Machine learning methods for property prediction in chemoinformatics: Quo Vadis?

Journal of Chemical Information and Modeling

Volumn 52, Issue 6, 2012, Pages 1413-1437

  Varnek, Alexandre ^a   Baskin, Igor ^a,b  

^a UNIVERSITÉ DE STRASBOURG   (France)

^b MOSCOW STATE UNIVERSITY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

PREDICTIVE ANALYTICS;

DOSE-RESPONSE CURVES; MACHINE LEARNING METHODS; MODELING OF PROPERTIES; MOLECULAR SPECIES; PREDICTIVE PERFORMANCE; PROPERTY PREDICTIONS; STATISTICAL INFERENCE; STRUCTURE PROPERTY;

MACHINE LEARNING;

EID: 84862848391     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci200409x     Document Type: Review

References (344)

1. Bellman, R. E. Dynamic Programming; Princeton University Press: Princeton, NJ, 1957.
2. Cherkassky, V. and Mulier, F. Learning from Data: Concept, Theory and Methods, 2 nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2007.
3. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities Proc. Natl. Acad. Sci. U.S.A. 1982, 79 (8) 2554-2558
4. Rumelhart, D. E.; McClelland, J. L. Parallel Distributed Processing; MIT Press: Cambridge, MA, 1986; Vol. 1,2.
5. Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: Berlin, 1995.
6. Vapnik, V. Statistical Learning Theory; Wiley-Interscience: New York, 1998.
7. Valiant, L. G. A Theory of the Learnable Commun. ACM 1984, 27 (11) 1134-1142
8. Rissanen, J. A universal prior for the integers and estimation by minimum description length Ann. Stat. 1983, 11 (2) 416-431
9. Gasteiger, J. Chemoinformatics: A new field with a long tradition Anal. Bioanal. Chem. 2006, 384 (1) 57-64
10. Gasteiger, J.; Engel, T. Chemoinformatics: A Textbook; Wiley-VCH: Weinheim, 2003.
11. Gasteiger, J. Handbook of Chemoinformatics: From Data to Knowledge; Wiley-VCH: Weinheim, 2003.
12. Engel, T. Basic overview of chemoinformatics J. Chem. Inf. Model. 2006, 46 (6) 2267-2277
13. Varnek, A.; Baskin, I. I. Chemoinformatics as a theoretical chemistry discipline Mol. Inf. 2011, 30 (1) 20-32
14. Brown, N. Chemoinformatics: An introduction for computer scientists ACM Comput. Surv. 2009, 41 (2) 1-38
15. Maggiora, G. M. On outliers and activity cliffs why QSAR often disappoints J. Chem. Inf. Model. 2006, 46 (4) 1535-1535
16. Dietterich, T. G.; Lathrop, R. H.; Lozano-Pérez, T. Solving the multiple instance problem with axis-parallel rectangles Artif. Intell. 1997, 89 (1-2) 31-71
17. Baskin, I. I.; Gordeeva, E. V.; Devdariani, R. O.; Zefirov, N. S.; Palyulin, V. A.; Stankevich, M. I. Solving the inverse problem of structure-property relations for the case of topological indexes Dokl. Akad. Nauk SSSR 1989, 307 (3) 613-17
18. Gordeeva, E. V.; Molchanova, M. S.; Zefirov, N. S. General methodology and computer program for the exhaustive restoring of chemical structures by molecular connectivity indexes. Solution of the inverse problem in QSAR/QSPR Tetrahedron Comput. Methodol. 1990, 3 (6) 389-415
19. Skvortsova, M. I.; Baskin, I. I.; Slovokhotova, O. L.; Palyulin, V. A.; Zefirov, N. S. Inverse problem in QSAR/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices) J. Chem. Inf. Comput. Sci. 1993, 33 (4) 630-634
20. Kier, L. B.; Hall, L. H.; Frazer, J. W. Design of molecules from quantitative structure-activity relationship models. 1. Information transfer between path and vertex degree counts J. Chem. Inf. Comput. Sci. 1993, 33 (1) 143-147
21. Skvortsova, M. I.; Baskin, I. I.; Palyulin, V. A.; Slovokhotova, O. L.; Zefirov, N. S. Structural design. Inverse Problems for Topological Indices in QSAR/QSPR Studies. In AIP Conf. Proc. 330. E.C.C.C.1 Comput. Chem. F.E.C.S. Conf., Nancy, France, Bernardi, F.; Rivail, J.-L., Eds. AIP Press: Woodbury, NY, 1995; pp 486-499.
22. Rücker, C.; Meringer, M.; Kerber, A. QSPR using MOLGEN-QSPR: The example of haloalkane boiling points J. Chem. Inf. Comput. Sci. 2004, 44 (6) 2070-2076
23. Churchwell, C. J.; Rintoul, M. D.; Martin, S.; Visco, D. P., Jr.; Kotu, A.; Larson, R. S.; Sillerud, L. O.; Brown, D. C.; Faulon, J. L. The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides J. Mol. Graphics Modell. 2004, 22 (4) 263-73
24. Wong, W.; Burkowski, F. A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem J. Cheminf. 2009, 1 (1) 4
25. Miyao, T.; Arakawa, M.; Funatsu, K. Exhaustive structure generation for inverse-QSPR/QSAR Mol. Inf. 2010, 29 (1-2) 111-125
26. White, D.; Wilson, R. C. Generative models for chemical structures J. Chem. Inf. Model. 2010, 50 (7) 1257-1274
27. Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A neural device for searching direct correlations between structures and properties of chemical compounds J. Chem. Inf. Comput. Sci. 1997, 37 (4) 715-721
28. Kireev, D. B. ChemNet: A novel neural network based method for graph/property mapping J. Chem. Inf. Comput. Sci. 1995, 35 (2) 175-80
29. Ivanciuc, O. Molecular structure encoding into artificial neural networks topology Rom. Chem. Q. Rev. 2001, 8, 197-220
30. Bianucci, A. M.; Micheli, A.; Sperduti, A.; Starita, A. Application of cascade correlation networks for structures to chemistry Appl. Intell. 2000, 12 (1-2) 117-146
31. Micheli, A.; Sperduti, A.; Starita, A.; Bianucci, A. M. Analysis of the internal representations developed by neural networks for structures applied to quantitative structure-activity relationship studies of benzodiazepines J. Chem. Inf. Comput. Sci. 2001, 41 (1) 202-218
32. Goulon, A.; Picot, T.; Duprat, A.; Dreyfus, G. Predicting activities without computing descriptors: Graph machines for QSAR SAR QSAR Environ. Res. 2007, 18 (1-2) 141-153
33. Ralaivola, L.; Swamidass, S. J.; Saigo, H.; Baldi, P. Graph kernels for chemical informatics Neural Netw. 2005, 18 (8) 1093-1110
34. Mahé, P.; Ueda, N.; Akutsu, T.; Perret, J.-L.; Vert, J.-P. Graph kernels for molecular structure-activity relationship analysis with support vector machines J. Chem. Inf. Model. 2005, 45 (4) 939-951
35. Mahe, P.; Ralaivola, L.; Stoven, V.; Vert, J.-P. The pharmacophore kernel for virtual screening with support vector machines J. Chem. Inf. Model. 2006, 46 (5) 2003-2014
36. Kashima, H.; Tsuda, K.; Inokuchi, A. Marginalized Kernels Between Labeled Graphs. In Proceedings, Twentieth International Conference on Machine Learning, AAAI Press: Washington, DC, 2003; Vol. 1, pp 321-328.
37. Swamidass, S. J.; Chen, J.; Phung, P.; Ralaivola, L.; Baldi, P. Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity Bioinformatics 2005, 21, I359-I368
38. Fröhlich, H.; Wegner, J.; Sieker, F.; Zell, Z. Kernel functions for attributed molecular graphs - A new similarity based approach to ADME prediction inclassification and regression QSAR Comb. Sci. 2006, 25 (4) 317-326
39. Fröhlich, H. Optimal Assignment Kernels for ADME in Silico Prediction. In Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques; Lodhi, H.; Yamanishi, Y., Eds.; IGI Global: Hershey, PA, 2011; pp 16-34.
40. Rupp, M.; Schneider, G. Graph kernels for molecular similarity Mol. Inf. 2010, 29 (4) 266-273
41. Rupp, M.; Körner, R.; Tetko, I. V. Predicting the pKa of small molecules Comb. Chem. High T. Scr. 2011, 14 (5) 307-327
42. Vishwanathan, S. V. N.; Schraudolph, N. N.; Kondor, R.; Borgwardt, K. M. Graph kernels J. Mach. Learn. Res. 2010, 11, 1201-1242
43. Varmuza, K., Multivariate Data Analysis in Chemistry. In Handbook of Chemoinformatics. From Data to Knowledge; Gasteiger, J., Ed.; Wiley-VCH: Weinheim, 2003; pp 1098-1133.
44. Zupan, J.; Gasteiger, J. Neural Networks in Chemistry; Wiley-VCH: Weinheim, 1999.
45. Ivanciuc, O. Applications of Support Vector Machines in Chemistry. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Cundari, T. R., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, 2007; Vol. 23, pp 291-400.
46. Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Neural networks in building QSAR models Methods Mol. Biol. 2008, 458, 137-158
47. Halberstam, N. M.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Neural networks as a method for elucidating structure-property relationships for organic compounds Russ. Chem. Rev. 2003, 72 (7) 629-649
48. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: Molecular representations, data mining methods, new application areas, and performance evaluation J. Chem. Inf. Mod 2010, 50 (2) 205-216
49. Eckert, H.; Bajorath, J. Molecular similarity analysis in virtual screening: Foundations, limitations and novel approaches Drug Discovery Today 2007, 12 (5-6) 225-33
50. Bishop, C. M. Pattern Recognition and Machine Learning; Springer: New York, 2006.
51. Upton, G.; Cook, I. Oxford Dictionary of Statistics, 2 nd ed. (revised); Oxford University Press: Oxford, 2008.
52. Chen, N.; Lu, W.; Yang, J.; Li, G. Support Vector Machine in Chemistry; World Scientific: Singapore, 2004.
53. Farkas, O.; Heberger, K. Comparison of ridge regression, partial least-squares, pairwise correlation, forward- and best subset selection methods for prediction of retention indices for aliphatic alcohols J. Chem. Inf. Model. 2005, 45 (2) 339-346
54. Hawkins, D. M.; Basak, S. C.; Shi, X. QSAR with few compounds and many features J. Chem. Inf. Comput. Sci. 2001, 41 (3) 663-670
55. Merkwirth, C.; Mauser, H.; Schulz-Gasch, T.; Roche, O.; Stahl, M.; Lengauer, T. Ensemble methods for classification in cheminformatics J. Chem. Inf. Comput. Sci. 2004, 44 (6) 1971-1978
56. Spycher, S.; Pellegrini, E.; Gasteiger, J. Use of structure descriptors to discriminate between modes of toxic action of phenols J. Chem. Inf. Model. 2005, 45 (1) 200-8
57. Jaynes, E. T. Probability Theory. The Logic of Science; Cambridge University Press: Cambridge, 2003.
58. Jaynes, E. T. Prior probabilities IEEE Trans. Syst. Sci. Cyb. 1968, 4 (3) 227-241
59. Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes in Machine Learning; The MIT Press: Cambridge, MA, 2006.
60. Bishop, C. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, 1995.
61. Burden, F. R.; Winkler, D. A. Robust QSAR models using Bayesian regularized neural networks J. Med. Chem. 1999, 42 (16) 3183-3187
62. Bruneau, P. Search for predictive generic model of aqueous solubility using Bayesian neural nets J. Chem. Inf. Comput. Sci. 2001, 41 (6) 1605-1616
63. Burden, F. R.; Ford, M. G.; Whitley, D. C.; Winkler, D. A. Use of automatic relevance determination in QSAR studies using Bayesian neural networks J. Chem. Inf. Comput. Sci. 2000, 40 (6) 1423-1430
64. Klocker, J.; Wailzer, B.; Buchbauer, G.; Wolschann, P. Bayesian neural networks for aroma classification J. Chem. Inf. Comput. Sci. 2002, 42 (6) 1443-1449
65. Burden, F. R. Quantitative structure-activity relationship studies using Gaussian processes J. Chem. Inf. Comput. Sci. 2001, 41 (3) 830-835
66. Tino, P.; Nabney, I. T.; Williams, B. S.; Losel, J.; Sun, Y. Nonlinear prediction of quantitative structure-activity relationships J. Chem. Inf. Comput. Sci. 2004, 44 (5) 1647-1653
67. Obrezanova, O.; Csanyi, G.; Gola, J. M. R.; Segall, M. D. Gaussian processes: A method for automatic QSAR modeling of ADME properties J. Chem. Inf. Model. 2007, 47 (5) 1847-1857
68. Efron, B. Bootstrap methods: Another look at the jackknife Ann. Stat. 1979, 7, 1-26
69. Tetko, I. V.; Bruneau, P.; Mewes, H.-W.; Rohrer, D. C.; Poda, G. I. Can we estimate the accuracy of ADMET predictions? Drug Discovery Today 2006, 11 (15/16) 700-707
70. Sushko, I.; Novotarskyi, S.; Korner, R.; Pandey, A. K.; Cherkasov, A.; Li, J.; Gramatica, P.; Hansen, K.; Schroeter, T.; Muller, K. R.; Xi, L.; Liu, H.; Yao, X.; Oberg, T.; Hormozdiari, F.; Dao, P.; Sahinalp, C.; Todeschini, R.; Polishchuk, P.; Artemenko, A.; Kuz'min, V.; Martin, T. M.; Young, D. M.; Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.; Muller, C.; Varnek, A.; Prokopenko, V. V.; Tetko, I. V. Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set J. Chem. Inf. Model. 2010, 50 (12) 2094-2111
71. Platt, J. Probabilities for SV Machines. In Advances in Large Margin Classifiers; Smola, A. J.; Bartlett, P. L.; Schölkopf, B.; Schuurmans, D., Eds.; MIT Press: Cambridge, MA, 2000; pp 61-74.
72. Kwok, J. T. Y.; Tsang, I. W. H. The pre-image problem in kernel methods IEEE Trans. Neural Netw. 2004, 15 (6) 1517-1525
73. Tetko, I. V. Neural network studies. 4. Introduction to associative neural networks J. Chem. Inf. Comput. Sci. 2002, 42 (3) 717-728
74. Tetko, I. V.; Tanchuk, V.; Chentsova, N. P.; Antonenko, S. V.; Poda, G. I.; Kukhar, V. P.; Luik, A. I. HIV-1 reverse transcriptase inhibitor design using artificial neural networks J. Med. Chem. 1994, 37 (16) 2520-6
75. Artemenko, N. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds Russ. Chem. Bull. 2003, 52 (1) 20-29
76. Zhokhova, N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, A. N.; Zefirov, N. S. Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies Dokl. Chem. 2007, 417 (2) 282-284
77. Zhu, H.; Tropsha, A.; Fourches, D.; Varnek, A.; Papa, E.; Gramatical, P.; Öberg, T.; Dao, P.; Cherkasov, A.; Tetko, I. V. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis J. Chem. Inf. Model. 2008, 48 (4) 766-784
78. Varnek, A.; Fourches, D.; Horvath, D.; Klimchuk, O.; Gaudin, C.; Vayer, P.; Solov'ev, V.; Hoonakker, F.; Tetko, I. V.; Marcou, G. ISIDA: Platform for virtual screening based on fragment and pharmacophoric descriptors Curr. Comput.-Aided Drug Des. 2008, 4 (3) 191-198
79. Breiman, L. Bagging predictors Mach. Learn. 1996, 24 (2) 123-140
80. Breiman, L. Random forests Mach. Learn. 2001, 45 (1) 5-32
81. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: A classification and regression tool for compound classification and QSAR modeling J. Chem. Inf. Comput. Sci. 2003, 43 (6) 1947-1958
82. Guha, R.; Jurs, P. C. Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors J. Chem. Inf. Comput. Sci. 2004, 44 (6) 2179-2189
83. Li, S.; Fedorowicz, A.; Singh, H.; Soderholm, S. C. Application of the random forest method in studies of local lymph node assay based skin sensitization data J. Chem. Inf. Model. 2005, 45 (4) 952-964
84. Ting, K. M.; Witten, I. H. Stacking Bagged and Dagged Models. In Fourteenth International Conference on Machine Learning; Fisher, D. H., Ed.; Morgan Kaufmann Publishers: San Francisco, CA, 1997; pp 367-375.
85. Ho, T. K. The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. 1998, 20 (8) 832-844
86. Wolpert, D. H. Stacked generalization Neural Netw. 1992, 5 (2) 241-259
87. Breiman, L. Stacked regressions Mach. Learn. 1996, 24 (1) 49-64
88. Freund, Y.; Schapire, R. E., Experiments with a New Boosting Algorithm. In Thirteenth International Conference on Machine Learning; Morgan Kaufmann: San Francisco, 1996; pp 148-156.
89. Wegner, J. K.; Froehlich, H.; Zell, A. Feature selection for descriptor based classification models. 2. Human intestinal absorption (HIA) J. Chem. Inf. Comput. Sci. 2004, 44 (3) 931-939
90. Friedman, J. H. Greedy function approximation: A gradient boosting machine Ann. Stat. 2001, 29 (5) 1189-1232
91. Friedman, J. H. Stochastic gradient boosting Comput. Stat. Data An. 2002, 38 (4) 367-378
92. Svetnik, V.; Wang, T.; Tong, C.; Liaw, A.; Sheridan, R. P.; Song, Q. Boosting: An ensemble learning tool for compound classification and QSAR modeling J. Chem. Inf. Model. 2005, 45 (3) 786-799
93. Demiriz, A.; Bennett, K. P.; Shawe-Taylor, J. Linear Programming boosting via column generation Mach. Learn. 2002, 46 (1-3) 225-254
94. Saigo, H.; Nowozin, S.; Kadowaki, T.; Kudo, T.; Tsuda, K. GBoost: A mathematical programming approach to graph classification and regression Mach. Learn. 2009, 75 (1) 69-89
95. Saigo, H.; Kadowaki, T.; Tsuda, K., A Linear Programming Approach for Molecular QSAR Analysis. In International Workshop on Mining and Learning with Graphs 2006; Gaertner, T.; Garriga, G. C.; Meinl, T., Eds.; Berlin, 2006; pp 85-96.
96. Chapelle, O.; Schoelkopf, B.; Zien, A. Semi-Supervised Learning; The MIT Press: Cambridge, MA, 2006.
97. Joachims, T. Transductive Inference for Text Classification Using Support Vector Machines. In International conference on Machine Learning (ICML); Kaufmann, M., Ed.; Bled, Slovenia, 1999; pp 200-209.
98. Cohn, D. A.; Ghahramani, Z.; Jordan, M. I. Active learning with statistical models J. Artif. Intell. Res. 1996, 4, 129-145
99. Schein, A. I.; Ungar, L. H. Active learning for logistic regression: An evaluation Mach. Learn. 2007, 68 (3) 235-265
100. Wang, Z.; Chen, S.; Chen, Z. An active learning approach for neural network ensemble Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2005, 42 (3) 375-380
101. Danziger, S. A.; Zeng, J.; Wang, Y.; Brachmann, R. K.; Lathrop, R. H. Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants Bioinformatics 2007, 23 (13) i104-i114
102. Fujiwara, Y.; Yamashita, Y.; Osoda, T.; Asogawa, M.; Fukushima, C.; Asao, M.; Shimadzu, H.; Nakao, K.; Shimizu, R. Virtual screening system for finding structurally diverse hits by active learning J. Chem. Inf. Model. 2008, 48 (4) 930-940
103. Fukumizu, K. Statistical active learning in multilayer perceptrons IEEE Trans. Neural Netw. 2000, 11 (1) 17-26
104. Vijayakumart, S.; Ogawa, H. Improving generalization ability through active learning IEICE Trans. Inf. Syst. 1999, E82-D (2) 480-487
105. Warmuth, M. K.; Liao, J.; Rätsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machines in the drug discovery process J. Chem. Inf. Comput. Sci. 2003, 43 (2) 667-673
106. Zomer, S.; Del Nogal Sánchez, M.; Brereton, R. G.; Pérez Pavón, J. L. Active learning support vector machines for optimal sample selection in classification J. Chemom. 2004, 18 (6) 294-305
107. Cheng, J.; Wang, K. Active learning for image retrieval with Co-SVM Pattern Recogn. 2007, 40 (1) 330-334
108. Gu, P.; Zhu, Q.; Zhang, C. A novel active learning approach for SVM in the presence of multi-views J. Chem. Inf. Comput. Sci. 2010, 7 (2) 317-324
109. Bordes, A.; Ertekin, S.; Weston, J.; Bottou, L. Fast kernel classifiers with online and active learning J. Mach. Learn. Res. 2005, 6, 1579-1619
110. Glasmachers, T.; Igel, C. Second-order SMO improves SVM online and active learning Neural Comput. 2008, 20 (2) 374-382
111. Iyengar, V. S.; Apte, C.; Zhang, T., Active Learning Using Adaptive Resampling. In Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Ramakrishnan, R.; Stolfo, S.; Bayardo, R.; Parsa, I.; Ramakrishnan, R.; Stolfo, S.; Bayardo, R.; Parsa, I., Eds.; ACM: Boston, MA, 2000; pp 91-98.
112. Xu, J.; Shi, P. Active learning based on maximizing information gain for content-based image retrieval J. Southeast Univ. (Engl. Ed.) 2004, 20 (4) 431-435
113. Kim, H. J.; Kim, J. U. Combining active learning and boosting for Naïve Bayes text classifiers Lect. Notes Comput. Sci. 2004, 3129, 519-527
114. Yang, L.; Hanneke, S.; Carbonell, J. Bayesian active learning using arbitrary binary valued queries Lect. Notes Comput. Sci. 2010, 6331, 50-58
115. Henrich, F. F.; Obermayer, K. Active learning by spherical subdivision J. Mach. Learn. Res. 2008, 9, 105-130
116. Hopfinger, A. J.; Wang, S.; Tokarski, J. S.; Jin, B.; Albuquerque, M.; Madhav, P. J.; Duraiswami, C. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism J. Am. Chem. Soc. 1997, 119 (43) 10509-10524
117. Müller, G. nD QSAR: A medicinal chemist's point of view Quant. Struct.-Act. Relat. 2002, 21 (4) 391-396
118. Albuquerque, M. G.; Hopfinger, A. J.; Barreiro, E. J.; De Alencastro, R. B. Four-dimensional quantitative structure: Activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists J. Chem. Inf. Comput. Sci. 1998, 38 (5) 925-938
119. Klein, C. D. P.; Hopfinger, A. J. Pharmacological activity and membrane interactions of antiarrhythmics: 4D-QSAR/QSPR analysis Pharm. Res. 1998, 15 (2) 303-311
120. Hopfinger, A. J.; Reaka, A.; Venkatarangan, P.; Duca, J. S.; Wang, S. Construction of a virtual high throughput screen by 4D-QSAR analysis: Application to a combinatorial library of glucose inhibitors of glycogen phosphorylase b J. Chem. Inf. Comput. Sci. 1999, 39 (6) 1151-1160
121. Duca, J. S.; Hopfinger, A. J. Estimation of molecular similarity based on 4D-QSAR analysis: Formalism and validation J. Chem. Inf. Comput. Sci. 2001, 41 (3-6) 1367-1387
122. Ravi, M.; Hopfinger, A. J.; Hormann, R. E.; Dinan, L. 4D-QSAR analysis of a set of ecdysteroids and a comparison to CoMFA modeling J. Chem. Inf. Comput. Sci. 2001, 41 (6) 1587-1604
123. Santos-Filho, O. A.; Hopfinger, A. J. A search for sources of drug resistance by the 4D-QSAR analysis of a set of antimalarial dihydrofolate reductase inhibitors J. Comput.-Aided Mol. Des. 2001, 15 (1) 1-12
124. Vedani, A.; Dobler, M. Multidimensional QSAR: Moving from three- to five-dimensional concepts Quant. Struct.-Act. Relat. 2002, 21 (4) 382-390
125. Vedani, A.; Dobler, M. 5D-QSAR: the key for simulating induced fit? J. Med. Chem. 2002, 45 (11) 2139-49
126. Vedani, A.; Dobler, M.; Lill, M. A. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor J. Med. Chem. 2005, 48 (11) 3700-3703
127. Bonachera, F.; Parent, B.; Barbosa, F.; Froloff, N.; Horvath, D. Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy pharmacophore triplets and adapted molecular similarity scoring schemes J. Chem. Inf. Model. 2006, 46 (6) 2457-2477
128. Ruggiu, F.; Marcou, G.; Varnek, A.; Horvath, D. I. Property-labelled fragment descriptors Mol. Inf. 2010, 29 (12) 855-868
129. JChem, version 5.9; ChemAxon: Budapest, Hungary, 2012. http://www.chemaxon.com/jchem/intro/index.html (accessed April 04, 2012).
130. Horvath, D.; Bonachera, F.; Solov'ev, V.; Gaudin, C.; Varnek, A. Stochastic versus stepwise strategies for quantitative structure-activity relationship generation: How much effort may the mining for successful QSAR models take? J. Chem. Inf. Mod 2007, 47 (3) 927-939
131. Lukacova, V.; Balaz, S. Multimode ligand binding in receptor site modeling: Implementation in CoMFA J. Chem. Inf. Comput. Sci. 2003, 43 (6) 2093-2105
132. Zhang, Y.; Lukacova, V.; Bartus, V.; Balaz, S. Structural determinants of binding of aromates to extracellular matrix: A multi-species multi-mode CoMFA study Chem. Res. Toxicol. 2007, 20 (1) 11-19
133. Zhang, Y.; Lukacova, V.; Bartus, V.; Nie, X.; Sun, G.; Manivannan, E.; Ghorpade, S. R.; Jin, X.; Manyem, S.; Sibi, M. P.; Cook, G. R.; Balaz, S. Binding of matrix metalloproteinase inhibitors to extracellular matrix: 3D-QSAR analysis Chem. Biol. Drug. Des. 2008, 72 (4) 237-248
134. Zhou, Z. H. Multi-instance learning from supervised view J. Comput. Sci. Tech. 2006, 21 (5) 800-809
135. Dooly, D. R.; Zhang, Q.; Goldman, S. A.; Amar, R. A. Multiple-instance learning of real-valued data J. Mach. Learn. Res. 2003, 3 (4-5) 651-678
136. Maron, O.; Lozano-Perez, T. A Framework for Multiple-Instance Learning. In Advances in Neural Information Processing Systems 10; Jordan, M. I.; Kearns, M. J.; Solla, S. A., Eds.; MIT Press: Cambridge, 1998; Vol. 10, pp 570-576.
137. Andrews, S.; Hofmann, T.; Tsochantaridis, I. Multiple Instance Learning with Generalized Support Vector Machines. In Eighteenth National Conference on Artificial Intelligence; MIT Press: Cambridge, MA, 2002; pp 943-944.
138. Rencher, A. C.; Schaalje, G. B. Linear Models in Statistics; John Wiley & Sons: Hoboken, NJ, 2008.
139. Alpaydin, E. Introduction to Machine Learning, 2 nd ed.; The MIT Press: Cambridge, MA, 2010.
140. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer: New York, 2001.
141. Muggleton, S. H.; De Raedt, L. Inductive logic programming: Theory and methods J. Logic Program. 1994, 19 (20) 629-679
142. De Raedt, L.; Frasconi, P.; Kersting, K.; Muggleton, S. Probabilistic Inductive Logoc Programming. Theory and Applications; Springer: Berlin, Heidelberg, 2008.
143. Kersting, K. An Inductive Logic Programming Approach to Statistical Relational Learning; IOS Press: Amsterdam, 2006.
144. King, R. D.; Muggleton, S. H.; Srinivasan, A.; Sternberg, M. J. E. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming Proc. Natl. Acad. Sci. U.S.A. 1996, 93 (1) 438-442
145. Srinivasana, A.; Muggleton, S. H.; Sternberg, M. J. E.; King, R. D. Theories for mutagenicity: A study in first-order and feature-based induction Artif. Intell. 1996, 85 (1-2) 277-299
146. Amini, A.; Muggleton, S. H.; Lodhi, H.; Sternberg, M. J. E. A novel logic-based approach for quantitative toxicology prediction J. Chem. Inf. Model. 2007, 47 (3) 998-1006
147. Sternberg, M. J. E.; Muggleton, S. H. Structure activity relationships (SAR) and pharmacophore discovery using Inductive Logic Programming (ILP) QSAR Comb. Sci. 2003, 22 (5) 527-532
148. Cannon, E. O.; Amini, A.; Bender, A.; Sternberg, M. J. E.; Muggleton, S. H.; Glen, R. C.; Mitchell, J. B. O. Support vector inductive logic programming outperforms the Naive Bayes Classifier and inductive logic programming for the classification of bioactive chemical compounds J. Comput.-Aided Mol. Des. 2007, 21 (5) 269-280
149. Tsunoyama, K.; Amini, A.; Sternberg, M. J. E.; Muggleton, S. H. Scaffold hopping in drug discovery using inductive logic programming J. Chem. Inf. Model. 2008, 48 (5) 949-957
150. King, R. D.; Srinivasan, A. The discovery of indicator variables for QSAR using inductive logic programming J. Comput.-Aided Mol. Des. 1997, 11 (6) 571-580
151. Marchand-Geneste, N.; Watson, K. A.; Alsberg, B. K.; King, R. D. New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors J. Med. Chem. 2002, 45 (2) 399-409
152. Buttingsrud, B.; King, R. D.; Alsberg, B. K. An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programming J. Chemom. 2007, 21 (12) 509-519
153. Varnek, A.; Gaudin, C.; Marcou, G.; Baskin, I.; Pandey, A. K.; Tetko, I. V. Inductive transfer of knowledge: Application of multi-task learning and feature net approaches to model tissue-air partition coefficients J. Chem. Inf. Model. 2009, 49 (1) 133-144
154. Caruana, R. Multitask learning Mach. Learn. 1997, 28 (1) 41-75
155. Evgeniou, T.; Micchelli, C. A.; Pontil, M. Learning multiple tasks with kernel methods J. Mach. Learn. Res. 2005, 6
156. Markou, M.; Singh, S. Novelty detection: A review-Part 1: Statistical approaches Signal Process. 2003, 83 (12) 2481-2497
157. Markou, M.; Singh, S. Novelty detection: A review-Part 2:: Neural network based approaches Signal Process. 2003, 83 (12) 2499-2521
158. Tax, D. M. J.; Duin, R. P. W. Support vector data description Mach. Learn. 2004, 54 (1) 45-66
159. Hristozov, D.; Oprea, T. I.; Gasteiger, J. Ligand-based virtual screening by novelty detection with self-organizing maps J. Chem. Inf. Model. 2007, 47 (6) 2044-2062
160. Karpov, P. V.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Virtual screening based on one-class classification Dokl. Chem. 2011, 437 (2) 107-111
161. Karpov, P. V.; Osolodkin, D. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. One-class classification as a novel method of ligand-based virtual screening: The case of glycogen synthase kinase 3OI inhibitors Bioorg. Med. Chem. Lett. 2011, 21 (22) 6728-6731
162. Karpov, P. V.; Baskin, I. I.; Zhokhova, N. I.; Zefirov, N. S. Method of continuous molecular fields in the one-class classification task Dokl. Chem. 2011, 440 (2) 263-265
163. Liu, Y. H.; Liu, Y. C.; Chen, Y. J. Fast support vector data descriptions for novelty detection IEEE Trans. Neural Networks 2010, 21 (8) 1296-1313
164. Wang, D.; Yeung, D. S.; Tsang, E. C. C. Structured one-class classification IEEE Trans. Syst., Man, Cyber., Part B 2006, 36 (6) 1283-1294
165. Li, C.; Zhang, Y.; Li, X. OcVFDT: One-class very fast decision tree for one-class classification of data streams. In 3rd International Workshop on Knowledge Discovery from Sensor Data, SensorKDD'09 in Conjunction with the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-09; ACM: New York, NY, 2009; pp 79-86.
166. Angiulli, F. Condensed nearest neighbor data domain description IEEE Trans. Pattern Anal. 2007, 29 (10) 1746-1758
167. Görnitz, N.; Kloft, M.; Brefeld, U. Active and semi-supervised data domain description Lect. Notes Comput. Sci. 2009, 5781, 407-422
168. Lee, H. J.; Cho, S. SOM-based novelty detection using novel data Lect. Notes Comput. Sci. 2005, 3578, 359-366
169. Hoffmann, H. Kernel PCA for novelty detection Pattern Recogn. 2007, 40 (3) 863-874
170. Kwok, J. T.; Tsang, I. W. H.; Zurada, J. M. A class of single-class minimax probability machines for novelty detection IEEE Trans. Neural Networks 2007, 18 (3) 778-785
171. Cohen, G.; Sax, H.; Geissbuhler, A. Novelty detection using one-class parzen density estimator. An application to surveillance of nosocomial infections Stud. Health Technol. Inform. 2008, 136, 21-26
172. Savran, Y.; Gunsel, B. Novelty detection on metallic surfaces by GMM learning in Gabor space Lect. Notes Comput. Sci. 2010, 6112, 325-334
173. Clifton, D. A.; Hugueny, S.; Tarassenko, L. Novelty detection with multivariate extreme value statistics J. Signal Process. Syst. 2010, 1-19
174. Rätsch, G.; Mika, S.; Schölkopf, B.; Müller, K. R. Constructing boosting algorithms from SVMs: An application to one-class classification IEEE Trans. Pattern Anal. 2002, 24 (9) 1184-1199
175. Kassab, R.; Alexandre, F. Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data Mach. Learn. 2009, 74 (2) 191-234
176. Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR applicability domain estimation by projection of the training set in descriptor space: A review Altern. Lab. Anim. 2005, 33 (5) 445-459
177. Baskin, I. I.; Kireeva, N.; Varnek, A. The one-class classification approach to data description and to models applicability domain Mol. Inf. 2010, 29 (8-9) 581-587
178. Schölkopf, B.; Smola, A. J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond; MIT Press: Cambridge, MA; London, 2002.
179. Fechner, N.; Jahn, A.; Hinselmann, G.; Zell, A. Estimation of the applicability domain of kernel-based machine learning models for virtual screening J. Cheminf. 2010, 2, 1
180. Soto, A. J.; Vazquez, G. E.; Strickert, M.; Ponzoni, I. Target-driven subspace mapping methods and their applicability domain estimation Mol. Inf. 2011, 30 (9) 779-789
181. Sugiyama, M.; Krauledat, M.; Mueller, K.-R. Covariate shift adaptation by importance weighted cross validation J. Mach. Learn. Res. 2007, 8, 985-1005
182. Daume, H.; Marcu, D. Domain adaptation for statistical classifiers J. Artif. Intell. Res. 2006, 26, 101-126
183. Satpal, S.; Sarawagi, S. Domain adaptation of conditional probability models via feature subsetting Lect. Notes Comput. Sci. 2007, 4702, 224-235
184. Zhang, Q.; Qiu, X.; Huang, X.; Wu, L. Domain adaptation for conditional random fields Lect. Notes Comput. Sci. 2008, 4993, 192-202
185. Jiang, J.; Zha, C. A Two-Stage Approach to Domain Adaptation for Statistical Classifiers. In 16th ACM Conference on Information and Knowledge Management; ACM: New York, 2007; pp 401-410.
186. Arnold, A.; Cohen, W. W. Intra-Document Structural Frequency Features for Semi-Supervised Domain Adaptation. In 17th ACM Conference on Information and Knowledge Management; ACM: New York, 2008; pp 1291-1299.
187. Gupta, R.; Sarawagi, S. Domain adaptation of information extraction models SIGMOD Record 2008, 37 (4) 35-40
188. Swamidass, S. J.; Azencott, C. A.; Lin, T. W.; Gramajo, H.; Tsai, S. C.; Baldi, P. Influence relevance voting: An accurate and interpretable virtual high throughput screening method J. Chem. Inf. Model. 2009, 49 (4) 756-766
189. Johnson, A. M.; Maggiora, G. M. Concepts and Applications of Molecular Similarity; John Willey & Sons: New York, 1990.
190. Kimeldorf, G. S.; Wahba, G. Some results on Tchebycheffian spline functions J. Math. Anal. Appl. 1971, 33, 82-95
191. Müller, K. R.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B. An introduction to kernel-based learning algorithms IEEE Trans. Neural Networks 2001, 12 (2) 181-201
192. Hofmann, T.; Schölkopf, B.; Smola, A. J. Kernel methods in machine learning Ann. Stat. 2008, 36 (3) 1171-1220
193. Shawe-Taylor, J.; Cristianini, N. Kernel Methods for Pattern Analysis; Cambridge University Press: New York, 2004.
194. Gönen, M.; Alpaydin, E. Multiple kernel learning algorithms J. Mach. Learn. Res. 2011, 12, 2211-2268
195. Lanckriet, G. R. G.; Cristianini, N.; Bartlett, P.; El Ghaoui, L.; Jordan, M. I. Learning the kernel matrix with semidefinite programming J. Mach. Learn. Res. 2004, 5, 27-72
196. Cristianini, N.; Kandola, J.; Elisseeff, A.; Shawe-Taylor, J. On kernel target alignment Stud. Fuzziness Soft. Comput. 2006, 194, 205-256
197. Ong, C. S.; Smola, A. J.; Williamson, R. C. Learning the kernel with hyperkernels J. Mach. Learn. Res. 2005, 6, 1043-4071
198. Zhokhova, N. I.; Baskin, I. I.; Bakhronov, D. K.; Palyulin, V. A.; Zefirov, N. S. Method of continuous molecular fields in the search for quantitative structure-activity relationships Dokl. Chem. 2009, 429 (1) 273-276
199. Zhuang, J.; Tsang, I. W.; Hoi, S. C. H. A family of simple non-parametric kernel learning algorithms J. Mach. Learn. Res. 2011, 12, 1313-1347
200. Kulis, B.; Sustik, M. A.; Dhillon, I. S. Low-rank kernel learning with bregman matrix divergences J. Mach. Learn. Res. 2009, 10, 341-376
201. Johnson, R.; Zhang, T. Graph-based semi-supervised learning and spectral kernel design IEEE Trans. Inf. Theory 2008, 54 (1) 275-288
202. Weinberger, K. Q.; Saul, L. K. Distance metric learning for large margin nearest neighbor classification J. Mach. Learn. Res. 2009, 10, 207-244
203. Huang, K. Z.; Ying, Y. M.; Campbell, C. Generalized sparse metric learning with relative comparisons Knowl. Inf. Syst. 2011, 28 (1) 25-45
204. Mohr, J. A.; Jain, B. J.; Obermayer, K. Molecule kernels: A descriptor- and alignment-free quantitative structure-activity relationship approach J. Chem. Inf. Model. 2008, 48 (9) 1868-1881
205. Saigo, H.; Vert, J. P.; Ueda, N.; Akutsu, T. Protein homology detection using string alignment kernels Bioinformatics 2004, 20 (11) 1682-1689
206. Hoffmann, B.; Zaslavskiy, M.; Vert, J. P.; Stoven, V. A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: Application to ligand prediction. BMC Bioinf. 2010, 11, Art. No. 99.
207. Haasdonk, B. Feature space interpretation of SVMs with indefinite kernels IEEE Trans. Pattern Anal. 2005, 27 (4) 482-492
208. Pekalska, E.; Haasdonk, B. Kernel discriminant analysis for positive definite and indefinite kernels IEEE Trans. Pattern Anal. 2009, 31 (6) 1017-1031
209. Sun, H. W.; Wu, Q. A. Least square regression with indefinite kernels and coefficient regularization Appl. Comput. Harmon. Anal. 2011, 30 (1) 96-109
210. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH Publishers: Weinheim, 2000.
211. Baskin, I.; Varnek, A. Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening. In Chemoinformatics Approaches to Virtual Screening; Varnek, A.; Tropsha, A., Eds.; RSC Publishing: Cambridge, 2008; pp 1-43.
212. Bakir, G.; Hofmann, T.; Schoelkopf, B.; Smola, A. J.; Taskar, B.; Vishwanathan, S. V. N. Predicting Structured Data; The MIT Press: Cambridge, MA, 2007.
213. Cook, D. J.; Holder, L. B. Mining Graph Data; Wiley-Interscience: Hoboken, NJ, 2007.
214. De Raedt, L. Logical and Relational Learning; Springer-Verlag: Berlin, Heidelberg, 2008.
215. Tsochantaridis, I.; Joachims, T.; Hofmann, T.; Altun, Y. Large margin methods for structured and interdependent output variables J. Mach. Learn. Res. 2005, 6, 1453-1484
216. Kramer, S.; De Raedt, L.; Helma, C. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; ACM Press: New York, 2001; pp 136-143.
217. De Raedt, L.; Kramer, S. The Levelwise Version Space Algorithm and its Application to Molecular Fragment Finding. In The Seventeenth International Joint Conference on Articial Intelligence; Morgan Kaufmann: San Francisco, 2001; pp 853-862.
218. Kramer, S.; De Raedt, L. Feature Construction with Version Spaces for Biochemical Applications. In The Eighteenth International Conference on Machine Learning; Morgan Kaufmann: San Francisco, 2001; pp 258-265.
219. Inokuchi, A., Mining Generalized Substructures from a Set of Labeled Graphs. In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04); IEEE Computer Society: Brighton, UK, 2004; pp 415-418.
220. Yan, X. F.; Han, J. W. gSpan: Graph-Based Substructure Pattern Mining. In 2002 IEEE International Conference on Data Mining, Proceedings; Kumar, V.; Tsumoto, S.; Zhong, N.; Yu, P. S.; Wu, X. D., Eds.; IEEE Computer Soc.: Los Alamitos, 2002; pp 721-724.
221. Chi, Y.; Muntz, R. R.; Nijssen, S.; Kok, J. N. Frequent subtree mining - an overview Fundam. Inform. 2005, 66 (1-2) 161-198
222. Saigo, H.; Tsuda, K. Graph Mining in Chemoinformatics. In Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques; Lodhi, H.; Yamanishi, Y., Eds.; IGI Global: Hershey, PA, 2010; pp 95-128.
223. Baskin, I. I.; Zhokhova, N. I.; Palyulin, V. A.; Zefirov, N. S. Additive inductive learning in QSAR/QSPR studies and molecular modeling Chem. Central J. 2009, 3, 1-1
224. Erhan, D.; L'Heureux, P.-J.; Yue, S. Y.; Bengio, Y. Collaborative filtering on a family of biological targets J. Chem. Inf. Model. 2006, 46 (2) 626-635
225. Faulon, J. L.; Misra, M.; Martin, S.; Sale, K.; Sapra, R. Genome scale enzyme-Metabolite and drug-Target interaction predictions using the signature molecular descriptor Bioinformatics 2008, 24 (2) 225-233
226. Jacob, L.; Vert, J. P. Protein-ligand interaction prediction: An improved chemogenomics approach Bioinformatics 2008, 24 (19) 2149-2156
227. Geppert, H.; Humrich, J.; Stumpfe, D.; Gaertner, T.; Bajorath, J. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors J. Chem. Inf. Model. 2009, 49 (4) 767-779
228. Bakir, G.; Hofmann, T.; Schölkopf, B.; Smola, A. J.; Taskar, B.; Vishwanathan, S. V. N. Predicting Structured Data; MIT Press: Cambridge, Massachusetts, London, 2007.
229. Cortes, C.; Mohri, M.; Weston, J. A general regression technique for learning transductions.. In ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning; ACM: New York, NY, USA, 2005; pp 153-160.
230. Geurts, P.; Wehenkel, L.; D'Alché-Buc, F., Kernelizing the Output of Tree-Based Methods. In ACM International Conference Proceeding Series; 2006; Vol. 148, pp 345-352.
231. Geurts, P.; Wehenkel, L.; D'Alché-Buc, F., Gradient Boosting for Kernelized Output Spaces. In ACM International Conference Proceeding Series, 2007; Vol. 227, pp 289-296.
232. Yu, C. N. J.; Joachims, T.; Elber, R.; Pillardy, J. Support vector training of protein alignment models J. Comput. Biol. 2008, 15 (7) 867-880
233. Geurts, P.; Touleimat, N.; Dutreix, M.; d'Alché-Buc, F. Inferring biological networks with output kernel trees BMC Bioinf. 2007, 8 (Suppl. 2) S4
234. Rathke, F.; Hansen, K.; Brefeld, U.; Muller, K.-R. StructRank: A new approach for ligand-based virtual screening J. Chem. Inf. Model. 2010, 51 (1) 83-92
235. Vapnik, V. Transductive Inference and Semi-Supervised Learning. In Semi-Supervised Learning; Chapelle, O.; Schoelkopf, B.; Zien, A., Eds.; MIT Press: Cambridge, MA, 2006; pp 453-472.
236. Cramer, R. D.; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins J. Am. Chem. Soc. 1988, 110 (18) 5959-5967
237. Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity J. Med. Chem. 1994, 37 (24) 4130-46
238. Goodford, P. The Basic Principles of GRID. In Molecular Interaction Fields. Applications in Drug Discovery and ADME Prediction; Cruciani, G., Ed.; Wiley-VCH: Weinheim, 2006; pp 3-26.
239. Silverman, B. D.; Platt, D. E. Comparative molecular moment analysis (CoMMA): 3D-QSAR without molecular superposition J. Med. Chem. 1996, 39 (11) 2129-40
240. Bravi, G.; Gancia, E.; Mascagni, P.; Pegna, M.; Todeschini, R.; Zaliani, A. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D QSAR study in a series of steroids J. Comput.-Aided Mol. Des. 1997, 11 (1) 79-92
241. Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRid-INdependent descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors J. Med. Chem. 2000, 43 (17) 3233-43
242. Cruciani, G.; Pastor, M.; Guba, W. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds Eur. J. Pharm. Sci. 2000, 11 (Suppl. 2) S29-S39
243. Carbo-Dorca, R.; Robert, D.; Amat, L.; Girones, X.; Besalu, E. Molecular Quantum Similarity in QSAR and Drug Design; Springer: Berlin, Heidelberg, New York, 2000.
244. Halberstam, N. M.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Construction of neural-network structure-conditions-property relationships: Modeling of the physicochemical properties of hydrocarbons Dokl. Chem. 2002, 384 (1-3) 140-143
245. Oprisiu, I.; Varlamova, E.; Muratov, E.; Artemenko, A.; Marcou, G.; Polishchuk, P.; Kuz'min, V. QSPR approaches to predict non-additive properties of mixtures. Application to bubble point temperatures of binary mixtures of liquid. Mol. Inf.. 2012, 31, accepted for publication.
246. Ramsay, J. O.; Silverman, B. W. Functional Data Analysis, 2 nd ed.; Springer: New York, 2005.
247. Devillers, J. Neural Networks in QSAR and Drug Design; Academic Press: London, 1996.
248. Bengio, Y. Learning deep architectures for AI Found. Trends Mach. Learn. 2009, 2 (1) 1-127
249. Wu, T.-F.; Lin, C.-J.; Weng, R. C. Probability estimates for multi-class classification by pairwise coupling J. Mach. Learn. Res. 2004, 5, 975-1005
250. Huang, T.-M.; Kecman, V.; Kopriva, I. Kernel Based Algorithms for Mining Huge Data Sets. Supervised, Semi-Supervised, and Unsupervised Learning; Springer: Berlin, Heidelberg, 2006.
251. Huang, T. M.; Kecman, V.; Kopriva, I. Iterative single data algorithm for kernel machines from huge data sets: Theory and performance Stud. Comput. Intell. 2006, 17, 61-95
252. Shalev-Shwartz, S.; Singer, Y.; Srebro, N.; Cotter, A. Pegasos: primal estimated sub-gradient solver for SVM Math. Program. 2011, 127 (1) 3-30
253. Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Wang, X.-R.; Lin, C.-J. LIBLINEAR: A library for large linear classification J. Mach. Learn. Res. 2008, 9, 1871-1874
254. Kivinen, J.; Smola, A. J.; Williamson, R. C. Online learning with kernels IEEE Trans. Signal Process. 2004, 52 (8) 2165-2176
255. Taniguchi, M.; Du, H.; Lindsey, J. S. Virtual libraries of tetrapyrrole macrocycles: Combinatorics, isomers, product distributions, and data mining J. Chem. Inf. Model. 2011, 51 (9) 2233-2247
256. Quinonero-Candela, J.; Sugiyama, M.; Schwaighofer, A.; Lawrence, N. D. Dataset Shift in Machine Learning; MIT Press: Cambridge, MA, 2009.
257. Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; Vaughan, J. W. A theory of learning from different domains Mach. Learn. 2010, 79 (1-2) 151-175
258. Mansour, Y. Learning and domain adaptation Lect. Notes Comput. Sci. 2009, 5808, 32-34
259. Mansour, Y. Learning and domain adaptation Lect. Notes Comput. Sci. 2009, 5809, 4-6
260. Pathak, M. A.; Nyberg, E. H. Learning algorithms for domain adaptation Lect. Notes Comput. Sci. 2009, 5828, 293-307
261. Woodward, A. M.; Alsberg, B. K.; Kell, D. B. The effect of heteroscedastic noise on the chemometric modelling of frequency domain data Chemom. Intell. Lab. Syst. 1998, 40 (1) 101-107
262. Lopera, L. G.; Cepeda-Cuervo, E.; Achcar, J. A. Heteroscedastic normal-exponential mixture models: Bayesian and classical approaches Appl. Math. Comput. 2011, 218 (7) 3635-3648
263. Munoz-Gonzalez, L.; Lazaro-Gredilla, M.; Figueiras-Vidal, A. R. Heteroscedastic Gaussian Process Regression Using Expectation Propagation. In 2011 IEEE International Workshop on Machine Learning for Signal Processing; Tan, T.; Katagiri, S.; Tao, J.; Nakamura, A.; Larsen, J., Eds.; IEEE: New York, 2011.
264. Ranu, S.; Calhoun, B. T.; Singh, A. K.; Swamidass, S. J. Probabilistic substructure mining from small-molecule screens Mol. Inf 2011, 30 (9) 809-815
265. Estabrooks, A.; Jo, T.; Japkowicz, N. A multiple resampling method for learning from imbalanced data sets Comput. Intell. 2004, 20 (1) 18-36
266. Kondratovich, E. P.; Zhokhova, N. I.; Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. Fragmental descriptors in (Q)SAR: Prediction of the assignment of organic compounds to pharmacological groups using the support vector machine approach Russ. Chem. Bull. 2009, 58 (4) 657-662
267. Baldi, P.; Azencott, C.; Swamidass, S. J. Bridging the gap between neural network and kernel methods: Applications to drug discovery Front. Artif. Intell. Appl. 2011, 226, 3-13
268. Sheridan, R. P.; Singh, S. B.; Fluder, E. M.; Kearsley, S. K. Protocols for bridging the peptide to nonpeptide gap in topological similarity searches J. Chem. Inf. Comput. Sci. 2001, 41 (5) 1395-1406
269. Truchon, J. F.; Bayly, C. I. Evaluating virtual screening methods: Good and bad metrics for the "early recognition" problem J. Chem. Inf. Model. 2007, 47 (2) 488-508
270. Swamidass, S. J.; Azencott, C. A.; Daily, K.; Baldi, P. A CROC stronger than ROC: Measuring, visualizing and optimizing early retrieval Bioinformatics 2010, 26 (10) 1348-1356
271. Tax, D. M. J. One-Class Classification. Concept-Learning in the Absence of Counter-Examples; Doctor Thesis, Technische Universiteit Delft, Delft, The Netherlands, 2001.
272. Bhardwaj, N.; Gerstein, M.; Lu, H. Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique. BMC Bioinf.. 2010, 11 (SUPPL.1).
273. Blanchard, G.; Lee, G.; Scott, C. Semi-supervised novelty detection J. Mach. Learn. Res. 2010, 11, 2973-3009
274. Guha, R. On the interpretation and interpretability of quantitative structure-activity relationship models J. Comput.-Aided Mol. Des. 2008, 22 (12) 857-871
275. Hansch, C.; Muir, R. M.; Fujita, T.; Maloney, P. P.; Geiger, F.; Streich, M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients J. Am. Chem. Soc. 1963, 85 (18) 2817-2824
276. Baskin, I. I.; Ait, A. O.; Halberstam, N. M.; Palyulin, V. A.; Zefirov, N. S. An approach to the interpretation of backpropagation neural network models in QSAR studies SAR QSAR Environ. Res. 2002, 13 (1) 35-41
277. Guha, R.; Jurs, P. C. Interpreting computational neural network QSAR Models: A measure of descriptor importance J. Chem. Inf. Model. 2005, 45 (3) 800-806
278. Guha, R.; Stanton, D. T.; Jurs, P. C. Interpreting computational neural network quantitative structure-activity relationship models: A detailed interpretation of the weights and biases J. Chem. Inf. Model. 2005, 45 (4) 1109-1121
279. Baehrens, D.; Schroeter, T.; Harmeling, S.; Kawanabe, M.; Hansen, K.; Müller, K. R. How to explain individual classification decisions J. Mach. Learn. Res. 2010, 11, 1803-1831
280. Hansen, K.; Baehrens, D.; Schroeter, T.; Rupp, M.; Müller, K. R. Visual interpretation of kernel-based prediction models Mol. Inf. 2011, 30 (9) 817-826
281. Pan, S. J.; Yang, Q. A survey on transfer learning IEEE Trans. Knowl. Data Eng. 2010, 22 (10) 1345-1359
282. Ratle, F.; Kanevski, M.; Terrettaz-Zufferey, A. L.; Esseiva, P.; Ribaux, O. A comparison of one-class classifiers for novelty detection in forensic case data Lect. Notes Comput. Sci. 2007, 4881, 67-76
283. Khan, S. S.; Madden, M. G. A survey of recent trends in one class classification Lect. Notes Comput. Sci. 2010, 6206, 188-197
284. Cohn, D.; Atlas, L.; Ladner, R. Improving generalization with active learning Mach. Learn. 1994, 15 (2) 201-221
285. Kanamori, T. Statistical asymptotic theory of active learning Ann. I. Stat. Math. 2002, 54 (3) 459-475
286. Prince, M. Does active learning work? A review of the research J. Eng. Educ. 2004, 93 (3) 223-231
287. Asogawa, M.; Osoda, T.; Fujiwara, Y.; Yamashita, Y. Efficient drug screening using active learning NEC J. Adv. Technol. 2005, 2 (2) 145-148
288. Vogiatzis, D.; Tsapatsoulis, N. Active learning for microarray data Int. J. Approx. Reason. 2008, 47 (1) 85-96
289. Mohamed, T. P.; Carbonell, J. G.; Ganapathiraju, M. K. Active learning for human protein-protein interaction prediction. BMC Bioinf. 2010, 11 (Suppl.1), Art. No. S57.
290. Bayarri, M. J.; Berger, J. O. The interplay of Bayesian and frequentist analysis Stat. Sci. 2004, 19 (1) 58-80
291. Baskin, I.; Marcou, G.; Varnek, A. Tutorial on Ensemble Learning. http://infochim.u-strasbg.fr/new/CS3-2010/Tutorial/Ensemble/EnsembleModeling. pdf (accessed April 5, 2012).
292. Kuncheva, L. I. Combining Pattern Classifiers: Methods and Algorithms; Wiley-Interscience: Hoboken, NJ, 2004.
293. Huang, T. M.; Kecman, V.; Kopriva, I. Semi-supervised learning and applications Stud. Comput. Intell. 2006, 17, 125-173
294. Joachims, T. Transductive Support Vector Machines. In Semi-Supervised Learning; Chapelle, O.; Schoelkopf, B.; Zien, A., Eds.; MIT Press: Cambridge, MA, 2006; pp 105-117.
295. Liu, J.; Wang, H.; Zhao, T. Protein-protein interaction extraction based on combining TSVM and active learning Gaojishu Tongxin/Chinese High Technol. Lett. 2009, 19 (5) 480-486
296. Joachims, T. SVMlight, version 6.02; 2008. http://svmlight.joachims.org/ (accessed April 4, 2012).
297. Joachims, T. SGTlight, version 1.00; 2003. http://sgt.joachims.org/ (accessed April 4, 2012).
298. Huang, T.-M.; Kecman, V. SemiL, 2005. http://www.learning-from-data.com/ te-ming/semil.htm (accessed April 4, 2012).
299. Belkin, M.; Niyogi, P.; Sindhwani, V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples J. Mach. Learn. Res. 2006, 7, 2399-2434
300. Miao, Z. M.; Zhao, L. W.; Hu, G. Y.; Wang, Q. Semi-supervised learning based on one-class classification Moshi Shibie yu Rengong Zhineng/Pattern Recognit. Artif. Intell. 2009, 22 (6) 924-930
301. Pan, Z. S.; Yan, Y. S.; Miao, Z. M.; Ni, G. Q.; Zhang, H. Semi-supervised learning based on one-class classification and ensemble learning Jiefangjun Ligong Daxue Xuebao/J. PLA Univ. Sci. Technol. (Natural Science Ed.) 2010, 11 (4) 397-402
302. Stuttgart Neural Network Simulator (SNNS), version 4.3. http://www.ra.cs.uni-tuebingen.de/SNNS/ (accessed April 4, 2012).
303. Bakker, B.; Heskes, T. Task clustering and gating for Bayesian multitask learning J. Mach. Learn. Res. 2004, 4 (1) 83-99
304. Pillonetto, G.; Dinuzzo, F.; De Nicolao, G. Bayesian online multitask learning of Gaussian processes IEEE Trans. Pattern Anal. 2010, 32 (2) 193-205
305. Lu, W. C.; Chen, N. Y.; Li, G. Z.; Yang, J. Multitask Learning Using Partial Least Squares Method. In Proceedings of the Seventh International Conference on Information Fusion; Svensson, P.; Schubert, J., Ed.; International Society of Information Fusion: Stockholm, Sweden, 2004; Vol. 1, pp 79-84.
306. Dekel, O.; Long, P. M.; Singer, Y. Online multitask learning Lect. Notes Comput. Sci. 2006, 4005, 453-467
307. Bueno-Crespo, A.; Sánchez-García, A.; Morales- Sánchez, J.; Sancho-Gómez, J. L. Multitask learning with data editing Lect. Notes Comput. Sci. 2007, 4527, 320-326
308. Liu, Q.; Liao, X.; Carin, H. L.; Stack, J. R.; Carin, L. Semisupervised multitask learning IEEE Trans. Pattern Anal. 2009, 31 (6) 1074-1086
309. Kato, T.; Kashima, H.; Sugiyama, M.; Asai, K. Conic programming for multitask learning IEEE Trans. Knowl. Data Eng. 2010, 22 (7) 957-968
310. Widmer, C.; Toussaint, N. C.; Altun, Y.; Rätsch, G. Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. BMC Bioinf. 2010, 11 (Suppl. 8), Art. No. S5.
311. Cortes, C.; Vapnik, V. Support-vector networks Mach. Learn. 1995, 20 (3) 273-297
312. Vapnik, V. N. An overview of statistical learning theory IEEE Trans. Neural Networks 1999, 10 (5) 988-999
313. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines ACM Trans. Intel. Syst. Technol. 2001, 2 (3) 27:1-27:27
314. Hoerl, A. E.; Kennard, R. W. Risge regression: Application to nonorthogonal problems Technometrics 1970, 12 (1) 69-82
315. Efron, B.; Hastie, T.; Johnstone, I.; Tibshirani, R.; Ishwaran, H.; Knight, K.; Loubes, J. M.; Massart, P.; Madigan, D.; Ridgeway, G.; Rosset, S.; Zhu, J. I.; Stine, R. A.; Turlach, B. A.; Weisberg, S. Least angle regression Ann. Stat 2004, 32 (2) 407-499
316. Fraley, C.; Hesterberg, T. Least angle regression and LASSO for large datasets Stat. Anal. Data Mining 2009, 1 (4) 251-259
317. Tibshirani, R. Regression shrinkage and selection via the lasso J. Roy. Stat. Soc. B 1996, 58 (1) 267-288
318. Tibshirani, R. The lasso method for variable selection in the cox model Stat. Med. 1997, 16 (4) 385-395
319. Wang, L.; Gordon, M. D.; Zhu, J. Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning. In ICDM 2006: Sixth International Conference on Data Mining, Proceedings; Clifton, C. W.; Zhong, N.; Liu, J. M.; Wah, B. W.; Wu, X. D., Eds.; IEEE Computer Soc.: Los Alamitos, 2006; pp 690-700.
320. Witten, D. M.; Tibshirani, R.; Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis Biostatistics 2009, 10 (3) 515-534
321. Witten, D. M.; Tibshirani, R. J. Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol, 2009, 8 (1), Art. No. 28.
322. Huang, T.-M.; Kecman, V.; Kopriva, I. ISDA, 2006. http://www.learning- from-data.com/download.htm (accessed April 4, 2012).
323. Sonnenburg, S.; Rätsch, G.; Henschel, S.; Widmer, C.; Behr, J.; Zien, A.; De Bona, F.; Binder, A.; Gehl, C.; Franc, V. The Shogun machine learning toolbox J. Mach. Learn. Res. 2010, 11, 1799-1802
324. SHOGUN, version 1.1.0; 2011. http://www.shogun-toolbox.org/ (accessed April 4, 2012).
325. LIBLINEAR, version 1.8; 2011. http://www.csie.ntu.edu.tw/∼cjlin/ liblinear/ (accessed April 4, 2012).
326. Huang, T.-M.; Kecman, V. LinearSVM, version 3.0; 2009. http://www.linearsvm.com/ (accessed January 24, 2012).
327. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V.; Tanchuk, V. Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I. I.; Palyulin, V. A.; Radchenko, E. V.; Welsh, W. J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-De-Sousa, J.; Zhang, Q. Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I. V. Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information J. Comput.-Aided Mol. Des. 2011, 25 (6) 533-554
328. Wang, L.; Froehlich, H.; Rieck, K.; Tsai, C.-T.; Lin, T.-J. SVDD. http://www.csie.ntu.edu.tw/∼cjlin/libsvmtools/#libsvm-for-svdd-and-finding- the-smallest-sphere-containing-all-data (accessed January 12, 2012).
329. Angiulli, F. Condensed nearest neighbor data domain description Lect. Notes Comput. Sci. 2005, 3646, 12-23
330. Angelov, P.; Zhou, X. Evolving fuzzy classifier for novelty detection and landmark recognition by mobile robots Stud. Comput. Intell. 2007, 50, 89-118
331. Wu, Q.; Tan, S.; Duan, M.; Cheng, X. A two-stage algorithm for domain adaptation with application to sentiment transfer problems Lect. Notes Comput. Sci. 2010, 6458, 443-453
332. Luo, B.; Wilson, R. C.; Hancock, E. R. A linear generative model for graph structure Lect. Notes Comput. Sci. 2005, 3434, 54-62
333. Xiao, B.; Hancock, E. R. A spectral generative model for graph structure Lect. Notes Comput. Sci. 2006, 4109, 173-181
334. White, D.; Wilson, R. C. Spectral Generative Models for Graphs. In 14th International Conference on Image Analysis and Processing, Proceedings; IEEE Computer Soc.: Los Alamitos, 2007; pp 35-40.
335. White, D.; Wilson, R. C. Parts Based Generative Models for Graphs. In 19th International Conference on Pattern Recognition; Vols. 1-6, IEEE: New York, 2008; pp 3318-3321.
336. Inokuchi, A.; Washio, T.; Motoda, H. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery; Springer-Verlag: London, 2000; pp 13-23.
337. Borgelt, C.; Meinl, T.; Berthold, M. MoSS: A Program for Molecular Substructure Mining. In Proceedings of the 1st international Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations; ACM Press: New York, 2005; pp 6-15.
338. Zaki, M. J. Efficiently mining frequent trees in a forest. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; ACM Press: New York, 2002; pp 71-80.
339. Chi, Y.; Yang, Y.; Xia, Y.; Muntz, R. R. CMTreeMiner: Mining both closed and maximal frequent subtrees Lect. Notes Comput. Sci. 2004, 3056, 63-73
340. Baskin, I. I.; Palyulin, V. A.; Zefirov, N. S. A methodology for searching direct correlations between structures and properties of organic compounds by using computational neural networks Dokl. Akad. Nauk 1993, 333 (2) 176-179
341. Bianucci, A. M.; Micheli, A.; Sperduti, A.; Starita, A. A novel approach to QSPR/QSAR based on neural networks for structures Stud. Fuzziness Soft Comput. 2003, 120, 265-296
342. Goulon, A.; Duprat, A.; Dreyfus, G. Graph machines and their applications to computer-aided drug design: A new approach to learning from structured data Lect. Notes Comput. Sci. 2006, 4135, 1-19
343. Frank, E.; Xu, X. Applying Propositional Learning Algorithms to Multi-Instance Data; Working paper 06/03); University of Waikato, Department of Computer Science: Hamilton, New Zealand, 2003.
344. Ramsay, J. O.; Hooker, G.; Graves, S. Functional Data Analysis with R and MATLAB; Springer: New York, 2009.