Abstract
This paper describes the use of a support vector machine algorithm for the classification of molecules database in order for the prediction of the activity of drugs. Molecules database are fragmented, and each molecule is represented by a set of contained fragments. Molecular weighted descriptors are tested for the representation of molecular fragments in order to represent the dataset as a MxF array where each element takes the value of the molecular weighted descriptor calculated for the fragment. As weighted descriptors take into account distances and heteroatoms present in the fragments, the representation space allows the discrimination of similar structural fragments. A Support Vector Machine algorithm is used for the classification process for a training set. Prediction of the activity of the test set is carried out in function of results of training stage and the application of a proposed heuristic. Results obtained shows that the use of weighted molecular descriptors improves the prediction of drug activity for heterogeneous datasets.
Keywords
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References
Ghuloum, A.M., Sage, C.R., Jain, A.N.: Molecular Hashkeys: A Novel Method for Molecular Characterization and Its Application for Predicting Important Pharmaceutical Properties of Molecules. Journal of Medicinal Chemistry 42(10), 1739–1748 (1999)
Cross, S., Baroni, M., Carosati, E., Benedetti, P., Clementi, S.: FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set. J. Chem. Inf. Model. 50(8), 1442–1450 (2010)
Culp, M., Johnson, K., Michailidis, G.: The Ensemble Bridge Algorithm: A New Modeling Tool for Drug Discovery Problems. J. Chem. Inf. Model. 50(2), 309–316 (2010)
Luque Ruiz, I., Cerruela García, G., Gómez-Nieto, M.A.: Representation of the Molecular Topology of Cyclical Structures by Means of Cycle Graphs. 3. Hierarchical Model of Screening of Chemical Databases. J. Chem. Inf. Comput. Sci. 44, 1903–1911 (2004)
Sun, H.: An Accurate and Interpretable Bayesian Classification Model for Prediction of hERG Liability. Chem. Med. Chem. 1(3), 315–322 (2006)
Zhou, J., Augelli-Szafran, C.E., Bradley, J.A., Chen, X., Koci, B.J.: Novel Potent hERG Potassium Channel Enhancers And Their In Vitro Antiarrhythmic Activity. Molecular Pharmacology 68(3), 876–884 (2005)
Chen, W.-h., Wang, W.-y., Zhang, J., Yang, D., Wang, Y.-p.: State-dependent blockade of human ether-a-go-go-related gene (hERG) K + channels by changrolin in stably transfected HEK293 cells. Acta Pharmacologica Sinica 31, 915–922 (2010)
Bayada, D.M., Hamersma, H., van Geerestein, V.J.: Molecular diversity and representativity in chemical databases. J. Chem. Inf. Comput. Sci. 39, 1–10 (1999)
Furlanello, C., Serafini, M., Merler, S., Jurman, G.: An accelerated procedure for recursive feature ranking on microarray data. Neural Networks 16, 641–648 (2003)
García-Pedrajas, N., Ortiz-Boyer, D.: A cooperative constructive method for neural networks for pattern recognition. Pattern Recognition 40(1), 80–99 (2007)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 127–167 (1998)
Todeschini, R., Consonni, V.: Handbook of Molecular Descriptors. Wiley-VCH (2000)
JChem 5.4.0.0, ChemAxon (2010), http://www.chemaxon.com
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Cerruela García, G., Luque Ruiz, I., Ángel Gómez-Nieto, M. (2011). Prediction of Drug Activity Using Molecular Fragments-Based Representation and RFE Support Vector Machine Algorithm. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21827-9_41
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DOI: https://doi.org/10.1007/978-3-642-21827-9_41
Publisher Name: Springer, Berlin, Heidelberg
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