparison consists in establishing which method selects the subset of descriptors that best predicts a given property, as well as maintaining the cardinality of the subset to a minimum. The performance of the proposal was assessed for predicting hydrophobicity, using an ensemble of neural networks for the prediction task. The results showed that the evolutionary approach using a non linear fitness function constitutes a novel and a promising technique for this bioinformatic application.

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References

  1. Selick, H.E., Beresford, A.P., Tarbit, M.H.: The Emerging Importance of Predictive ADME Simulation in Drug Discovery. Drug Discov 7(2), 109–116 (2002)

    Article  Google Scholar 

  2. Taskinen, J., Yliruusi, J.: Prediction of Physicochemical Properties Based on Neural Network Modeling. Adv. Drug Deliver. Rev. 55(9), 1163–1183 (2003)

    Article  Google Scholar 

  3. Jónsdottir, S.Ó., Jørgensen, F.S., Brunak, S.: Prediction Methods and Databases Within Chemoinformatics: Emphasis on Drugs and Drug Candidates. Bioinformatics 21, 2145–2160 (2005)

    Article  Google Scholar 

  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 11, 700–707 (2006)

    Article  Google Scholar 

  5. Huuskonnen, J.J., Livingstone, D.J., Tetko, I.V.: Neural Network Modeling for Estimation of Partition Coefficient Based on Atom-Type Electrotopological State Indices. J. Chem. Inf. Comput. Sci. 40, 947–995 (2000)

    Article  Google Scholar 

  6. Agatonovic-Kustrin, S., Beresford, R.J.: Basic Concepts of Artificial Neural Network (ANN) Modeling and its Application in Pharmaceutical Research. J. Pharmaceut. Biomed. 22(5), 717–727 (2000)

    Article  Google Scholar 

  7. Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural Networks Studies. 1. Comparison of Over-fitting and Overtraining. J. Chem. Inf. Comput. Sci. 35, 826–833 (1995)

    Google Scholar 

  8. Topliss, J.G., Edwards, R.P.: Chance Factors in Studies of Quantitative Structure-Activity Relationships. J. Med. Chem. 22(10), 1238–1244 (1979)

    Article  Google Scholar 

  9. Li, L., Weinberg, C.R., Darden, T.A., Pedersen, L.G.: Gene selection for sample classifica-tion based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12), 1131–1142 (2002)

    Article  Google Scholar 

  10. Tan, T., Fu, X., Zhang, Y., Bourgeois, A.G.: A genetic algorithm-based method for feature subset selection. Soft Comput 12(2), 111–120 (2008)

    Article  Google Scholar 

  11. Zhu, Z., Ong, Y., Dash, M.: Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognition 40(11), 3236–3248 (2007)

    Article  MATH  Google Scholar 

  12. Forman, G.: An extensive empirical study of feature selection metrics for text classification. JMLR 3, 1289–1306 (2003)

    Article  MATH  Google Scholar 

  13. Lin, K., Kang, K., Huang, Y., Zhou, C., Wang, B.: Naive bayes text categorization using improved feature selection. Journal of Computational Information Systems 3(3), 1159–1164 (2007)

    Google Scholar 

  14. Montañés, E., Quevedo, J.R., Combarro, E.F., Díaz, I., Ranilla, J.: A hybrid feature selec-tion method for text categorization. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 15(2), 133–151 (2007)

    Article  Google Scholar 

  15. Kohavi, R., John, G.: Wrappers for feature selection. Artificial Intelligence 97, 273–324 (1997)

    Article  MATH  Google Scholar 

  16. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  17. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. JMLR 3, 1157–1182 (2003)

    Article  MATH  Google Scholar 

  18. Dutta, D., Guha, R., Wild, D., Chen, T.: Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models. J. Chem. Inf. Model. 47, 989–997 (2007)

    Article  Google Scholar 

  19. Liu, S., Liu, H., Yin, C., Wang, L.: VSMP: A novel variable selection and modeling method based on the prediction. J. Chem. Inf. Comp. Sci. 43(3), 964–969 (2003)

    Article  Google Scholar 

  20. Wegner, J.K., Zell, A.: Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J. Chem. Inf. Comp. Sci. 43(3), 1077–1084 (2003)

    Article  Google Scholar 

  21. Kah, M., Brown, C.D.: Prediction of the adsorption of lonizable pesticides in soils. J. Agr. Food Chem. 55(6), 2312–2322 (2007)

    Article  Google Scholar 

  22. Bayram, E., Santago, P., Harrisb, R., Xiaob, Y., Clausetc, A.J., Schmittb, J.D.: Genetic algorithms and self-organizing maps: A powerful combination for modeling complex QSAR and QSPR problems. J. of Comput.-Aided Mol. Des. 18, 483–493 (2004)

    Article  Google Scholar 

  23. So, S.-S., Karplus, M.: Evolutionary Optimization in Quantitative Structure-Activity Rela-tionship: An Application of Genetic Neural Networks. J. Med. Chem. 39, 1521–1530 (1996)

    Article  Google Scholar 

  24. Fernández, M., Tundidor-Camba, A., Caballero, J.: Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d] pyrimidine derivatives using artificial neural network en-sembles. J. Chem Inf. and Model. 45(6), 1884–1895 (2005)

    Article  Google Scholar 

  25. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufmann, San Mateo, CA (1991)

    Google Scholar 

  26. Breiman, L.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1993)

    Google Scholar 

  27. Trevino, V., Falciani, F.: GALGO: An R package for multivariate variable selection using genetic algorithms. Bioinformatics 22(9), 1154–1156 (2006)

    Article  Google Scholar 

  28. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-Linear Least Squares Problems. Technical University of Denmark, 2nd edn. (April, 2004)

    Google Scholar 

  29. Yaffe, D., Cohen, Y., Espinosa, G., Arenas, A., Giralt, F.: Fuzzy ARTMAP and back-propagation neural networks based quantitative structure - property relationships (QSPRs) for octanol: Water partition coefficient of organic compounds. J. Chem. Inf. Comp. Sci. 42(2), 162–183 (2002)

    Article  Google Scholar 

  30. Linpinski, C.A., Lombardo, F., Dominy, B.W., Freeny, P.: Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997)

    Article  Google Scholar 

  31. Duprat, A., Huynh, T., Dreyfus, G.: Towards a principled methodology for neural network design and performance evaluation in qsar; application to the prediction of logp. J. Chem. Inf. Comp. Sci. 38, 586–594 (1998)

    Article  Google Scholar 

  32. Wang, R., Fu, Y., Lai, L.: A new atom-additive method for calculating partition coefficients. J. Chem. Inf. Comp. Sci. 37(3), 615–621 (1997)

    Article  Google Scholar 

  33. 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. 19, 453–463 (2005)

    Article  Google Scholar 

  34. Winkler, D.A.: Neural networks in ADME and toxicity prediction. Drug. Future 29(10), 1043–1057 (2004)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la Computación (DCIC), Universidad Nacional del Sur, Av. Alem 1253, 8000, Bahía, Blanca, Argentina

    Axel J. Soto, Rocío L. Cecchini, Gustavo E. Vazquez & Ignacio Ponzoni

  2. Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur – CONICET, Complejo CRIBABB – Camino La Carrindanga km.7, CC 717, Bahía, Blanca, Argentina

    Axel J. Soto & Ignacio Ponzoni

Authors
  1. Axel J. Soto
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  2. Rocío L. Cecchini
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  3. Gustavo E. Vazquez
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  4. Ignacio Ponzoni
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Elena Marchiori Jason H. Moore

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© 2008 Springer-Verlag Berlin Heidelberg

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Soto, A.J., Cecchini, R.L., Vazquez, G.E., Ponzoni, I. (2008). A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_17

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  • DOI: https://doi.org/10.1007/978-3-540-78757-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78756-3

  • Online ISBN: 978-3-540-78757-0

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