Developing Best Practices for Descriptor-based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

Volumn 2, Issue , 2012, Pages 33-64

  Krein, Michael ^a   Huang, Tao Wei ^a   Morkowchuk, Lisa ^a   Agrafiotis, Dimitris K ^b   Breneman, Curt M ^a  

^a Rensselaer Polytechnic Institute   (United States)

^b JOHNSON AND JOHNSON PHARMACEUTICAL RESEARCH AND DEVELOPMENT   (United States)

Author keywords

Linear free energy relationship (LFER); Multiple linear regression (MLR); Property encoded shape distributions (PESD) descriptors; Quantitative structure activity relationship (QSAR); Root mean squared error (RMSE); Ultrafast shape recognition (USR)

Indexed keywords

EID: 84876581653     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9783527645121.ch2     Document Type: Chapter

References (153)

1. Brown, A.C. and Fraser, T.R. (1868) On the connection between chemical constitution and physiological action; with special reference to the physiological action of the salts of the ammonium bases derived from strychnia, Brucia, Thebaia, Codeia, Morphia, and Nicotia. Journal of Anatomy and Physiology, 2 (2), 224-242.
2. Meyer, H.H. (1899) Zur theorie der alkoholnarkose: I. Mittheilung. Welche eigenschaft der anaesthetika bedingt ihre narkotische wirkung? Archives of Experimental Pathology and Pharmacology, 42, 109-118.
3. Cronin, M.T.D. and Schultz, T.W. (2003) Pitfalls in QSAR. Journal of Molecular Structure: THEOCHEM, 622 (1-2), 39-51.
4. Zvinavashe, E., Murk, A.J., and Rietjens, I.M.C.M. (2008) Promises and pitfalls of quantitative structure- activity relationship approaches for predicting metabolism and toxicity. Chemical Research in Toxicology, 21 (12), 2229-2236.
5. Scior, T., Medina-Franco, J.L., Do, Q.T., Martinez-Mayorga, K., Yunes Rojas, J.A., and Bernard, P. (2009) How to recognize and workaround pitfalls in QSAR studies: A critical review. Current Medicinal Chemistry, 16, 4297-4313.
6. Doweyko, A. (2008) QSAR: Dead or alive? Journal of Computer-Aided Molecular Design, 22 (2), 81-89.
7. Hochberg, Y. and Tamhane, A.C. (2008) Frontmatter, John Wiley & Sons, Inc, New York.
8. Jin, H. and Ling, C.X. (2005) Using AUC and accuracy in evaluating learning algorithms. Knowledge and Data Engineering, IEEE Transactions on, 17 (3), 299-310.
9. Steffen, A., Kogej, T., Tyrchan, C., and Engkvist, O. (2009) Comparison of molecular fingerprint methods on the basis of biological profile data. Journal of Chemical Information and Modeling, 49 (2), 338-347.
10. King, R., Hirst, J., and Sternberg, M. (1993) New approaches to QSAR: Neural networks and machine learning. Perspectives in Drug Discovery and Design, 1 (2), 279-290.
11. Gramatica, P. (2007) Principles of QSAR models validation: Internal and external. QSAR & Combinatorial Science, 26 (5), 694-701.
12. Dearden, J.C., Cronin, M.T.D., and Kaiser, K.L.E. (2009)Hownot to develop a quantitative structure-activity or structure-property relationship (QSAR/ QSPR). SAR and QSAR in Environmental Research, 20 (3/4), 241-266.
13. Golbraikh, A. and Tropsha, A. (2002) Beware of q2. Journal of Molecular Graphics and Modelling, 20 (4), 269-276.
14. Weaver, S. and Gleeson, M.P. (2008) The importance of the domain of applicability in QSAR modeling. Journal of Molecular Graphics and Modelling, 26 (8), 1315-1326.
15. Tropsha, A. (2010) Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 29 (6-7), 476-488.
16. Golbraikh, A. and Tropsha, A. (2002) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Journal of Computer-Aided Molecular Design, 16 (5), 357-369.
17. Baroni, M., Clementi, S., Cruciani, G., Kettaneh-Wold, N., and Wold, S. (1993) D-optimal designs in QSAR. Quantitative Structure-Activity Relationships, 12 (3), 225-231.
18. Hansch, C. and Leo, A. (1995) Exploring QSAR: Fundamentals and applications in chemistry and biology. American Chemical Society, 1, 1-557.
19. Guha, R., Dutta, D., Jurs, P.C., and Chen, T. (2006) Local lazy regression: Making use of the neighborhood to improve QSAR predictions. Journal of Chemical Information and Modeling, 46 (4), 1836-1847.
20. Chen, J.J., Tsai, C.A., Young, J.F., and Kodell, R.L. (2005) Classification ensembles for unbalanced class sizes in predictive toxicology. SAR and QSAR in Environmental Research, 16 (6), 517-529.
21. Todeschini, R. and Consonni, V. (2000) Handbook of Molecular Descriptors, vol. 11, Wiley-VCH, Weinheim
22. Hammett, L.P. (1937) The effect of structure upon the reactions of organic compounds. Benzene derivatives. Journal of the American Chemical Society, 59 (1), 96-103.
23. Taft, R.W. (1952) Polar and steric substituent constants for aliphatic and obenzoate groups from rates of esterification and hydrolysis of esters. Journal of the American Chemical Society, 74, 3120-3128.
24. Hansch, C., Muir, R.M., Fujita, T., Maloney, P.P., Geiger, F., and Streich, M. (1963) The correlation of biological activity of plant growth regulators and chloromycetin derivatives withHammett constants and partition coefficients. Journal of the American Chemical Society, 85, 2817-2824.
25. Hansch, C., Kurup, A.,Garg, R., andGao, H. (2001) Chem-bioinformatics and QSAR:Areview of QSARlacking positive hydrophobic terms. Chemical Reviews, 101 (3), 619-672.
26. Garg, R., Gupta, S.P., Gao, H., Babu, M.S., Debnath, A.K., and Hansch, C. (1999) Comparative QSAR studies on anti-HIV drugs. Chemical Reviews, 99, 3525-3601.Garg, R., Gupta, S.P., Gao, H., Babu, M.S.
27. Guha, R. and Schürer, S. (2008) Utilizing high throughput screening data for predictive toxicology models: Protocols and application toMLSCNassays. Journal of Computer-Aided Molecular Design, 22 (6), 367-384.
28. Chen, B. and Wild, D.J. (2010) PubChem BioAssays as a data source for predictive models. Journal of MolecularGraphics and Modelling, 28 (5), 420-426.
29. Fliri, A.F., Loging, W.T., Thadeio, P.F., and Volkmann, R.A. (2005) Biospectra analysis: Model proteome characterization for linking molecular structure and biological response. Journal of Medicinal Chemistry, 48, 6918-6925.
30. Fliri, A.F., Loging, W.T., Thadeio, P.F., and Volkmann, R.A. (2005) Biological spectra analysis: Linking biological activity profiles to molecular structure. Proceedings of the National Academy of Sciences of the United States of America, 102 (2), 261-266.
31. Hawkins, D.M., Basak, S.C., Kraker, J., Geiss, K.T., and Witzmann, F.A. (2006) Combining chemodescriptors and biodescriptors in quantitative structure- activity relationship modeling. Journal of Chemical Information and Modeling, 46 (1), 9-16.
32. Balasubramanian, K., Khokhani, K., and Basak, S.C. (2006) Complex graph matrix representations and characterizations of proteomic maps and chemically induced changes to proteomes. Journal of Proteome Research, 5, 1133-1142.
33. Benigni, R.,Giuliani, A., and Passerini, L. (2001) Infrared spectra as chemical descriptors for QSAR models. Journal of Chemical Information and Computer Sciences, 41 (3), 727-730.
34. Willighagen, E.L., Denissen, H.M.G.W., Wehrens, R., and Buydens, L.M.C. (2006) Onthe use of 1Hand 13C1DNMRspectra as QSPR descriptors. Journal of Chemical Information and Modeling, 46 (2), 487-494.
35. Puzyn, T., Leszczynski, J., and Cronin, M.T. (2009) Recent advances in QSAR studies: methods and applications, in Recent Advances inQSARStudies:Methods and Applications, vol. 1 (eds T. Puzyn, J. Leszczynski, and M.T. Cronin), Springer, Berlin, pp. 383-403.
36. Kier, L.B., Murray, W.J., Randic, M., and Hall, L.H. (1975) Molecular connectivity: I. Relationship to nonspecific local anaesthesia. Journal of Pharmacological Sciences, 64, 1971-1974.
37. Dearden, J.C. (2007) In silico prediction of ADMET properties: How far have we come? Expert Opinion on DrugMetabolism & Toxicology, 3 (5), 635-639.
38. Randic, M. (2001) The connectivity index 25 years after. Journal of Molecular Graphics and Modelling, 20 (1), 19-35.
39. Bonchev, D. (1983) Information Theoretic Indices for Characterization of Chemical Structures, Research Studies Press, Chichester, UK.
40. Dehmer, M. and Mowshowitz, A. (2011) A history of graph entropy measures. Information Sciences, 181 (1), 57-78.
41. Zefirov, N.S. and Palyulin, V.A. (2002) Fragmental approach in QSPR. Journal of Chemical Information and Computer Sciences, 42, 1112-1122.
42. Nikolova, N. and Jaworska, J. (2003) Approaches to measure chemical similarity - a review. The QSAR and Combinatorial Science, 22, 1006-1026.
43. Nettles, J.H., Jenkins, J.L., Bender, A., Deng, Z., Davies, J.W., and Glick, M. (2006) Bridging chemical and biological space: .Target Fishing. using 2D and 3D molecular descriptors. Journal of Medicinal Chemistry, 49 (23), 6802-6810.
44. Nettles, J.H., Jenkins, J.L., Williams, C., Clark, A.M., Bender, A., Deng, Z., Davies, J.W., and Glick, M. (2007) Flexible 3D pharmacophores as descriptors of dynamic biological space. Journal of Molecular Graphics and Modelling, 26 (3), 622.
45. Todeschini, R. and Gramatica, P. (1997) SD-modelling and prediction by WHIM descriptors: Part 5. Theory development and chemical meaning of WHIM descriptors. Quantitative Structure- Activity Relationships, 16 (2), 113-119.
46. Schuur, J.H., Selzer, P., and Gasteiger, J. (1996) The coding of the threedimensional structure of molecules by molecular transforms and its application to structure-spectra correlations and studies of biological activity. Journal of Chemical Information and Computer Sciences, 36 (2), 334-344.
47. Consonni, V., Todeschini, R., and Pavan, M. (2002) Structure/response correlations and similarity/diversity analysis byGETAWAYdescriptors: Part 1. Theory of the novel 3D molecular descriptors. Journal of Chemical Information and Modeling, 42, 682-692.
48. Cramer, R.D., Patterson, D.E., and Bunce, J.D. (1988) Comparative molecular field analysis (CoMFA): Part 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, 110, 5959-5967.
49. Cramer, R.D., Jilek, R.J., and Andrews, K.M. (2002) dbtop: topomer similarity searching of conventional structure databases. Journal of MolecularGraphics & Modelling, 20, 447-462.
50. Jilek, R.J. and Cramer, R.D. (2004) Topomers: a validated protocol for their self-consistent generation. Journal of Chemical Information and Computer Sciences, 44, 1221-1227.
51. Ballester, P.J. and Richards, W.G. (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chemistry, 28, 1711-1723.
52. Ballester, P.J. and Richards, W.G. (2007) Ultrafast shape recognition for similarity search in molecular databases. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 463, 1307-1321.
53. Ballester, P.J., Finn, P.W., and Richards, W.G. (2009) Ultrafast shape recognition: Evaluating a new ligand-based virtual screening technology. Journal of Molecular Graphics & Modelling, 27, 836-845.
54. Clark, T. (2004) QSAR and QSPR based solely on surface properties? Journal of Molecular Graphics & Modelling, 22, 519-525.
55. Liu, R., Sun, H., and So, S.S. (2001) Development of quantitative structure- property relationship models for early ADME evaluation in drug discovery: Part 2. Blood-brain barrier penetration. Journal of Chemical Information and Computer Sciences, 41 (6), 1623-1632.
56. Jover, J., Bosque, R., and Sales, J. (2008) QSPR prediction of pKa for benzoic acids in different solvents. QSAR and Combinatorial Science, 27 (5), 563-581.
57. Tulp, I., Sild, S., and Maran, U. (2009) Relationship between structure and permeability in artificial membranes: Theoretical whole molecule descriptors in development of QSAR models. QSAR and Combinatorial Science, 28 (8), 811-814.
58. Ioakimidis, L., Thoukydidis, L., Mirza, A., Naeem, S., and Reynisson, J. (2008) Benchmarking the reliability of QikProp: Correlation between experimental and predicted values. QSAR and Combinatorial Science, 27 (4), 445-456.
59. Das, S., Kokardekar, A., and Breneman, C.M. (2009) Rapid comparison of protein binding site surfaces with property encoded shape distributions. Journal of Chemical Information and Modeling, 49 (12), 2863-2872.
60. Mezey, P.G. (1993) Shape in Chemistry, Wiley-VCH, New York.
61. Mezey, P.G. (1999) The holographic electron density theorem and quantum similarity measures. Molecular Physics, 96 (2), 169-178.
62. Walker, P.D. and Mezey, P.G. (1994) Ab initio quality electron densities for proteins: A MEDLA approach. Journal of the American Chemical Society, 116, 12022-12032.
63. Breneman, C.M., Thompson, T.R., Rhem, M., and Dung, M. (1995) Electron density modeling of large systems using the transferable atom equivalent method. Computers and Chemistry, 19 (3), 161.
64. Whitehead, C.E., Breneman, C.M., Sukumar, N., and Ryan, M.D. (2003) Transferable atom equivalent multicentered multipole expansion method. Journal of Computational Chemistry, 24, 512-529.
65. Sukumar, N., Krein, M., and Breneman, C.M. (2008) Bio- and chem-informatics: Where do the twain meet? Current Opinion in Drug Discovery & Development, 11 (3), 311-319.
66. Breneman, C.M., Sundling, C.M., Sukumar, N., Shen, L., Katt, W.P., and Embrechts, M.J. (2003) New developments in PEST shape/property hybrid descriptors. Journal of Computer- Aided Molecular Design, 17, 231-240.
67. Zauhar, R.J., Moyna, G., Tian, L., Li, Z., and Welsh, W.J. (2003) Shape signatures, a new approach to computer-aided ligandand receptor-based drug design. Journal of Medicinal Chemistry, 46, 5674-5690.
68. Pastor, M., Cruciani, G., McLay, I., Pickett, S., and Clementi, S. (2000) GRid- INdependent descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors. Journal of Medicinal Chemistry, 43, 3233-3243.
69. Cruciani, G., Crivori, P., Carrupt, P.A., and Testa, B. (2000) Molecular fields in quantitative structure-permeation relationships: The VolSurf approach. Journal of Molecular Structure: THEOCHEM, 503 (1-2), 17.
70. Topliss, J.G. and Edwards, R.P. (1979) Chance factors in studies of quantitative- structure property relationships. Journal of Medicinal Chemistry, 22, 1238-1244.
71. Eriksson, L., Andersson, P., Johansson, E., and Tysklind, M. (2006) Megavariate analysis of environmental QSAR data: Part I. A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). Molecular Diversity, 10 (2), 169-186.
72. Cooley, W., Lohnes, P., and Analysis, M.D. (1971) Multivariate Data Analysis, Wiley, New York.
73. Borg, I. and Groenen, P.J.F. (1997) Modern Multidimensional Scaling: Theory and Applications, Springer, New York.
74. Sammon, J.W. (1969) A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 18, 401-409.
75. Agrafiotis, D.K. (2003) Stochastic proximity embedding. Journal of Computational Chemistry, 24 (10), 1215-1221.
76. Rassokhin, D.N. and Agrafiotis, D.K. (2003) A modified update rule for stochastic proximity embedding. Journal of Molecular Graphics and Modelling, 22 (2), 133-140.
77. Agrafiotis, D.K. and Lobanov, V.S. (2000) Nonlinear mapping networks. Journal of Chemical Information and Computer Sciences, 40 (6), 1356-1362.
78. Rassokhin, D.N., Lobanov, V.S., and Agrafiotis, D.K. (2001) Nonlinear mapping of massive data sets by fuzzy clustering and neural networks. Journal of Computational Chemistry, 22 (4), 373-386.
79. Agrafiotis, D.K., Rassokhin, D.N., and Lobanov, V.S. (2001) Multidimensional scaling and visualization of large molecular similarity tables. Journal of Computational Chemistry, 22 (5), 488-500.
80. Tenenbaum, J.B., Silva, V.d., and Langford, J.C. (2000) A global geometric framework for nonlinear dimensionality reduction. Science, 290 (5500), 2319-2323.
81. Roweis, S.T. and Saul, L.K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (5500), 2323-2326.
82. Agrafiotis, D.K. and Xu, H. (2002) A selforganizing principle for learning nonlinear manifolds. Proceedings of the National Academy of Sciences of the United States of America, 99 (25), 15869-15872.
83. Agrafiotis, D.K. and Xu, H. (2003) A geodesic framework for analyzing molecular similarities. Journal of Chemical Information and Computer Sciences, 43 (2), 475-484.
84. Agrafiotis, D.K., Xu, H., Zhu, F., Bandyopadhyay, D., and Liu, P. (2010) Stochastic proximity embedding: Methods and applications. Molecular Informatics, 29 (11), 758-770.
85. Stanforth, R.W., Kolossov, E., and Mirkin, B. (2007) A measure of domain of applicability for QSAR modelling based on intelligent K-means clustering. QSAR & Combinatorial Science, 26 (7), 837-844.
86. Kerzic, D., Blazic, B.J., and Batagelj, V. (1994) Comparison of three different approaches to the property prediction problem. Journal of Chemical Information and Computer Science, 34, 391-394.
87. Bennett, K., Demiriz, A., and Embrechts, M. (1999) Semi-Supervised Clustering Using Genetic Algorithms, Artificial Neural Networks in Engineering.
88. Rose,V.S.,Croall, I.F., andMacfie, H.J.H. (1991) An application of unsupervised neural network methodology Kohonen topology-preserving mapping to QSAR analysis. Quantitative Structure-Activity Relationships, 10 (1), 6-15.
89. Wold, S., Ruhe, A., Wold, H., and Dunn, W.J. III (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal of Scientific Statistical Computing, 5 (3), 735.
90. Hoskuldson, A. (1988) PLS regression methods. Journal of Chemometrics, 2, 211.
91. Geladi, P. (1988) Notes on the history and nature of partial least-squares (PLS) modelling. Journal ofChemometrics, 2, 231.
92. Kauffman, G.W. and Jurs, P.C. (2001) QSAR and k-nearest neighbor classification analysis of selective cyclooxygenase-2 inhibitors using topologically-based numerical descriptors. Journal of Chemical Information and Computer Sciences, 41 (6), 1553-1560.
93. Konovalov, D.A., Coomans, D., Deconinck, E., and Vander Heyden, Y. (2007) Benchmarking of QSAR models for blood-brain barrier permeation. Journal of Chemical Information and Modeling, 47 (4), 1648-1656.
94. Votano, J.R., Parham, M.,Hall, L.H., Kier, L.B., Oloff, S., Tropsha, A., Xie, Q., and Tong, W. (2004) Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis, 19 (5), 365-377.
95. Patterson, D.E., Cramer, R.D., Ferguson, A.M., Clark, R.D., and Weinberger, L.E. (1996) Neighborhood behavior: A useful concept for validation of .molecular diversity. descriptors. Journal of Medicinal Chemistry, 39 (16), 3049-3059.
96. Huuskonen, J., Salo, M., and Jyrki, Taskinen, (1997) Neural network modeling for estimation of the aqueous solubility of structurally related drugs. Journal of Pharmaceutical Sciences, 86 (4), 450-454.
97. Livingstone, D.J., Manallack, D.T., and Tetko, I.V. (1997) Data modelling with neural networks: Advantages and limitations. Journal of Computer-Aided Molecular Design, 11 (2), 135-142.
98. Guha, R. (2008)Onthe interpretation and interpretability of quantitative structure- activity relationship models. Journal of Computer-Aided Molecular Design, 22 (12), 857-871.
99. Breiman, L. (1996) Bagging predictors. Machine Learning, 24 (2), 123-140.
100. Freund, Y. and Schapire, R.E. (1995) A decision-theoretic generalization of on-line learning and an application to boosting, in Proceedings of the Second European Conference on Computational Learning Theory, Springer, Berlin, pp. 23-37.
101. Breiman, L. (1996) Stacked regressions. Machine Learning, 24 (1), 49-64.
102. Agrafiotis, D.K., Cedeño, W., and Lobanov, V.S. (2002) On the use of neural network ensembles in QSAR and QSPR. Journal of Chemical Information and Computer Sciences, 42 (4), 903-911.
103. Mattioni, B.E., Kauffman, G.W., Jurs, P.C., Custer, L.L., Durham, S.K., and Pearl, G.M. (2003) Predicting the genotoxicity of secondary and aromatic amines using data subsetting to generate a model ensemble. Journal of Chemical Information and Computer Sciences, 43 (3), 949-963.
104. Merkwirth, C., Mauser, H., Schulz- Gasch, T., Roche, O., Stahl, M., and Lengauer, T. (2004) Ensemble methods for classification in cheminformatics. Journal of Chemical Information and Computer Sciences, 44 (6), 1971-1978.
105. Seierstad, M. and Agrafiotis, D.K. (2006) A QSAR model of hERG binding using a large, diverse, and internally consistent training set. Chemical Biology & Drug Design, 67 (4), 284-296.
106. Ginn, C.M.R., Willett, P., and Bradshaw, J. (2000) Combination of molecular similarity measures using data fusion. Perspectives in Drug Discovery and Design, 20, 1-16.
107. Salim, N., Holliday, J., and Willett, P. (2002) Combination of fingerprint-based similarity coefficients using data fusion. Journal of Chemical Information and Computer Sciences, 43 (2), 435-442.
108. Baurin, N., Mozziconacci, J.-C., Arnoult, E., Chavatte, P., Marot, C., and Morin- Allory, L. (2003) 2D QSAR consensus prediction for high-throughput virtual screening. An application to COX-2 inhibition modeling and screening of the NCI database. Journal of Chemical Information and Computer Sciences, 44 (1), 276-285.
109. Baber, J.C., Shirley, W.A., Gao, Y., and Feher, M. (2005) The use of consensus scoring in ligand-based virtual screening. Journal of Chemical Information and Modeling, 46 (1), 277-288.
110. Charifson, P.S., Corkery, J.J., Murcko, M.A., and Walters, W.P. (1999) Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of Medicinal Chemistry, 42 (25), 5100-5109.
111. Paul, N. and Rognan, D. (2002) ConsDock: A new program for the consensus analysis of protein-ligand interactions. Proteins: Structure, Function, and Bioinformatics, 47 (4), 521-533.
112. Bruce, C.L., Melville, J.L., Pickett, S.D., and Hirst, J.D. (2007) Contemporary QSAR classifiers compared. Journal of Chemical Information and Modeling, 47 (1), 219-227.
113. Myles, A.J., Feudale, R.N., Liu, Y.,Woody, N.A., and Brown, S.D. (2004) An introduction to decision tree modeling. Journal of Chemometrics, 18 (6), 275-285.
114. Carvalho, D.R. and Freitas, A.A. (2004) A hybrid decision tree/genetic algorithm method for data mining. Information Sciences, 163 (1-3), 13-35.
115. Dudek, A.Z., Arodz, T., and Galvez, J. (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): A review. Combinatorial Chemistry and High Throughput Screening, 9, 213-228.
116. Hou, T., Wang, J., Zhang, W., and Xu, X. (2006) ADME evaluation in drug discovery: Part 7. Prediction of oral absorption by correlation and classification. Journal of Chemical Information and Modeling, 47 (1), 208-218.
117. Breiman, L. (2001) Random Forests. Machine Learning, 45 (1), 5-32.
118. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., and Feuston, B.P. (2003) Random forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43 (6), 1947-1958.
119. Tong,W., Hong, H., Fang, H., Xie, Q., and Perkins, R. (2003) Decision forest: combining the predictions of multiple independent decision tree models. Journal of Chemical Information and Computer Sciences, 43 (2), 525-531.
120. Cortes, C. and Vapnik, V.N. (1995) Support-vector networks. Machine Learning, 20, 273-297.
121. Scholkopf, B., Smola, A.J., Williamson, R.C., and Bartlett, P.L. (2000) New support vector algorithms. Neural Computation, 12, 1207-1245.
122. Rosipal, R. and Trejo, L.J. (2001) Kernel partial least squares regression in reproducing kernel Hilbert space. Machine Learning Resources, 2, 97-123.
123. Bennett, K. and Campbell, C. (2000) Support vector machines: Hype or hallelujah. SIGKDD Explorations, 2 (2), 1-13.
124. Embrechts, M.J., Arciniegas, F.A., Ozdemir, M., Breneman, C.M., Bennett, K.P., and Lockwood, L. (2001) Bagging Neural Network Sensitivity Analysis for Feature Reduction in QSAR Problems. 2001 INNS-IEEE International Joint Conference on Neural Networks, Washington, DC, IEEE Press, Washington, DC, pp. 2478-2482.
125. Bakken, G.A. and Jurs, P.C. (2000) Classification of multidrug-resistance reversal agents using structure-based descriptors and linear discriminant analysis. Journal of Medicinal Chemistry, 43 (23), 4534-4541.
126. Bennett, K. and Demiriz, A. (2000) Optimization approaches to semisupervised learning, in Applications and Algorithms of Complementarity (eds M.C. Ferris, O.L. Mangasarian, and J.S. Pang), Kluwer Academic, Boston.
127. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001)Drug design bymachine learning: Support vector machines for pharmaceutical data analysis. Computational Chemistry, 26, 5-14.Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001)Drug design bymachine learning: Support vector machines for pharmaceutical data analysis. Computational Chemistry, 26, 5-14.
128. Czerminski, R., Yasri, A., andHartsough, D. (2001) Use of support vector machine in pattern classification: Application to QSAR studies. Quantitative Structure- Activity Relationships, 20, 227-240.
129. Bennett, K.P. and Embrechts, M.J. (2003) An optimization perspective on partial least squares, in Advances in Learning Theory: Methods, Models and Applications, vol. 190 (eds J.A.K. Suykens, G. Horvath, S. Basu, C. Micchelli, and J. Vandewalle), IOS Press, Amsterdam, pp. 227-250.
130. Ryan, D., McLellan, M., and Breneman, C.M. (2008) QSAR model stability: How much information is in the data? American Chemical Society National Meeting, New Orleans, LA.
131. Geppert, H., Horváth, T., Gärtner, T., Wrobel, S., and Bajorath, J. (2008) Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds. Journal of Chemical Information and Modeling, 48 (4), 742-746.
132. Agarwal, S., Dugar, D., and Sengupta, S. (2010) Ranking chemical structures for drug discovery: A new machine learning approach. Journal of Chemical Information and Modeling, 50 (5), 716-731.
133. Bergeron, C., Zaretzki, J., Breneman, C., and Bennett, K.P. (2008) Multiple instance ranking. Proceedings of the 25th International Conference on Machine Learning, ACM, Helsinki, Finland, pp. 48-55.
134. Verma, R.P. and Hansch, C. (2005) An approach toward the problem of outliers in QSAR. Bioorganic and Medicinal Chemistry, 13 (15), 4597-4621.
135. Maggiora, G.M. (2006) On outliers and activity cliffs - why QSAR often disappoints. Journal of Chemical Information and Modeling, 46 (4), 1535.
136. Fourches, D., Muratov, E., and Tropsha, A. (2010) Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling, 50 (7), 1189-1204.
137. Embrechts, M.J., Robert Kewley, J., and Breneman, C. (1998) Computationally intelligent data mining for the automated design and discovery of novel pharmaceuticals, in Smart Engineering Systems: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining and Rough Sets, November 1-4, 1998 (eds C.H. Dagli, M. Akay, A.L. Buczak, O. Ersoy, and B.R. Fernandex), ASME Press, St. Louis, MO, pp. 397-403.
138. Mazzatorta, P., Benfenati, E., Neagu, D., and Gini, G. (2002) The importance of scaling in data mining for toxicity prediction. Journal of Chemical Information and Computer Sciences, 42 (5), 1250-1255.
139. Wold, S. and Dunn, W.J. (1983) Multivariate quantitative structure- activity relationships (QSAR): Conditions for their applicability. Journal of Chemical Information and Computer Sciences, 23 (1), 6-13.
140. Tropsha, A., Gramatica, P., and Gombar, V.K. (2003) The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR and Combinatorial Science, 22 (1), 69-77.
141. Roy, P.P., Leonard, J.T., and Roy, K. (2008) Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 90 (1), 31-42.
142. Liu, H., Papa, E., and Gramatica, P. (2006) QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chemical Research in Toxicology, 19 (11), 1540-1548.
143. Cramer, R.D., Bunce, J.D., Patterson, D.E., and Frank, I.E. (1988) Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quantitative Structure- Activity Relationships, 7 (1), 18-25.
144. Rücker, C., Rücker, G., and Meringer, M. (2007) y-randomization and its variants in QSPR/QSAR. Journal of Chemical Information and Modeling, 47 (6), 2345-2357.
145. Clark, R. and Fox, P. (2004) Statistical variation in progressive scrambling. Journal of Computer-Aided Molecular Design, 18 (7), 563-576.
146. Jaworska, J., Nikolova-Jeliazkova, N., and Aldenberg, T. (2005) QSAR applicability domain estimation by projection of the training set in descriptor space: a review. Alternatives to Laboratory Animals, 33 (5), 445-459.
147. Dimitrov, S., Dimitrova, G., Pavlov, T., Dimitrova, N., Patlewicz, G., Niemela, J., and Mekenyan, O. (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. Journal of Chemical Information and Modeling, 45 (4), 839-849.
148. Tetko, I.V., Sushko, I., Pandey, A.K., Zhu, H., Tropsha, A., Papa, E., Öberg, T., Todeschini, R., Fourches, D., and Varnek, A. (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling, 48 (9), 1733-1746.
149. Das, S., Krein,M.P., and Breneman, C.M. (2010) Binding affinity prediction with property-encoded shape distribution signatures. Journal of Chemical Information and Modeling, 50 (2), 298-308.
150. Guha, R. and Van Drie, J.H. (2008) Structure-activity landscape index: Identifying and quantifying activity cliffs. Journal of Chemical Information and Modeling, 48, 646-658.
151. Peltason, L. and Bajorath, J. (2007) SAR index: quantifying the nature of structure-activity relationships. Journal of Medicinal Chemistry, 50 (23), 5571-5578.
152. Peltason, L., Iyer, P., and Bajorath, J. (2010) Rationalizing three-dimensional activity landscapes and the influence of molecular representations on landscape topology and the formation of activity cliffs. Journal of Chemical Information and Modeling, 50 (6), 1021-1033.
153. Bajorath, J., Peltason, L., Wawer, M., Guha, R., Lajiness, M.S., and Van Drie, J.H. (2009) Navigating structure-activity landscapes. Drug Discovery Today, 14 (13-14), 698-705.