Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers

References

• S.M. Paul, D.S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, and A.L. Schacht, How to improve R&D productivity: The pharmaceutical industry's grand challenge, Nat. Rev. Drug Discov. 9 (2010), pp. 203–214.
   PubMed Web of Science ®Google Scholar
• Y. Fukunishi, Structure-based drug screening and ligand-based drug screening with machine learning, Comb. Chem. High Throughput Screen. 12 (2009), pp. 397–408.
   PubMed Web of Science ®Google Scholar
• C. Hansch and T. Fujita, p-σ-π Analysis. A method for the correlation of biological activity and chemical structure, J. Am. Chem. Soc. 86 (1964), pp. 1616–1626.
   Web of Science ®Google Scholar
• E. Byvatov, K.H. Baringhaus, G. Schneider, and H. Matter, A virtual screening filter for identification of cytochrome P450 2C9 (CYP2C9) inhibitors, QSAR Comb. Sci. 26 (2007), pp. 618–628.
   Google Scholar
• S. Sciabola, I. Morao, and M.J. de Groot, Pharmacophoric fingerprint method (TOPP) for 3D-QSAR modeling: Application to CYP2D6 metabolic stability, J. Chem. Inf. Model. 47 (2007), pp. 76–84.
   PubMed Web of Science ®Google Scholar
• J.R. Votano, M. Parham, L.M. Hall, L.H. Hall, L.B. Kier, S. Oloff, and A. Tropsha, QSAR modeling of human serum protein binding with several modeling techniques utilizing structure–information representation, J. Med. Chem. 49 (2006), pp. 7169–7181.
   PubMed Web of Science ®Google Scholar
• L.H. Hall and L.B. Kier, Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 1039–1045.
   Google Scholar
• R. Carrasco-Velar, J. Prieto-Entenza, A. Antelo-Collado, J. Padrón-García, G. Cerruela-García, Á. Maceo-Pixa, R. Alcolea-Núñez, and L. Silva-Rojas, Hybrid reduced graph for SAR studies, SAR QSAR Environ. Res. 24 (2013), pp. 201–214.
   PubMed Web of Science ®Google Scholar
• G.C. García, B. Palacios-Bejarano, I.L. Ruiz, and M. Gómez-Nieto, Comparison of representational spaces based on structural information in the development of QSAR models for benzylamino enaminone derivatives, SAR QSAR Environ. Res. 23 (2012), pp. 751–774.
   PubMed Web of Science ®Google Scholar
• R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Vol. 11, John Wiley & Sons, Weinheim, Germany, 2008.
   Google Scholar
• P. Yang, Y. Hwa Yang, B.B Zhou, and A.Y Zomaya, A review of ensemble methods in bioinformatics, Curr. Bioinform. 5 (2010), pp. 296–308.
   Web of Science ®Google Scholar
• T. Hancock, R. Put, D. Coomans, Y. Vander, Heyden, and Y. Everingham, A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies, Chemom. Intell. Lab. Syst. 76 (2005), pp. 185–196.
   Web of Science ®Google Scholar
• C. Merkwirth, H. Mauser, T. Schulz-Gasch, O. Roche, M. Stahl, and T. Lengauer, Ensemble methods for classification in cheminformatics, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1971–1978.
   PubMedGoogle Scholar
• D. Plouffe, A. Brinker, C. McNamara, K. Henson, N. Kato, K. Kuhen, A. Nagle, F. Adrián, J.T. Matzen, and P. Anderson, In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen, Proceed. Nat. Acad. Sci. 105 (2008), pp. 9059–9064.
   PubMed Web of Science ®Google Scholar
• F. Hammann, C. Suenderhauf, and J.R. Huwyler, A binary ant colony optimization classifier for molecular activities, J. Chem Inf. Model. 51 (2011), pp. 2690–2696.
   PubMed Web of Science ®Google Scholar
• F. Fontaine, M. Pastor, I. Zamora, and F. Sanz, Anchor−GRIND: Filling the gap between standard 3D QSAR and the GRid-INdependent Descriptors, J. Med. Chem. 48 (2005), pp. 2687–2694.
   PubMed Web of Science ®Google Scholar
• B. Gaüzère, L. Brun, and D. Villemin, Two new graphs kernels in chemoinformatics, Pattern Recognit. Lett. 33 (2012), pp. 2038–2047.
   Web of Science ®Google Scholar
• C. Helma, T. Cramer, S. Kramer, and L. De Raedt, Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1402–1411.
   PubMedGoogle Scholar
• The Carcinogenic Potency Database (CPDB). Available at https://toxnet.nlm.nih.gov/cpdb/cpdb.html. (accessed on 4 june 2017).
   Google Scholar
• B.N. Ames, W.E. Durston, E. Yamasaki, and F.D. Lee, Carcinogens are mutagens: A simple test system combining liver homogenates for activation and bacteria for detection, Proceed. Nat. Acad. Sci. 70 (1973), pp. 2281–2285.
   PubMed Web of Science ®Google Scholar
• G. Hakimelahi and G. Khodarahmi, The identification of toxicophores for the prediction of mutagenicity, hepatotoxicity and cardiotoxicity, J. Iran Chem. Soc. 2 (2005), pp. 244–267.
   Web of Science ®Google Scholar
• J. Blagg, Structure–activity relationships for in vitro and in vivo toxicity, Annu. Rep. Med. Chem. 41 (2006), pp. 353–368.
   Web of Science ®Google Scholar
• B.P. Cho, F.A. Beland, and M.M. Marques, NMR structural studies of a 15-mer DNA sequence from a ras protooncogene, modified at the first base of codon 61 with the carcinogen 4-aminobiphenyl, Biochemistry 31 (1992), pp. 9587–9602.
   PubMed Web of Science ®Google Scholar
• C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474.
   PubMed Web of Science ®Google Scholar
• R.E. Carhart, D.H. Smith, and R. Venkataraghavan, Atom pairs as molecular features in structure–activity studies: Definition and applications, J. Chem. Inf. Comput. Sci. 25 (1985), pp. 64–73.
   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
• E-State fragments in E-State Fingerprint. Available at http://www.scbdd.com/padel_desc/fps-estate/. (accessed on 3 November 2017).
   Google Scholar
• J.L. Durant, B.A. Leland, D.R. Henry, and J.G. Nourse, Reoptimization of MDL keys for use in drug discovery, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 1273–1280.
   PubMedGoogle Scholar
• SMARTS Patterns for Functional Group in Substructure Fingerprint. Available at http://www.scbdd.com/cdk_desc/fps-substructure/. (accessed on 4 july 2017).
   Google Scholar
• G.M. Downs, V.J. Gillet, J.D. Holliday, and M.F. Lynch, Review of ring perception algorithms for chemical graphs, J. Chem. Inf. Comput. Sci. 29 (1989), pp. 172–187.
   Google Scholar
• I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, USA, 2005.
   Google Scholar
• S. Garcia, J. Derrac, J. Cano, and F. Herrera, Prototype selection for nearest neighbor classification: Taxonomy and empirical study, IEEE Trans. Pattern Anal. Mach. Intell. 34 (2012), pp. 417–435.
   PubMed Web of Science ®Google Scholar
• A. Ben-David, A lot of randomness is hiding in accuracy, Eng. Appl. Artif. Intell. 20 (2007), pp. 875–885.
   Web of Science ®Google Scholar
• C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011), pp. 1–27.
   Web of Science ®Google Scholar
• L.I. Kuncheva, A theoretical study on six classifier fusion strategies, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), pp. 281–286.
   Web of Science ®Google Scholar
• T.G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Mach. Learn. 40 (2000), pp. 139–157.
   Web of Science ®Google Scholar
• J. Maudes, J.J. Rodríguez, C. García-Osorio, and N. García-Pedrajas, Random feature weights for decision tree ensemble construction, Inf. Fusion 13 (2012), pp. 20–30.
   Web of Science ®Google Scholar
• N. García-Pedrajas, C. García-Osorio, and C. Fyfe, Nonlinear boosting projections for ensemble construction, J. Mach. Learn. Res. 8 (2007), pp. 1–33.
   Web of Science ®Google Scholar
• S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, Upper Saddle River, New York, USA, 1998.
   Google Scholar
• N. García-Pedrajas and C. García-Osorio, Constructing ensembles of classifiers using supervised projection methods based on misclassified instances, Expert Syst. Appl. 38 (2011), pp. 343–359.
   Web of Science ®Google Scholar
• N. García-Pedrajas, J. Maudes-Raedo, C. García-Osorio, and J.J. Rodríguez-Díez, Supervised subspace projections for constructing ensembles of classifiers, Inf. Sci. 193 (2012), pp. 1–21.
   Web of Science ®Google Scholar
• K. Fukunaga and J. Mantock, Nonparametric discriminant analysis, IEEE Trans. Pattern Anal. Mach. Intell. (1983), pp. 671–678.
   PubMed Web of Science ®Google Scholar
• B.-C. Kuo and D.A. Landgrebe, Nonparametric weighted feature extraction for classification, IEEE Trans. Geosci. Remote Sensing 42 (2004), pp. 1096–1105.
   Web of Science ®Google Scholar
• Q. Tian, J. Yu, and T.S. Huang, Boosting multiple classifiers constructed by hybrid discriminant analysis, in Multiple Classifier Systems: Proceedings of the 6th International Workshop, MCS 2005, Seaside, CA, 13–15 June, N.C. Oza, R. Polikar, J. Kittler, and F. Roli, eds., Springer Berlin Heidelberg, Berlin, Heidelberg, 2005, pp. 42–52.
   Google Scholar
• W.J. Youden, Index for rating diagnostic tests, Cancer 3 (1950), pp. 32–35.
   PubMed Web of Science ®Google Scholar
• J. Demšar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 (2006), pp. 1–30.
   Web of Science ®Google Scholar
• S.L. Salzberg, On comparing classifiers: Pitfalls to avoid and a recommended approach, Data Min. Knowl. Discov. 1 (1997), pp. 317–328.
   Web of Science ®Google Scholar
• F. Wilcoxon, Individual comparisons by ranking methods, Biometrics 1 (1945), pp. 80–83.
   Web of Science ®Google Scholar
• D.J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, CRC Press, New York, USA, 2003.
   Google Scholar
• P.B. Nemenyi, Distribution-Free Multiple Comparisons, PhD thesis, Princeton University, USA, 1963.
   Google Scholar