Quantitative Approaches to Structure-Activity Relationships

The Practice of Medicinal Chemistry

Volumn , Issue , 2008, Pages 491-513

  van de Waterbeemd, Han ^a   Rose, Sally ^b  

^a ASTRAZENECA   (United Kingdom)

^b Cresset BioMolecular Discovery Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84882530187     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-374194-3.00023-8     Document Type: Chapter

References (117)

1. Engel Th. Basic overview of chemometrics. J. Chem. Inf. Model. 2006, 46:2267-2277.
2. Chen W.L. Chemoinformatics: past, present and future. J. Chem. Inf. Model 2006, 46:2230-2255.
3. Stouch T.R., Kenyon J.R., Johnson S.R., Chen X.Q., Doweyko A., Li Y. In silico ADME/Tox: why models fail. J. Comput.-Aided Mol. Des. 2003, 17:83-92.
4. Martin Y.C. What works and what does not: lessons from experience in a pharmaceutical company. QSAR Comb. Sci. 2006, 25:1192-1200.
5. nd Computer-Assisted Drug Design 2007, vol. 4:43-63. Elsevier, Oxford. J.B. Taylor, D.J. Triggle (Eds.).
6. Hansch C. The physicochemical approach to drug design and discovery (QSAR). Drug Dev. Res. 1981, 1:267-309.
7. Hansch C. On the state of QSAR. Drug Inf. J. 1984, 18:115-122.
8. Craig P.N. QSAR - origins and present status: a historical perspective. Drug Inf. J. 1984, 18:123-130.
9. Kubinyi H. Methods and principles in medicinal chemistry. QSAR: Hansch Analysis and Related Approaches 1993., Vol. 1. Wiley-VCH, Weinheim.
10. Hansch C. Quantitative structure-activity relationships and the unnamed science. Acc. Chem. Res. 1993, 26:147-153.
11. Hyde R.M., Livingstone D.J. Perspectives in QSAR: computer chemistry and pattern recognition. J. Comput.-Aided Mol. Des. 1988, 2:145-155.
12. Kubinyi H. The Free-Wilson method and its relationship to the extrathermodynamic approach. Comprehensive Medicinal Chemistry, Quantitative Drug Design 1990, Vol. 4:589-643. Pergamon Press, New York. C. Hansch, P.G. Sammes, J.B. Taylor (Eds.).
13. Cramer R.D. Partial least squares (PLS): its strength and limitations. Perspect. Drug Discov. Des. 1993, 1:269-278.
14. Eriksson L., Johansson E. Multivariate design and modeling in QSAR. Chemom. Intell. Lab. Syst. 1996, 34:1-19.
15. Fleischer R., Frohberg P., Büge A., Nuhn P., Wiese M. QSAR analysis of substituted 2-phenylhydrazonoacetamides acting as inhibitors of 15-lipoxygenase. Quant. Struct.-Act. Relat. 2000, 19:162-172.
16. Gedeck P., Rohde B., Bartels C. QSAR - How good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. J. Chem. Inf. Model. 2006, 46:1924-1936.
17. Rodgers S.L., Davis A.M., van de Waterbeemd H. Time-series QSAR analysis of human plasma protein binding data. QSAR Comb. Sci. 2007, 26:511-521.
18. 2 receptor histamine antagonists. J. Med. Chem. 1988, 31:656-671.
19. van de Waterbeemd H., Kansy M. Hydrogen-bonding capacity and brain penetration. Chimia 1992, 46:299-303.
20. Lombardo F., Shalaeva M.Y., Tupper K.A., Gao F., Abraham M.H. ElogPoct: a tool for lipophilicity determination in drug discovery. J. Med. Chem. 2000, 43:2922-2928.
21. van de Waterbeemd H., Testa B. The parametrization of lipophilicity and other structural properties in drug design. Adv. Drug Res. 1987, 16:85-225.
22. El Tayar N., Testa B., Carrupt P.A. Polar intermolecular interactions encoded in partition coefficients: an indirect estimation of hydrogen-bond parameters of polyfunctional solutes. J. Phys. Chem. 1992, 96:1455-1459.
23. 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.
24. Bevan C.D., Lloyd R.S. A high-throughput screening method for the determination of aqueous drug solubility using laser nephelometry in microtiter plates. Anal. Chem. 2000, 72:1781-1787.
25. Testa B., Kier L.B. The concept of molecular structure in structure-activity relationship studies and drug design. Med. Res. Rev. 1991, 11:35-48.
26. Todeschini R., Consonni V. Methods and principles in medicinal chemistry,. Handbook of Molecular Descriptors 2000, Vol. 11. Wiley-VCH, Weinheim.
27. Gavaghan C.L., Hasselgren-Arnby C., Blomberg N., Strandlund G., Boyer S. Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J. Comput.-Aided Mol. Des. 2007, 21:189-206.
28. 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.
29. Rose V.S., Rahr E., Hudson B.D. The use of Procrustes analysis to compare different property sets for the characterization of a diverse set of compounds. Quant. Struct.-Act. Relat. 1994, 13:152-158.
30. Dudek A.Z., Arodz T., Galvez J. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Through. Scr. 2006, 9:213-228.
31. Tong W., Hong H., Xie Q., Shi L., Fang H., Perkins R. Assessing QSAR limitations - a regulatory perspective. Curr. Comput.-Aided Drug Des. 2007, 1:195-205.
32. Liu Q., Hirono S., Moriguchi I. Quantitative structure-activity relationships for calmodulin inhibitors. Chem. Pharm. Bull. 1990, 38:2184-2189.
33. Rose V.S., Wood J., MacFie H.J.H. Single class discrimination using principal component analysis (SCD-PCA). Quant. Struct.-Act. Relat. 1991, 10:359-368.
34. Manallack D.T., Livingstone D.J. Neural networks in drug discovery: have they lived up to their promise?. Eur. J. Med. Chem. 1999, 34:195-208.
35. King R.D., Hirst J.D., Sternberg M.J.E. New approaches to QSAR: neural networks and machine learning. Perspect. Drug Discov. Des. 1993, 1:279-290.
36. A-Razzak M., Glen R.C. Rule induction applied to the derivation of quantitative structure-activity relationships. Methods and Principles in Medicinal Chemistry, Vol. 3. Advanced Computer-Assisted Techniques in Drug Discovery 1995, Vol. 3:319-331. Wiley-VCH, Weinheim. H. van de Waterbeemd (Ed.).
37. Bruneau P., McElroy N.R. Log D7.4 modeling using Bayesian regularized neural networks. Assessment and correction of the errors of prediction. J. Chem. Inf. Model. 2006, 46:1379-1387.
38. Winkler D., Burden F.R. Bayesian neural nets for modeling in drug discovery. Drug Discov. Today 2004, 2:104-111.
39. Palmer D.S., O'Boyle N.M., Glen R.C., Mitchell J.B.O. Random forest models to predict aqueous solubility. J. Chem. Inf. Model. 2007, 47:150-158.
40. Rogers D., Hopfinger A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34:854-866.
41. Clarke D.E. Evolutionary Algorithms in Molecular Design 2000, Wiley-VCH, Weinheim.
42. van de Waterbeemd H., El Tayar N., Carrupt P.A., Testa B. Pattern recognition study of QSAR substituent descriptors. J. Comput.-Aided. Mol. Des. 1989, 3:111-132.
43. Rose V.S., Croall I.F., MacFie H.J.H. An application of unsupervised neural network methodology (Kohonen topology-preserving mapping) to QSAR analysis. Quant. Struct.-Act. Relat. 1991, 10:6-15.
44. McFarland J.W., Gans D.J. On the significance of clusters in the graphical display of structure-activity data. J. Med. Chem. 1986, 29:505-514.
45. Rose V.S., Wood J. Generalized cluster significance analysis and stepwise cluster significance analysis with conditional probabilities. Quant. Struct.-Act. Relat. 1998, 17:348-356.
46. Kowalski B.R., Bender C.F. Pattern recognition. II Linear and non-linear methods for displaying chemical data. J. Am. Chem. Soc. 1973, 95:686-693.
47. Domine D., Devillers J., Chastrette M. A nonlinear map of substituent constants for selecting test series and deriving structure-activity relationships. I. Aromatic series. J. Med. Chem. 1994, 37:973-980.
48. Domine D., Devillers J., Chastrette M. A nonlinear map of substituent constants for selecting test series and deriving structure-activity relationships. II. Aliphatic series. J. Med. Chem. 1994, 37:981-987.
49. Anzali S., Barnickel G., Krug M., Sadowski J., Wagener M., Gasteiger J., Polanski J. The comparison of geometric and electronic properties of molecular surfaces by neural networks: application to the analysis of corticosteroid-binding globulin activity of steroids. J. Comput.-Aided Mol. Des. 1996, 10:521-534.
50. Leonard J.T., Roy K. On selection of training and test sets for the development of predictive QSAR models. QSAR Comb. Sci. 2006, 25:235-249.
51. Gleeson M.P. Plasma protein binding affinity and its relationship to mole-cular structure: an in silico analysis. J. Med. Chem. 2007, 50:101-112.
52. Wold S., Eriksson L. Methods and Principles in Medicinal Chemistry, Chemometric Methods in Molecular Design 1995, Vol. 2:309-318. Wiley-VCH, Weinheim. H. van de Waterbeemd (Ed.).
53. Baroni M., Costantino G., Cruciani G., Riganelli D., Valigi R., Clementi S. Generating optimal linear PLS estimations (GOLPE): an advanced chemometric tool for handling 3D-QSAR problems. Quant. Struct.-Act. Relat. 1993, 12:9-20.
54. 2!. J. Mol. Graph. Model. 2002, 20:269-276.
55. 2 for the training set or low root mean square error of prediction for the test set?. QSAR Comb. Sci. 2005, 24:385-396.
56. Wakeling I.N., Morris J.J. A test of significance for partial least squares regression. J. Chemom. 1993, 7:291-304.
57. Tetko I.V., Tanchuk V.Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS2.1 program. J. Chem. Inf. Comput. Sci. 2002, 42:1136-1145.
58. Tetko I.V., Bruneau P., Mewes H.W., Rohrer D.C., Poda G.L. Can we estimate the accuracy of ADME-Tox predictions?. Drug Discov. Today. 2006, 11:700-707.
59. Zhang S., Golbraikh A., Oloff S., Kohn H., Tropsha A. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model. 2006, 46:1984-1995.
60. Guha R., Dutta D., Jurs P.C., Chen T. Local lazy regression: making use of the neighbourhood to improve QSAR predictions. J. Chem. Inf. Model. 2006, 46:1836-1847.
61. Yuan H., Wang Y., Cheng Y. Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity. J. Chem. Inf. Model. 2007, 47:159-169.
62. De Cerqueira Lima P., Golbraikh A., Oloff S., Xiao Y., Tropsha A. Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model. 2006, 46:1245-1254.
63. Arodz T., Yuen D.A., Dudek A.Z. Ensemble of linear models for predicting drug properties. J. Chem. Inf. Model. 2006, 46:416-423.
64. Rodgers S., Davis A.M., Tomkinson N., Van de Waterbeemd, H. QSAR modeling using automatically updating correction libraries: application to a human plasma protein binding model. J. Chem. Inf. Model. 2007, 47, 2401-2407.
65. Cartmell J., Enoch S., Krstajic D., Leahy D.E. Automated QSPR through competitive workflow. J. Comput.-Aided. Mol. Des. 2005, 19:821-833.
66. Carlson R., Nordahl A. Exploring organic synthetic experimental procedures. Top. Curr. Chem. 1993, 166:1-64.
67. Topliss J.G. Utilization of operational schemes for analog synthesis in drug design. J. Med. Chem. 1972, 15:1006-1011.
68. Craig P.N. Interdependence between physical parameters and selection of substituent groups for correlation studies. J. Med. Chem. 1971, 14:680-684.
69. Hansch C., Leo A. Substituent Constants for Correlation Analysis in Chemistry and Biology 1979, John Wiley & Sons, New York.
70. Norinder U., Högberg T. PLS-based quantitative structure-activity relationship for substituted benzamides of clebopride type. Application of experimental design in drug design. Acta Chem. Scand. 1992, 46:363-366.
71. Lewis R.A. A general method for exploiting QSAR models in lead optimization. J. Med. Chem. 2005, 48:1638-1648.
72. Brown N., McKay B., Gasteiger J. A novel workflow for the inverse QSPR problem using multiobjective optimization. J. Comput.-Aided Mol. Des. 2006, 20:333-341.
73. Sheridan P.P., Hunt P., Culberson J.C. Molecular transformations as a way of finding and exploiting consistent local QSAR. J. Chem. Inform. Model. 2006, 46:180-192.
74. Haubertin D.Y., Bruneau P. A database of historically-observed chemical replacements. J. Chem. Inf. Model. 2007, 47:1294-1302.
75. Leach A.G., Jones H.D., Cosgrove D.A., Kenny P.W., Ruston L., MacFaul P., Wood J.M., Colclough N., Law B. Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J. Med. Chem. 2006, 49:6672-6682.
76. Nicolotti O., Gillet V., Fleming P., Green D. Multi-objective optimisation in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. J. Med. Chem. 2002, 43:5069-5080.
77. Pharmacokinetic Optimization in Drug Research: Biological, Physicochemical and Computational Strategies 2001, Wiley-VCH, Weinheim and Zurich.
78. Livingstone D. Data Analysis for Chemists 1995, Oxford University Press, Oxford.
79. Hansch C., Leo A. Exploring QSAR. Fundamentals and Applications in Chemistry and Biology 1995, American Chemical Society, Washington, DC.
80. Dickins M., van de Waterbeemd H. Simulation models for drug disposition and drug interactions. Drug Discov. Today: BioSilico 2004, 2:38-45.
81. Ottaviani G., Martel S., Carrupt P.A. In silico and in vitro filters for the fast estimation of skin permeation and distribution of new chemical entities. J. Med. Chem. 2007, 50:742-748.
82. Lipophilicity in Drug Action and Toxicology 1996, Wiley-VCH, Weinheim.
83. Schultz T.W., Hewitt M., Netzeva T.I., Cronin M.T.D. Assessing applicability domains of toxicological QSARs: definition, confidence in predicted values, and the role of mechanisms of action. QSAR Comb. Sci. 2007, 26:238-254.
84. Grunewald G.L., Dahanukar V.H., Jalluri R.K., Criscione K.R. Synthesis, biochemical evaluation and classical and three-dimensional quantitative structure-activity relationship studies of 7-substituted-1,2,3,4-tetrahydroisoquinolines and their relative affinities toward phenylethanolamine N-methyltransferase and the α2-adrenoreceptor. J. Med. Chem. 1999, 42:118-134.
85. Smith D.A., Jones B.C., Walker D.K. Design of drugs involving the concepts and theories of drug metabolism and pharmacokinetics. Med. Res. Rev. 1996, 16:243-266.
86. Chan O.H., Stewart B.H. Physicochemical and drug-delivery considerations for oral drug bioavailability. Drug Discov. Today 1996, 1:461-473.
87. van de Waterbeemd H., Smith D.A., Beaumont K., Walker D.K. Property-based design: optimisation of drug absorption and pharmacokinetics. J. Med. Chem. 2001, 44:1313-1333.
88. Yamashita F., Hashida M. In silico approaches for predicting ADME properties of drugs. Drug Metab. Pharmacokin. 2004, 19:327-338.
89. van de Waterbeemd H., Gifford E. ADMET in silico modeling: towards prediction paradise?. Nat. Rev. Drug Discov. 2003, 2:192-204.
90. van de Waterbeemd, H. In silico models to predict oral absorption. In ADME/Tox Approaches, Comprehensive Medicinal Chemistry 2nd Ed(Taylor, J. B., Triggle, D. J., Testa, B., van de Waterbeemd, H., Eds.) Vol. V., Elsevier: Oxford, 2007, pp. 669-697.
91. Zhao Y.H., Le J., Abraham M.H., Hersey A., Eddershaw P.J., Luscombe C.N., Boutina D., Beck G., Sherborne B., Cooper I., Platts J.A. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 2001, 90:749-784.
92. Burton J., Ijjaali I., Barberan O., Petitet F., Vercauteren D.P., Michel.A. Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: importance of the quality of the dataset. J. Med. Chem. 2006, 49:6231-6240.
93. Zhao Y.H., Abraham M.H., Ibrahim A., Fish P.V., Cole S., Lewis M.L., De Groot M., Reynolds D.P. Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J. Chem. Inf. Model. 2007, 47:170-175.
94. Song M., Clark M. Development and evaluation of an in silico model for hERG binding. J. Chem. Inf. Model. 2006, 46:392-400.
95. Coats E.A., Cordes H.-P., Kulkarni V.M., Richter M., Schaper K.-J., Wiese M., Seydel J.K. Multiple regression and principal component analysis of antibacterial activities of sulfones and sulfonamides in whole cell and cell-free systems of various DDS sensitive and resistant bacterial strains. Quant. Struct.-Act. Relat. 1985, 4:99-109.
96. Almerico A.M., Tutone M., Lauria A., Diana P., Barraja P., Montalbano A., Cirrincione G., Dattolo G. A multivariate analysis of HIV-1 protease inhibitors and resistance induced by mutation. J. Chem. Inf. Model. 2006, 46:168-179.
97. Cheeseright T., Mackey M., Rose S., Vinter A. Molecular field technology applied to virtual screening and finding the bioactive conformation. Expert Opin. Drug Discov. 2007, 2:131-144.
98. Gorse D., Lahana R. Functional diversity of compound libraries. Curr. Opin. Chem. Biol. 2000, 4:287-294.
99. Gillet V.J., Nicolotti O. Evaluation of reactant-based and product-based approaches to the design of combinatorial libraries. Perspect. Drug Discov. Dev. 2000, 20:265-287.
100. Good A.C., Lewis R.A. A new methodology for profiling combinatorial libraries and screening sets: cleaning up the design process with HARPick. J. Med. Chem. 1997, 40:3926-3936.
101. Martin E., Wong A. Sensitivity analysis and other improvements to tailored combinatorial library design. J. Chem. Inf. Comput. Sci. 2000, 40:215-220.
102. Sheridan R.P., SanFeliciano S.G., Kearsley S.K. Designing targeted libraries with genetic algorithms. J. Mol. Graph. Model. 2000, 18:320-334.
103. Brown R.D., Hassan M., Waldman M. Combinatorial library design for diversity, cost efficiency and drug-like character. J. Mol. Graph. Model. 2000, 18:427-437.
104. Rose S., Stevens A. Computational design strategies for combinatorial libraries. Curr. Opin. Chem. Biol. 2003, 7:331-339.
105. Baxter C.A., Murray C.W., Waszkowycz B., Li J., Sykes R.A., Bone R.G.A., Perkins T.D.J., Wylie W. New approach to molecular docking and its application to virtual screening of chemical databases. J. Chem. Inf. Comput. Sci. 2000, 40:254-262.
106. Sadowski J., Kubinyi H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 1998, 41:3325-3329.
107. Ajay A., Walter W.P., Murko M.A. Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules?. J. Med. Chem. 1998, 41:3314-3324.
108. Nell P.G., Mundt S.M. Chemoinformatic tools in high-throughput screening data analyses. Methods and Principles in Medicinal Chemistry, Vol. 35. High Throughput-Screening in Drug Discovery 2006, Vol. 35:207-257. Wiley-VCH, Weinheim. J. Hüser (Ed.).
109. Lobell M., Hendrix M., Hinzen B., Keldenich J., Meier H., Schmeck C., Schohe-Loop R., Wunberg T., Hillisch A. In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem 2006, 1:1229-1236.
110. Yan S.F., Asatryan H., Li J., Zhou Y. Novel statistical approach for primary high-throughput screening hit selection. J. Chem. Inf. Model. 2005, 45:1784-1790.
111. Fischer H.P., Heyse S. From targets to leads: the importance of advanced data analysis for decision support in drug discovery. Curr. Opin. Drug Discov. Dev. 2005, 8:334-346.
112. Kolossov E., Lemon A. Medicinal chemistry tools: making sense of HTS data. Eur. J. Med. Chem. 2006, 41:166-175.
113. Yan S.F., King F.J., He Y., Caldwell J.S., Zhou Y. Learning from the data: mining of large high-throughput screening databases. J. Chem. Inf. Model. 2006, 46:2381-2395.
114. Salt D.W., Ajmani S., Chrichton R., Livingstone D.J. An improved approximation to the estimation of the critical F values in best subset regression. J. Chem. Inf. Model. 2007, 47:143-149.
115. Nigsch F., Bender A., Van Buuren B., Tissen J., Nigsch E., Mitchell J.B.O. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. J. Chem. Inf. Model. 2006, 46:2412-2422.
116. Huang J., Ma G., Muhammad I., Cheng Y. Identifying P-glycoprotein substrates using a support vector machine optimized by a particle swarm. J. Chem. Inf. Model. 2007, 47:1638-1647.
117. Obrezanova O., Csanyi G., Gola J.M.R., Segall M.S. Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model. 2007, 47, 1847-1857.