Calculated Molecular Properties and Multivariate Statistical Analysis

Drug Bioavailability: Estimation of Solubility, Permeability, Absorption and Bioavailability

Volumn 40, Issue , 2009, Pages 373-408

  Norinder, Ulf ^a  

^a ASTRAZENECA R AND D   (Sweden)

Author keywords

ADME; Applicability domain; Descriptors; Quantitative structure property relationship; Statistical methods; Validation

Indexed keywords

STATISTICAL METHODS;

ADME; APPLICABILITY DOMAIN; DESCRIPTORS; QUANTITATIVE STRUCTURE - PROPERTY RELATIONSHIPS; VALIDATION;

MULTIVARIANT ANALYSIS;

EID: 85017663863     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9783527623860.ch14     Document Type: Chapter

References (135)

1. http://www.talete.mi.it.
2. http://www.edusoft-lc.com/molconn.
3. http://www.chemcomp.com.
4. http://www.tripos.com.
5. http://www.molecular-networks.com.
6. Liu, H.X., Hu, R.J., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D. and Fan, B.T. (2005) The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine. Journal of Computer-Aided Molecular Design, 19, 33-46.
7. Bai, J.P.F., Utis, A., Crippen, G., He, H.-D., Fischer, V., Tullman, R., Yin, H.-Q., Hsu, C.-P., Jiang, L. and Hwang, K.-K. (2004) Use of classification regression tree in predicting oral absorption in humans. Journal of Chemical Information and Computer Sciences, 44, 2061-2069.
8. Agatonovic-Kustrin, S., Beresford, R., Pauzi, A. and Yusof, M. (2001) Theoretically-derived molecular descriptors important in human intestinal absorption. Journal of Pharmaceutical and Biomedical Analysis, 25, 227-237.
9. Klon, A.E., Lowrie, J.F. and Diller, D.J. (2006) Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. Journal of Chemical Information and Modeling, 46, 1945-1956.
10. Zmuidinavicius, D., Didziapetris, R., Japertas, P., Avdeef, A. and Petrauskas, A. (2003) Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption. Journal of Pharmaceutical Sciences, 92, 621-633.
11. Klopman, G., Stefan, L.R. and Saiakhov, R.D. (2002) ADME evaluation. 2. A computer model for the prediction of intestinal absorption in humans. European Journal of Pharmaceutical Sciences, 17, 253-263.
12. Lipinski, C.A., Lombardo, F., Dominy, B.W. and Feeney, P.J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23, 3-25.
13. http://www.scitegic.com.
14. Durant, J.L., Leland, B.A., Henry, D.R. and Nourse, J.G. (2002) Reoptimizationof MDL keys for use in drug discovery. Journal ofChemical Information and Computer Sciences, 42, 1273-1280.
15. http://www.digitalchemistry.co.uk.
16. http://www.leadscope.com.
17. http://www.mdl.com.
18. Kazius, J., McGuire, R. and Bursi, R. (2005) Derivation and validation oftoxicophores for mutagenicity prediction. Journal of Medicinal Chemistry, 48, 312-320.
19. http://www.daylight.com.
20. Niwa, T. (2003) General regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from twodimensional chemical structures. Journal of Chemical Information and Computer Sciences, 43, 113-119.
21. Perez, M.A.C., Sanz, M.B., Torres, L.R., Avalos, R.G., Gonzalez, M.P. and Diaz, H.G. (2004) A topological substructural approach for predicting human intestinal absorption of drugs. European Journal of Medicinal Chemistry, 39, 905-916.
22. Sun, H. (2004) A universal molecular descriptor system for prediction of logP, logS, logBB, and absorption. Journal of Chemical Information and Computer Sciences, 44, 748-757.
23. Hall, L.H. and Kier, L.B. (1991) The molecular connectivity chi indices and kappa shape indices in structure-property modelling, in Reviews ofComputational Chemistry, Vol. 2 (eds D.B. Boyd and K. Lipkowitz), VCH Publishers, USA, pp. 367-422.
24. Downs, G.M. (2004) Molecular descriptors, in Computational Medicinal Chemistry for Drug Discovery (eds P. Bultinck, J.P. Tollenaere and H. de Winter), Marcel Dekker, USA, pp. 515-537.
25. Gunturi, S.B. and Narayanan, R. (2007) In silico ADME modeling 3: computational models to predict human intestinal absorption using sphere exclusion and kNN QSAR methods. QSAR & Combinatorial Science, 26, 653-668.
26. Hall, L.H. and Kier, L.B. (1999) Molecular Structure Description: The Electrotopological State, Academic Press, USA.
27. Hall, L.H., Mohney, B. and Kier, L.B. (1991) The electrotopological state: structure information at the atomic level for molecular graphs. Journal ofChemical Information and Computer Sciences, 31, 76-82.
28. Kellogg, G.E., Kier, L.B., Gaillard, P. and Hall, L.H. (1996) The e-state fields. Applications to 3D QSAR. Journal of Computer-Aided Molecular Design, 10, 513-520.
29. Rose, K., Hall, L.H. and Kier, L.B. (2002) Modeling blood-brain barrier partitioning using the electrotopological state. Journal ofChemical Information and Computer Sciences, 42, 651-666.
30. Stenberg, P., Norinder, U., Luthman, K. and Artursson, P. (2001) Experimental and computational screening models for the prediction of intestinal drug absorption. Journal ofMedicinal Chemistry, 44, 1927-1937.
31. Todeschini, R. and Grammatica, P. (1997) 3D-modelling and prediction by WHIM descriptors. Part 6. Application of WHIM descriptors in QSAR studies. Quantitative Structure-Activity Relationships, 16, 120-125.
32. Todeschini, R., Grammatica, P., Marengo, E. and Provenzani, R. (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles (SMP) as toxicity biosensors of chlorophenols. Chemosphere, 33, 71-79.
33. Todeschini, R., Lasagni, M. and Marengo, E. (1994) New molecular descriptors for 2D and 3D structures. Theory. Journal of Chemometrics, 8, 263-272.
34. Todeschini, R., Vighi, M., Provenzani, R., Finzio, A. and Grammatica, P. (1996) Modeling and prediction by using whim descriptors in QSAR studies: toxicity of heterogeneous chemicals on Daphnia magna. Chemosphere, 32, 1527-1545.
35. Stanton, D.T. and Jurs, P.C. (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Analytical Chemistry, 62, 2323-2329.
36. Stanton, D.T., Mattioni, B.E., Knittel, J.J. and Jurs, P.C. (2004) Development and use of hydrophobic surface area (HSA) descriptors for computer-assisted quantitative structure-activity and structure-property relationship studies. Journal of Chemical Information and Computer Sciences, 44, 1010-1023.
37. Wildman, S.A. and Crippen, G.M. (1999) Prediction of physicochemical parameters by atomic contributions. Journal of Chemical Information and Computer Sciences, 39, 868-873.
38. Pearlman, R.S. (1980) Molecular surface area and volumes and their use in structure/activity relationships, in Physical Chemical Properties of Drugs (eds S.H., Yalkowsky, A.A. Sinkula and S.C. Valvani), Marcel Dekker, USA, pp. 321-347.
39. Wessel, M.D., Jurs, P.C., Tolan, J.W. and Muskal, S.M. (1998) Predictionofhuman intestinal absorption of drug compounds from molecular structure. Journal of Chemical Information and Computer Sciences, 38, 726-735.
40. Mazzatorta, P., Cronin, M.T.D. and Benfenati, E. (2006) A QSAR study of avian oral toxicity using support vector machines and genetic algorithms. QSAR & Combinatorial Science, 25, 616-628.
41. Cruciani, G., Pastor, M. and Guba, W. (2000) VolSurf: a new tool for the pharmacokinetic optimization of lead compound. European Journal of Pharmaceutical Sciences, 11 (Suppl. 2), S29-S39.
42. Cruciani, G., Meniconi, M., Carosati, E., Zamora, I. and Mannhold, R. (2003) VOLSURF: a tool for drug ADME properties prediction, in Drug Bioavailability, Vol. 18 (eds H. van de Waterbeemd, H. Lennernas and P. Artursson), Methods and Principles in Medicinal Chemistry, Wiley-VCH, Germany, pp. 406-419.
43. Crivori, P., Cruciani, G., Carrupt, P.A. and Testa, B. (2000) Predicting blood-brain barrier permeation from threedimensional molecular structure. Journal ofMedicinal Chemistry, 43, 2204-2216.
44. Crivori, P., Zamora, I., Speed, B., Orrenius, C. and Poggesi, I. (2004) Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates. Journal of Computer-Aided Molecular Design, 18, 155-166.
45. Afzelius, L., Zamora, I., Masimirembwa, C.M., Karlen, A., Andersson, T.B., Mecucci, S., Baroni, M. and Cruciani, G. (2004) Conformer and alignment-independent model for predicting structurally diverse competitive CYP2C9 inhibitors. Journal of Medicinal Chemistry, 47, 907-914.
46. Afzelius, L., Masimirembwa, C.M., Karlen, A., Andersson, T.B. and Zamora, I. (2002) Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors. Journal of Computer-Aided Molecular Design, 16, 443-458.
47. + channel blockers. Bioorganic and Medicinal Chemistry, 12, 2307-2315.
48. Drie, J.H. (2004) Pharmacophore discovery: a critical review, in Computational Medicinal Chemistry for Drug Discovery (ed. P. Bultinck), Marcel Dekker, USA, pp. 437-460.
49. Mason, J.S. and Pickett, S.D. (2003) Combinatorial library design, molecular similarity and diversity applications, in Burgers Medicinal Chemistry and Drug Discovery 6 (ed. D.J. Abraham), John Wiley & Sons, Inc., USA, pp. 187-242.
50. Good, A.C., Mason, J.S. and Pickett, S.D. (2000) Pharmacophore pattern application in virtual screening, library design and QSAR, in Methods and Principles in Medicinal Chemistry, Vol. 10 (eds H.J. Bohm and G. Schneider), John Wiley & Sons, Inc., USA, pp. 131-159.
51. Mason, J.S., Morize, I., Menard, P.R., Cheney, D.L., Hulme, C.R. and Labaudiniere, R.F. (1999) New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. Journal of Medicinal Chemistry, 42, 3251-3264.
52. Saeh, J.C., Lyne, P.D., Takasaki, B.K. and Cosgrove, D.A. (2005) Lead hopping using SVM and 3D pharmacophore fingerprints. Journal of Chemical Information and Modeling, 45, 1122-1133.
53. Penzotti, J.E., Lamb, M.L., Evensen, E. and Grootenhuis, P.D.J. (2002) A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. Journal ofMedicinal Chemistry, 45, 1737-1740.
54. Baroni, M., Cruciani, G., Sciabola, S., Perruccio, F. and Mason, J.S. (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. Journal of Chemical Information and Modeling, 47, 279-294.
55. http://www.biobyte.com.
56. http://www.syrres.com/esc/kowwin.htm.
57. http://www.scivision.com.
58. www.acdlabs.com.
59. Eros, D., Kovesdi, I., Orfi, L., Takacs-Novak, K., Acsady, G. and Keri, G. (2002) Reliability of logP predictions based on calculated molecular descriptors: a critical review. Current Medicinal Chemistry, 9, 1819-1829.
60. Machatha, S.G. and Yalkowsky, S.H. (2005) Comparison of the octanol/water partition coefficients calculated by ClogP®, ACDlogP and KowWin® to experimentally determined values. International Journal of Pharmaceutics, 294, 185-192.
61. Norinder, U. and Osterberg, T. (2001) Theoretical calculation and prediction of drug transport processes using simple parameters and partial least squares projections to latent structures (PLS) statistics. The use of electrotopological state indices. Journal of Pharmaceutical Sciences, 90, 1076-1084.
62. Testa, B., Carrupt, P.-A., Gaillard, P., Billois, F. and Weber, P. (1996) Lipophilicity in molecular modeling. Pharmaceutical Research, 13, 335-343.
63. Broto, P., Moreau, G. and Van Dycke, C. (1984) Molecular structures: perception, autocorrelation descriptor and SAR studies. European Journal ofMedicinal Chemistry, 19, 61-70.
64. Ghose, A.K. and Crippen, G.M. (1986) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. 1. Partition coefficients as a measure of hydrophobicity. Journal of Computational Chemistry, 7, 565-577.
65. Du, Q., Liu, P.-J. and Mezey, P.G. (2005) Theoretical derivation of heuristic molecular lipophilicity potential: a quantum chemical description for molecular solvation. Journal of Chemical Information and Modeling, 45, 347-353.
66. Raevsky, O.A. (1997) Hydrogen bond estimation by means of HYBOT, in Computer-Assisted Lead Finding and Optimisation (eds H. van de Waterbeemd, B. Testa and G. Folkers), Verlag Helvetica Chimica Acta, Switzerland, pp. 367-378.
67. Raevsky, O.A., Schaper, K.-J., van de Waterbeemd, H. and McFarland, J. (1999) Hydrogen bond contribution to properties and activities ofchemicals and drugs, in Molecular Modeling and Prediction ofBioactivity (eds K. Gundertofte and K. Jorgensen), Kluwer Academic/Plenum Publishers, USA, pp. 221-228.
68. McFarland, J.W., Raevsky, O.A. and Wilkerson, W.W. (1999) Hydrogen bond acceptor and donor factors, Ca and Cd: new QSAR descriptors, in Molecular Modeling and Prediction ofBioactivity (eds K. Gundertofte and K. Jorgensen), Kluwer Academic/Plenum Publishers, USA, pp. 280-281.
69. Raevsky, O.A., Schaper, K.-J., Artursson, P. and McFarland, J.W. (2002) A novel approach for prediction of intestinal absorption ofdrugs in humans based on hydrogen bond descriptors and structural similarity. Quantitative Structure-Activity Relationships, 20, 402-413.
70. Abraham, M.H. and Le, J. (1999) The correlation and prediction ofthe solubility of compounds in water using an amended solvation energy relationship. Journal of Pharmaceutical Sciences, 88, 868-880.
71. Abraham, M.H., Ibrahim, A., Zhao, Y. and Acree, W.E., Jr. (2006) A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data. Journal of Pharmaceutical Sciences, 95, 2091-2100.
72. Abraham, M.H. and Martins, F. (2004) Human skin permeation and partition: general linear free-energy relationship analyses. Journal of Pharmaceutical Sciences, 93, 1508-1523.
73. Palm, K., Stenberg, P., Luthman, K. and Artursson, P. (1997) Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharmaceutical Research, 14, 568-571.
74. Clark, D.E. (1999) Rapid calculation of polar surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. Journal of Pharmaceutical Sciences, 88, 807-814.
75. Ertl, P., Rohde, B. and Selzer, P. (2000) Fast calculation ofmolecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. Journal ofMedicinal Chemistry, 43, 3714-3717.
76. van de Waterbeemd, H. and Kansy, M. (1992) Hydrogen-bonding capacity and brain penetration. Chimia, 46, 299-303.
77. Clark, D.E. and Pickett, S.D. (2000) Computational methods for the prediction oPdrug-likeness'. Drug Discovery Today, 5, 49-58.
78. Clark, D.E. (2001) Predictionofintestinal absorption and blood-brain barrier penetration by computational methods. Combinatorial Chemistry & High Throughput Screening, 4, 477-496.
79. Sherbukhin, V.V. (2002) Personal communication.
80. Fox, T. and Kriegl, J.M. (2006) Machine learning techniques for in silico modeling of drug metabolism. Current Topics in Medicinal Chemistry, 6, 1579-1591.
81. Livingstone, D. (1995) Data Analysis for Chemists Applications to QSAR and Chemical Product Design, Oxford University Press, United Kingdom.
82. Lombardo, F., Gifford, E. and Shalaeva, M.Y. (2003) In silico ADME prediction: data, models, facts and myths. Mini Reviews in Medicinal Chemistry, 3, 861-875.
83. Wold, S., Johansson, E. and Cocchi, M. (1993) PLS partial least-squares projections to latent structures, in 3D QSAR in Drug Design (ed. H. Kubinyi), ESCOM Science Publishers B.V., The Netherlands, pp. 523-550.
84. Cramer, R.D., Patterson, D.E. and Bunce, J.D. (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, 110, 5959-5967.
85. Wold, S. (1979) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20, 379-405.
86. Rojas, R. (1996) Neural Networks A Systematic Introduction, Springer-Verlag, Germany.
87. Guha, R., Stanton, D.T. and Jurs, P.C. (2005) Interpretingcomputational neural network quantitative structure-activity relationship models: a detailed interpretation of the weights and biases. Journal of Chemical Information and Modeling, 45, 1109-1121.
88. Ghuloum, A.M., Sage, C.R. and Jain, A.N. (2000) Molecular hashkeys: a novel method for molecular characterisation and its application for predicting important pharmaceutical properties of molecules. Journal ofMedicinal Chemistry, 42, 1739-1748.
89. Bruneau, P. (2001) Search for predictive generic model ofaqueous solubilityusing Bayesian neural nets. Journal of Chemical Information and Computer Sciences, 41, 1605-1616.
90. Gola, J., Obrezanova, O., Champness, E. and Segall, M. (2006) ADMET property prediction: the state ofthe art and current challenges. QSAR & Combinatorial Science, 25, 1172-1180.
91. Vapnik, V.N. (1995) The Nature of Statistical Learning Theory, Springer, USA.
92. Hearst, M.A., Scholkopf, B., Dumais, S., Osuna, E. and Platt, J. (1998) Trends and controversies: support vector machines. IEEE Intelligent Systems, 13, 18-28.
93. Czerminski, R., Yasri, A. and Hartsough, D. (2001) Use of support vector machine in pattern classification: applicationto QSAR studies. Quantitative Structure-Activity Relationships, 20, 227-240.
94. Trotter, M.W.B. and Holden, S.B. (2003) Support vector machines for ADME property classification. QSAR & Combinatorial Science, 22, 533-548.
95. Warmuth, M.K., Liao, J., Roatsch, G., Mathieson, M., Putta, S. and Lemmen, C. (2003) Active learning with support vector machines in the drug discovery process. Journal of Chemical Information and Computer Sciences, 43, 667-673.
96. Barrett, S.J. and Langdon, W.B. (2006) Advances in the application of machine learning techniques in drug discovery, design and development, in Applications of Soft Computing: Recent Trends (Advances in Soft Computing) (eds A. Tiwari, J. Knowles, E. Avineri, K. Dahaland R. Roy), Springer, USA.
97. Norinder, U. (2003) Support vector machine models in drug design applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing, 55, 337-346.
98. De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D.L. (2000) Tutorial. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50, 1-18.
99. Shen, M.,Xiao, Y., Golbraikh, A., Gombar, V.K. andTropsha, A. (2003) Development and validation of k-Nearest-Neighbor QSPR models of metabolic, stability of drug candidates. Journal of Medicinal Chemistry, 46, 3013-3020.
100. Li, Y., Jiang, J.-H., Chen, Z.-P., Xu, C.-J. and Yu, R.-Q. (1999) Robust linear discriminant analysis for chemical pattern recognition. Journal of Chemometrics, 13, 3-13.
101. Hawkins, D.M. and Kass, G.V. (1982) Automatic interaction detection, in Topics in Applied Multivariate Analysis (ed. D.M. Hawkins), Cambridge University Press, United Kingdom.
102. Breimann, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984) Classification and Regression Trees, Wadsworth, New York.
103. Norinder, U., Liden, P. and Bostrom, H. (2006) Discrimination between modes of toxic action of phenols using rule based methods. MolecularDiversity, 10,207-212.
104. Quinlan, J.R. (1992) C45 Programs for Machine Learning, Morgan Kaufmann Publishers, USA.
105. Niculescu, S.P. (2003) Artificial neural networks and genetic algorithms in QSAR. Journal ofMolecular Structure (THEOCHEM) , 622, 71-83.
106. Johansson, U., Knig, R. and Niklasson, L. (2003) Rule extraction from trained neural networks using genetic programming. 13th International Conference on Artificial Neural Networks, Istanbul, Turkey, supplementary proceedings, pp. 13-16.
107. Johansson, U., Sönströd, C., Koonig, R. and Niklasson, L. (2003) Neural networks and rule extraction for prediction and explanation in the marketing domain. The International Joint Conference on Neural Networks, IEEE Press, USA, Portland, OR, pp. 2866-2871.
108. Johansson, U., KPnig, R. and Niklasson, L. (2004) The truth is in there rule extraction from opaque models using genetic programming. 17th Florida Artificial Intelligence Research Symposium (FLAIRS) 04, AAAI Press, USA, Miami, FL, pp. 658-662.
109. Johansson, U. (2007) Obtaining accurate and comprehensible data mining models, PhD thesis, Institute of Technology, Linkooping University.
110. Hewitt, M., Cronin, M.T.D., Madden, J.C., Rowe, P.H., Johnson, C., Obi, A. and Enoch, S.J. (2007) Consensus QSAR models: do the benefits outweigh the complexity? Journal of Chemical Information and Modeling, 47,1460-1468.
111. http://www.salfordsystems.com.
112. http://www.compumine.com.
113. Melville, P. and Mooney, R.J. (2005) Creating diversity in ensembles using artificial data. Journal ofInformation Fusion (Special Issue on Diversity in Multiple Classifier Systems), 6, 99-111.
114. O'Brien, S.E. and de Groot, M.J. (2005) Greater than the sum of its parts: combining models for useful ADMET prediction. Journal ofMedicinal Chemistry, 48, 1287-1291.
115. 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, 365-377.
116. Manallack, D.T., Tehan, B.G., Gancia, E., Hudson, B.D., Ford, M.G., Livingstone, D.J., Whitley, D.C. and Pitt, W.R. (2003) A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. Journal of Chemical Information and Computer Sciences, 43, 674-679.
117. 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, 1971-1978.
118. Agrafiotis, D.K., Cedeno, 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, 903-911.
119. van Rhee, A.M. (2003) Use of recursion forests in the sequential screening process: consensus selection by multiple recursion trees. Journal of Chemical Information and Computer Sciences, 43, 941-948.
120. Netzeva, T.I., Worth, A.P., Aldenberg, T., Benigni, R., Cronin, M.T.D., Gramatica, P., Jaworska, J.S., Kahn, S., Klopman, G., Marchant, C.A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G.Y., Perkins, R., Roberts, D.W., Schultz, T.W., Stanton, D.T., van de Sandt, Tong, W., Veith, G. and Yang, C. (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM workshop 521. Alternatives to Laboratory Animals, 33, 155-173.
121. Zhang, H., Ando, H.Y., Chen, L. and Lee, P.H. (2007) On-the-fly selection of a training set for aqueous solubility prediction. Molecular Pharmacology, 4, 489-497.
122. 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, 1836-1847.
123. Helma, C. (2006) Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity. Molecular Diversity, 10, 147-158.
124. Zhang, S., Golbraikh,A., Oloff, S.,Kohn, H. and Tropsha, A. (2006) A novel automated lazylearningQSAR (ALL-QSAR) approach: method development, applications, and virtual screening ofchemical databases usingvalidatedALL-QSARmodels.Journal ofChemical Information and Modeling, 46, 1984-1995.
125. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikstrom, C. and Wold, S. (2006) Multi and Megavariate Data Analysis Part I: Basic Principles and Applications, 2nd edn, Umetrics.
126. Linusson, A., Gottfries, J., Olsson, T., Ornskov, E., Folestad, S., Norden, B. and Wold, S. (2001) Statistical molecular design, parallel synthesis, and biological evaluation of a library of thrombin inhibitors. Journal ofMedicinal Chemistry, 44, 3424-3439.
127. Box, G.E.P., Hunter, W.G. and Hunter, J.S. (1978) Statistics for Experimenters, John Wiley & Sons, Inc., USA.
128. Lundstedt, T., Seifert, E., Abramo, L., Thelin, B., Nystroom, A., Pettersen, J. and Bergman, R. (1998) Experimental design and optimisation. Chemometrics and Intelligent Laboratory Systems, 42, 3-40.
129. Gottfries, J. andWold, S. (2004) D-optimal onion designs in statistical molecular design. Chemometrics and Intelligent Laboratory Systems, 73, 37-46.
130. Marengo, E. and Todeschini, R. (1992) A new algorithm for optimal distance-based experimental design. Chemometrics and Intelligent Laboratory Systems, 16, 37-44.
131. Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H. and Tropsha, A. (2003) Rational selection oftraining and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17, 241-253.
132. Clark, M. and Cramer, R.D., III (1993) The probability of chance correlation using partial least squares (PLS). Quantitative Structure-Activity Relationships, 12, 137-145.
133. Golbraikh, A. and Tropsha, A. (2002) Beware of q2! Journal of Molecular Graphics & Modelling, 20, 269-276.
134. van der Voet, H. (1994) Comparing the predictive accuracy of models using a simple randomization test. Chemometrics and Intelligent Laboratory Systems, 25, 313-323.
135. 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 & Combinatorial Science, 22, 69-77.