QSAR in the pharmaceutical research setting: QSAR models for broad, large problems

Current Topics in Medicinal Chemistry

Volumn 10, Issue 6, 2010, Pages 619-637

  Sprous, D G ^a   Palmer, R K ^a   Swanson, J T ^b   Lawless, M ^b  

^a Redpoint Bio Inc   (United States)

^b ChemModeling   (United States)

Author keywords

Focus library; QSAR; QSAR 2D descriptors; Qsar validation

Indexed keywords

BETA 2 ADRENERGIC RECEPTOR; DRUG; G PROTEIN COUPLED RECEPTOR; MITOGEN ACTIVATED PROTEIN KINASE P38; POTASSIUM CHANNEL HERG; PROTEIN KINASE; RHODOPSIN;

COMPUTER PROGRAM; CORRELATION COEFFICIENT; DRUG ABSORPTION; DRUG BIOAVAILABILITY; DRUG RESEARCH; HUMAN; INTESTINE ABSORPTION; LIGAND BINDING; METHODOLOGY; MOLECULAR MODEL; NONHUMAN; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; VALIDATION PROCESS; X RAY CRYSTALLOGRAPHY;

DRUG DISCOVERY; HUMANS; LIGANDS; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, G-PROTEIN-COUPLED;

EID: 77950906295     PISSN: 15680266     EISSN: None     Source Type: Journal    
DOI: 10.2174/156802610791111506     Document Type: Review

References (163)

1. Leach, A. Molecular Modelling. Pearson Education Limited. Essex England, 1996.
2. Clark, R.D. Prospective ligand and target based 3D QSAR: State of the Art 2008. Curr. Top. Med. Chem., 2009, 9, 791-810.
3. Clark, R.D.; Sprous, D.G.; Leonard, J.M. Progressive Scrambling. Holtje, H.-D. Sippl, W. Eds., Rational Approaches to Drug Design. Prous Science. S.A. Barcelona, Spain 2001, pp. 475-486.
4. Golbraik, A.; Tropsha, A. Beware of q2. J. Mol. Graph. Model., 2002, 20, 269-276.
5. Golbraik, A.; Tropsha, A. Predicted QSAR modeling. J. Mol. Graph. Model., 2002, 20, 269-276.
6. Sheridan, R.P.; Feuston, B.P.; Maiorov, V.N.; Kearsley, S.K. Similarity to molecules in training set is a good descriminator for prediction accuracy in QSAR. J. Chem. Inf. Comput. Sci., 2004, 44, 1912-1928.
7. Sprous, D.G. Fingerprint-based clustering applied to define a QSAR model use radius. J. Mol. Graph. Mod., 2008, 27, 225-232.
8. Jorgensen, W.L. QSAR/QSPR and proprietary data. J. Chem. Inf. Model., 2006, 46, 3
9. Advanced Chemistry Development Inc. Toronto, Ontario CA.
10. [BioByte Corp. Claremont, CA USA, 2009.
11. CompuDrug International, Inc. Sedona AZ USA, 2009.
12. Simulations Plus, Inc. Lancaster CA USA, 2009.
13. Accelrys, Inc. San Diego CA USA, 2009.
14. Lhasa Limited, Leeds UK, 2009.
15. Leadscope Inc. Columbus, OH USA, 2009.
16. Tropsha, A. Reynolds, C.H. Designing focused libraries for drug discovery. J. Mol. Graph. Model., 2002, 20, 427-428.
17. Olah, M.M.; Bologna, C.G.; Oprea, T.I. Strategies for compound selection. Curr. Drug Discov. Tech., 2004, 1, 211-220.
18. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 1997, 23,3-25.
19. Oprea, T.I. Davis, A.M.; Teague, S.J.; Keeson, P.D. Is there a difference between leads and drugs. J. Chem. Inf. Comput. Sci., 2001, 41, 1308-1319.
20. Hahn, M.M.; Oprea, T.I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol., 2004, 8, 255-263.
21. Oprea, T.I.; Gottfries, J. Toward minimalist modeling of oral drug absorption. J. Mol. Graph. Model., 1999, 17, 261-274. (Pubitemid 32250313)
22. Sadowski, J.; Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem., 1998, 41, 3325-3329. (Pubitemid 28406539)
23. Prien, O. Target-family-oriented focused libraries for kinases - conceptual design aspects and commercial availability. Chem. Biol. Chem., 2005, 6,500-505. (Pubitemid 40868137)
24. Jimonet, P.; Jager, R. Strategies for designing GPCR-focus libraries and screening sets. Curr. Opin. Drug Discov. Dev., 2004, 7,325-333. (Pubitemid 44111942)
25. LaBute, P. Binary QSAR: A new method for the determination of QSARs. Pac. Sym. Biocomp., 1999, 4, 444-445.
26. Aronov, McClain, Moody & Murcko Kinase-likeness and kinase-privileged fragments. J. Med. Chem., 2008, 51, 1214-1222.
27. SYBYL Tripos Inc. St. Louis MO. USA. 2008.
28. [MOE. Chemical Computing Group. Montreal, Canada. 2008.
29. Tropsha, A.; Golbraikh A. Predictive QSAR modeling workflow. Cur. Pharm. Des., 2007, 13, 3494-3504.
30. Dragon. Talete SRL. Milan, Italy. 2009.
31. Molconn-z. Edusoft LLC. Ashland, VA. USA 2009.
32. ADAPT. Pennsylvania State University. College Station. PA USA, 2009.
33. Codessa. Semichem Inc. Shawnee Mission, KS. USA. 2009.
34. Stanton, D.; Jurs, P. Development and use of charged partial surface-area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal. Chem., 1990, 62, 2323-2329.
35. Cruciani, G.; Crivori, P.; Carrupt, P.-A. ; Testa, B. Molecular fields in quantitative structure permeation relationships: the VolSurf approach. J. Mol. Struct., THEOCHEM 2000, 503,17-30.
36. Cherkasov, A. Inductive descriptors. Curr. Comput. Aided Drug. Des. 2005, 1, 21-42.
37. Almond 2009. Molecular Discovery. Pinner UK.
38. Cramer, R.D. III; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis. J. Am. Chem. Soc., 1988, 110, 5959-5967.
39. Klebe, G.; Abraham, U. Mietzner, T. Molecular similarity indexes in a comparative-analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37, 4130-4146.
40. Wildman, S.A.; Crippen, G.M. Prediction of physiochemical parameters by atomic contributions. J. Chem. Inf. Comput. Sci., 1999, 39, 868-873.
41. Dixon, S.L.; Mertz, K.M. Jr. One-Dimensional molecular representations and similarity calculations: methodologies and validation. J. Med. Chem., 2001, 44, 3795-3809.
42. Kier LB, Hall LH. Molecular Connectivity in Structure-Activity Analysis. New York: Wiley, 1986.
43. Kier LB. Inclusion of symmetry as a shape attribute in kappa-index analysis. Quant. Struct. Activ. Relat., 1987, 6, 8-12. (Pubitemid 17072620)
44. Kier LB, Hall LH. Molecular Structure Description. The Electrotopological State, Academic Press New York: 1999.
45. Xing, Li; Glen, R.C.; Clark, R. D. Predictiong pKa by molecular tree structured fingerprints and PLS. J. Chem. Comput. Sci., 2003, 43, 870-879.
46. Rodgers, S.; Glen, R.C.; Bender, A. Characterizing bitterness. J. Chem. Inf. Model., 2006, 46, 569-576.
47. Ertle, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its approach to the prediction of drug transport properties. J. Med. Chem., 2000, 43, 3714-3717.
48. Zheng W.; Tropsha A. Novel variable selection quantitative structure property relationship approach based on the k-nearest neighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40, 185-194.
49. Sprous, D.G.; Salemme, F.R. A comparison of the chemical properties of drugs and GRAS compounds. Food. Chem. Toxicol., 2007, 45, 1419-1427.
50. Van Drie, J. Pharmacophore Discovery: A Critical Review. In: Computational Medicinal Chemistry for Drug Discovery, Bultinck, P.; Winter, H.; Langenaeker, W.; Tollenare, J. Eds. Marcel Dekker: New York, 2004; pp. 437-461.
51. Horvath, D. Descriptor based modeling of ligand binding. Presentation at EuroCUP III Toledo Spain, 2009.
52. Schuurmann, G.; Ebert, R.U.; Chen, J.; Wang, B.; Kuhne, R. External validation and prediction employing the predictive squared correlation coefficient s test set activity mean vs training set activity mean. J. Chem. Inf. Model., 2008, 48, 2140-2145.
53. Wu, W.; Walczak, B.; Massart, D.L.; Heuerding, S.; Erni, F.; Last, I.R.; Prebble, K.A. Representative subset selection in modified iterative predictor weighting (mIPW)-PLS models for parsimonious multivariate calibration. Chemometr. Intell. Lab. Syst., 1996, 33, 35-46.
54. Tetko, I.V., Kovalishyn, V.V. and Livingstone D.J. Volume learning algorithm artificialneural networks for 3D QSAR studies. J. Med. Chem., 2001, 44, 2411-2420.
55. Yasri, A.; Hartsough, D. J. Towards an optimal procedure for variable selection andQSAR model building. J. Chem. Inf. Comput. Sci., 2001, 41, 1218-1227. (Pubitemid 33663886)
56. Huuskonen, J. QSAR modeling with the electrotopological state: TIBO derivatives. J.Chem. Inf. Comput. Sci., 2001, 41, 425-429. (Pubitemid 32408660)
57. Maggiora, M. On outliers and activity cliffs. J. Chem. Inf. Model. 2006, 46,1535
58. Guha, R.; Van Drie, J.H. Assessing how well a modeling protocol captures and structure-activity landscape. J. Chem. Inf. Model., 2008, 48, 1716-1728.
59. Sisay, M.T.; Peltason, L.; Bajorath, J. Structural interpretation of activity cliffs revealed. J. Chem. Inf. Model., 2009, 49, 2179-2189
60. Pearlman, D.; Charifson, P. Are free energy calculations useful in practice? J. Med. Chem., 2001, 44, 3417-3423.
61. Li, H.; Yap, C.W.; Ung, C.Y.; Xue, Y.; Cao, Z.W.; Chen, Y.Z. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. J. Chem. Inf. Model., 2005, 45, 1376-1384.
62. Horvath, D.; Bonachera, F.; Solov'ev, V.; Gaudin, C.; Varnek, A. Stochastic versus Stepwise strategies for quantitative structure-activity realationship generation- how much effort may the mining for successful QSAR models take? J. Chem. Inf. Model., 2007, 47, 927-939.
63. Dutta, D.; Guha, R.; Wild, D.; Chen, T. Ensemble feature selection: consistent descriptor subsets for multiple QSAR models. J. Chem. Inf. Model., 2007, 47, 989-997. (Pubitemid 46973714)
64. Rücker, C.; Rücker, G.; Meringer, M. y-randomization and ist variants in QSPR/QSAR. J. Chem. Inf. Model., 2007, 47, 2345-2357. (Pubitemid 350284525)
65. Livingstone, D.J.; Salt, D.W. Judging the significance of multiple linear regression models. J. Med. Chem., 2005, 48, 661-663. (Pubitemid 40209088)
66. Hammett, L.P. The effect of structure upon the reactions of organic compounds. J. Am. Chem. Soc., 1937, 59, 93-103.
67. Taft, R.W. Linear free energy relationships from the rates of esterification and hydrolysis of aliphatic and ortho-substituted benzoate esters. J. Am. Chem. Soc., 1952, 74, 2729-2732.
68. Taft, R.W. Polar and steric substituent constants for aliphatic and obenzoate groups from rates of esterification and hydrolysis of esters. J. Am. Chem. Soc., 1952, 74, 3120-3128.
69. Taft, R.W. The separation of relative free energies of activation to three basic contributing factors and the relationship of these to structure. J. Am. Chem. Soc., 1952, 75, 4534-4537.
70. Tsantili-Kakoulidou, A.; Panderi, I.; Csizmadia, F.; Darvas F. Prediction of distribution coefficients from structure. J. Pharm. Sci., 1997, 86,1173-1179.
71. Cherkasov, A; Sprous, D.G.; Chen, R. Three dimensional correlation analysis-a novel approach to the quantification of substituent effect. J. Phys. Chem., 2003, 107, 9695-9704.
72. Lee, A.; Crippen, G.M. Prediction pKA. J. Chem. Inf. Model., 2009, 49, 2013-2033.
73. Mannhold, R.; Poda, G.I.; Ostermann, C.; Tetko, I.V. Calculation of molecular lipophilicity: statet-of-the-art and comparison of logP methods on more than 96,000 compounds. J. Pharm. Sci., 2009, 98, 861-893.
74. Leo, A.; Jow, P.Y.C.; Silipo, C.; Hansch, C. Calculation of hydrophobic constant (logP) from sigma and f constants. J. Med. Chem., 1975, 18, 865-869.
75. Viswanadhan, V. N.; Ghose, A. K.; Revankar, G. R.; Robins, R.K. Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. J. Chem. Inf. Comput. Sci., 1989, 29,163-172.
76. Wang, J.; Hou, T. Recent advances in in silico adme modeling. Annu. Rep. Comput. Chem., 2009, 5,101-127.
77. Langham, J.J.; Jain, A.J. Accurate and interpretable computational modeling of chemical mutagenicity. J. Chem. Inf. Soc., 2008, 48, 1833-1839.
78. Muster, W.; Breidenbach, A.; Fischer, H.; Kirchner, S.; Muller, L.; Pahler, A. Computational toxicology in drug development. Drug Discov. Today, 2008, 13, 303-310.
79. Walgren, J.L.; Mitchel, M.D.; Thompson, D.C. Role of metabolism in drug-induced idiosyncratic hepatoxicity. Crit. Rev. Toxicol., 2005, 35, 325-361.
80. Vedani, A.; Dobler, M.; Markus, A. From crystal structures and their analysis to the in silicoprediction of toxic phenomena. Helv. Chim. Acta, 2003, 86, 1554-1568.
81. Caldwell, G.W.; Yan, Z. Screening for reactive intermediates and toxicity assessment in drug discovery. Curr. Opin. Drug Discov. Dev., 2006, 9, 47-60. (Pubitemid 43099075)
82. Haverkamp, W.; Breithardt, G.; Camm, A.J.; Janse, M.J.; Rosen, M.R.; Antzelevitch; C.; Escande D.; Franz, M.; Malik, M.; Moss, A.; Shah, R. The potential for QT prolongation and proarrhythmia by non-anti-arrhythmic drugs. Cardiovasc. Res., 2000, 47, 219-233.
83. De Ponti, F.; Poluzzi, E.; Montanaro N.QT-interval prolongation by non-cardiac drugs. Eur. J. Clin. Pharmacol., 2000, 56,1-18.
84. Redfern, W.S.; Carlsson, L..; Davis, A.S.; Lynch, W.G.; MacKenzie, I.; Palethorpe, S.; Siegl, P.K.S.; Strang, I.; Sullivan, A.T.; Wallis, R.;Camm, A.J.; Hammond, T.G. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs. Cardiovasc. Res., 2003, 58, 32-45.
85. De Ponti, F.; Poluzzi, R.; Cavalli, A.; Recanatini, M.; Montanaro, N. Safety of nonantiarrhythmic drugs that prolong the QT interval. Drug Saf., 2002, 25, 263-286.
86. Neher, S.; Sakmann, B. The patch clamp technique. Sci. Am., 1992, 266, 44-51.
87. Wood, C.; Williams, C.; Waldron, G.J. Patch clamping by numbers. Drug Discov. Today, 2004, 9, 434-441.
88. Ekins, S.; Crumb, W.J.; Dustan-Sarazan, R.; Wikel, J.H.; Wrighton, S.A. Three dimensional quantitative structure activity relationship for inhibition of human ether-a-go-gorelated gene potassium channel. J. Pharm. Exp. Ther., 2002, 301, 427-434.
89. Johnson, S.R.; Yue, H.; Conder, M.L.; Shi, H.; Doweyko, A.M.; Lloyd, J.; Levesque, P. Estimation of hERG inhibition of drug candidates using multivariant property and pharmacophore SAR. Bioorg. Med. Chem., 2007, 15, 6182-6192. (Pubitemid 47088957)
90. Diller, D.J.; Hobbs, D.W. Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation. J. Comput. Aided Mol. Des., 2007, 21, 379-393.
91. Song, M.; Clark, M. Development and evaluation of an in-silico model for hERG binding. J. Chem. Inf. Model., 2006, 46, 392-400.
92. Thai, K.M.; Eckert, G.F. A binary QSAR model for classification of hERG potassium channel blockers. Bioorg. Med. Chem., 2008, 16, 4107-4119.
93. Polak, S.; Wisniowska, B.; Brandys, J. Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs cardiotoxic properties. Appl. Toxicol., 2009, 29, 183-206.
94. Seidler, J.; McGovern, S.L.; Doman, T.N.; Schoichet, B.K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem., 2003, 46, 4477-4486
95. Ajay, Walter, W.P.; Murcko, M.A. Can we learn to distinguish between "drug-like" and "nondrug-like" molecules. J. Med. Chem,. 1998, 41, 3314-3324.
96. Comprehensive Medicinal Chemistry Release 94.1. MDL Information Systems. San Leandro CA 94577.
97. Available Chemicals Directory. MDL Information Systems. San Leandro CA 94577 2009.
98. Hall, L.H.; Kier, L.B. The molecular connectivity chi indexes and kappa shape indexes in structure property modeling. In: Reviews in Computational Chemistry, Lipkowitz, K.B.; Boyd, D.B.; Eds. John Wiley: New York, 1991, vol. 2, pp. 367-422.
99. World Drug Index. World Drug Index. Version 2/96. 1996. Derwent Information.
100. van de Waterbeemd, H.; Camenisch, G.; Folker, G.; Raevsky, O.A. Estimation of CACO-2 cell permeability using calculated molecular descriptors. Quant. Struct. Act. Relat. 1996, 15, 480-490.
101. Winiwater, S.; Bonham, N.M.; Ax, F.; Hallber, A.; Lennernas, H.; Karlen, A. Correlation of human jejenal permeability (in vivo) of drugs with experimental and theoretical derived parameters. J. Med. Chem., 1998, 41, 4939-4949.
102. Palm, K.; Stenberg, P; Luthman, K.; Artursson, P. Polar molecular surface properties that predict the intestinal absorption of drugs. Pharm. Res., 1997, 14, 3-25. (Pubitemid 27220272)
103. Palm, K.; Luthman, K.; Ungell, A.L.; Strandlund, G.; Beigi, F.; Lundahl, P.; Artursson, P. Evaluation of dynamic polar molecular surface area as a predictor of drug absorption. J. Med. Chem., 1998, 41, 5382-5392.
104. Clark, D.E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena 2. Prediction of blood-brain barrier penetration. J. Pharm. Sci., 1999, 88, 815-821. (Pubitemid 29398551)
105. Kelder, J.; Grootenhuis, P.D.J.; Bayada, D.M.; Delbressine, L.P.C.; Ploemen, J.-P. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm. Res., 1999, 16, 1514-1519.
106. Clark, D.E.; Pickett, S.D. Computational methods for the prediction of "drug-likeness". Drug. Discov. Today, 2000, 5, 49-58.
107. Baurin, N.; Baker, R.; Richardson, C.; Chen, I., Foloppe, N.; Potter, A.; Jordan, A.; Roughley, S.; Parratt, M.; Greaney, P.; Morley, D.; Hubbard, R.E. Drug like annotation and duplicate analysis of a 23 supplier chemical database totalling 2.7M compounds. J. Chem. Inf. Comp. Sci., 2004, 44, 643-651.
108. Veber, D.F.; Johnson, S.R.; Cheng, H.-Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties which influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45, 2615-2623.
109. PhysProp: Physical Properties Database. Syracuse Research Corporation. Syracuse USA 2008.
110. Artursson, P.P.K.; Luthman, K. CACO-2 monolayers in experimental and theoretical predictions of drug discovery. Adv. Drug. Deliv. Rev., 1996, 22, 67-84. (Pubitemid 26423598)
111. Egan, W.J.; Lauri, G. Prediction of intestinal permeability. Adv. Drug Deliv. Rev., 2002, 54, 273-289.
112. Bergstrom, C.A.S.; Stratford, M.; Lazorova, L.; Avdeef, A.; Luthman, K.; Artusson, P. Current absorption classification of oral drugs based on molecular surface properties. J. Med. Chem., 2003, 46, 558-570.
113. Petitjean, M. Application of the radius-diameter diagram to the classification of topological and geometric shapes of chemical compounds. J. Chem. Inf. Comput. Sci., 1992, 32, 331-337.
114. Sternberg, P.; Norinder, U.; Luthman, K.; Artursson, P. Experimental and computational screening models for the prediction of intestinal drug absorption. J. Med. Chem., 2001, 44, 1927-1937. (Pubitemid 32884517)
115. Zenet, L.Z.; Kroetz, D.L.; Sheiner, L.B. Pharmacokinetics: the dynamics of drug absorption, distribution and elimination. In: The Pharmacological Basis of Therapeutics, Hardman, J.G.; Limbird, L.E., Eds. McGraw-Hill, Health Professions Division. New York, USA, 1996.
116. Potts, R.O.; Guy, R.H. A predictive algorithm for skin permeability. Pharm. Res., 1995, 12, 1628-1633.
117. Magnusson, B.M.; Anissimov, Y.G.; Cross, S.E.; Roberts, M.S. Molecular size as the main determinant of solute maximum flux across the skin, J. Invest. Dermatol., 2004, 122, 993-999.
118. Mistretta, C.M. Permeability of the tongue epithelium and its relation to taste. Am. J. Physiol., 1971, 220,1162-1167.
119. Zhang, H.; Zhang, L.; Streisand, J.B. Oral mucosal drug delivery. Clin. Pharmacokinetics, 2002, 41, 661-680.
120. Hann, M.H.; Oprea, T.I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol., 2004, 8, 255-263.
121. Carr, R.; Jhoti, H. Structure-based screening of low affinity compounds. Drug Discov. Today, 2002, 7, 522-527. (Pubitemid 34450940)
122. Erlanson, D.A.; Braisted, A.C.; Raphael, D.R.; Randal, M.; Stroud, R.M.; Gordon, E.M.; Wells, J.A. Site directed ligand discovery. Proc. Natl. Acad. Sci. USA, 2000, 97, 9367-9372
123. Vetter, D. 2002. Chemical microarrays, fragment diversity, label free imaging by plasmon resonance-a chemical genomics approach. J. Cell Biochem., 2002, 39, 79-84. (Pubitemid 36176559)
124. Congreve, M., Carr, Robin; Murray, C.; Jhoti, H. A rule-of-three for fragment based lead discovery. Drug Discov. Today, 2003, 8, 876-877.
125. Burdock, G.A. The GRAS process. Food Tech., 2003, 57, 17.
126. Eid, S.R.; Cortright, D.N. Transient receptor potential channels on sensory nerves. Handb Exp. Pharmacol., 2009, 194, 261-281.
127. Walters, D.E.; Orthoefer, F.T.; DuBois, G.E. Sweeteners: Discovery Molecular Design and Chemoreception. ACS Symposium Series, 1990, p. 450.
128. Prestwick Chemical Library. Prestwick Chemical Inc., Illkirch France 2005.
129. Flavor-Base Leffingwell and Associates, Canton GA USA 2004.
130. Jubilant Organosys. Noida, Uttar Pradesh, India 2005.
131. Eidogen-Sertanty. Oceanside CA USA 2005.
132. GVK BIO. Hyperabad India 2004.
133. Aureus Pharma. Paris France 2008.
134. Manning, G.; Whyte, D.B.; Martinez, R.; Hunter, T. The protein kinase complement of the human genome. Science, 2002, 298, 1912-1934. (Pubitemid 35425239)
135. Manallack, D.T.; Pitt, W.R.; Gancia, W.; Montana, J.G.; Livingstone, D.T.; Ford, M.G.; Whitley D.C. Selecting screening candidates for kinase and GPCR targets using neural networks. J. Chem. Inf. Comput. Sci., 2002, 42, 1256-1262.
136. Ford, D.C.; Pitt, W.R.; Whitley, D.C. Selecting compounds for focused screening using linear discrimination analysis and artificial neural networks. J. Mol. Graph. Mod., 2004, 22, 467-472
137. MDDR. MDL Information Systems. San Leandro CA. 2008.
138. Burden, F.R. Molecular identification number for substructure searches. J. Chem. Inf. Comput. Sci., 1989, 29, 225-227.
139. Pearlman; R.S.; Smith; K.M. Metric evaluation and the receptor-relecant subspace concept. J. Chem. Inf. Comput. Sci., 1999, 39, 28-35.
140. Pearlman, R.S.; Smith, K.M. Novel software tools for molecular diversity. Perspect. Drug Discov. Des., 1998, 9,339-353.
141. Whitley, D.C.; Ford, M.G.; Linvingstone, D.J. unsupervised forward selection. J. Chem. Inf. Comput. Sci., 2000, 40,1160-1168.
142. Dillon, W.R.; Goldstein, M. Multivariate Analysis. Wiley. New York, 1984.
143. Xia, X.; Maliski, E.G.; Gallant, P.; Rogers, D. Classification of kinase inhibitors using a Bayesian model. J. Med. Chem., 2004, 47, 4463-4470. (Pubitemid 39096834)
144. Briem, H.; Gunther, J. Classifying "kinase inhibitor-likeness" by using machinelearning methods. Chembiochem, 2005, 6, 558-566.
145. Pargellis, C.; Tong, L.; Churchill, L.; Cirillo, P.F.; Gilmore, T.; Graham, A.G.; Grob, P.M.; Hickey, R.R.; Moss, N.; Pav, S.; Regan, J. Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site. Nat. Struct. Biol., 2002, 9, 268-272. (Pubitemid 34289898)
146. Nagar, B.; Bornmannm W.G.; Pellicena P.; Schindler, T.; Veach, D.R.; Miller, W.T.; Clarkson, B.; Kuriyan, J. Structure of the kinase domain of c-ABL in complex with the small molecule inhibitors PD173955 and imatinib (STI-571). J. Cancer Res., 2002, 62, 4236-4243. (Pubitemid 34827278)
147. Sprous, D.G.; Zhang, J.; Zhang, L.; Wang, Z.; Tepper, M.A. Kinase inhibitor recognition by use of a multivariable QSAR model. J. Mol. Graph Model., 2006, 24, 278-295
148. McMahon, G.; Sun, L.; Liang, C.; C. Tang. Protein kinase inhibitors. Curr. Opin. Drug Discov. Dev., 1998, 1, 131-146.
149. Bridges, A. Chemical inhibitors of protein kinases Chem. Rev., 2001, 101, 2541-2571 (Pubitemid 35373034)
150. Adams, J.L.; Lee, D. Recent progress towards the identification of selective serine/threonine protein kinases. Curr. Opin. Drug Discov. Dev., 1999, 2, 96-109. (Pubitemid 29155607)
151. Yoshida, F.; Topliss, J.G. QSAR model for drug human oral bioavailability. J. Mol. Graph. Model., 2000, 5, 261-274.
152. Lewell, X.Q.; Judd, D.B.; Watson, S.P.; Hann, M.M. RECAP, J. Chem. Inf. Comput. Sci., 1998, 38, 511-522.
153. Deanda, F.; Stewart, E.L.; Reno, M.J.; Drewry, D.H. Kinase-targeted library design through the application of the PharmPrint methodology. J. Chem. Inf. Model., 2008, 48, 2395-2403.
154. Deanda, F.; Stewart, E.L. Application of the PharmPrint methodology to two protein kinases J. Chem. Inf. Comput. Sci., 2004, 44, 1803-2809.
155. McGregor, M.J.; Muskal, S.M. Pharmacophore fingerprinting 1. Application to QSAR and focus library design. J. Chem. Inf. Comput. Sci., 1999, 39,569-574.
156. McGregor, M.J.; Muskal, S.M. Pharmacophore fingerprinting 2. Application to primary library design J. Chem. Inf. Comput. Sci., 2000, 40,117-125.
157. Aronov, A.M.; McClain, B.; Moody, C.S.; Murcko, M.A. Kinase-likeness and kinase privileged fragments, toward virtual polypharmacology. J. Med. Chem., 2008, 51, 1214-1222.
158. Bemis, G.W.; Mercko, M.A. The properties of known drugs. J. Med. Chem., 1996, 39, 2887-2893.
159. Vassila, D.K.; Hohmann, J.G.; Zeng, H.; Li, F.; Ranchalis, J.E.; Mortrud, M.T.; Brown, A.; Rodriguez, S.S.; Weller, J.R.; Wright, A.C.; Bergmann, J.E.; Gaitanaris, G.A. The G protein coupled receptor repertoires on human and mouse. Proc. Natl. Acad. Sci. USA, 2003, 100, 4903-4908.
160. Lowrie, J.F.; Delisle, R.K.; Hobbs, D.W.; Diller, D.J. The different strategies for designing GPCR and kinase targeted libraries. Comb. Chem. HTS, 2004, 7, 495-510.
161. Horn, F.; Weare, J.; Beukers, M. W.; Horsch, S.; Bairoch, A.; Chen, W.; Edvardsen, O.; Campagne, F.; Vriend, G. GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Res., 1998, 26, 275-279.
162. Balakin, K.V.; Tkachenko, S.E.; Lang, S.A.; Okun, I; Ivanshchenko, A.A.; Savchuk, N.P. Property based design of GPCR targeted library. J. Chem. Inf. Comput. Sci., 2002, 42, 1332-1342.
163. Givehchi, A.; Schneider, G. Multispace classification for predicting GPCR ligands. Mol. Divers., 2005, 9, 371-383.