Chemoinformatic Tools for Library Design and the Hit-to-Lead Process: A User's Perspective

Chemoinformatics in Drug Discovery

Volumn 23, Issue , 2005, Pages 381-435

  Goodnow, Robert Alan ^a   Gillespie, Paul ^a   Bleicher, Konrad ^b  

^a HOFFMANN LA ROCHE INC   (United States)

^b F HOFFMANN LA ROCHE LTD   (Switzerland)

Author keywords

Chemoinformatic tools for library design; Druglikeness; Druglikeness in small molecule libraries; Frequent hitters; Hit to lead process; Identification of a lead series; Leads from libraries; Prediction of solubility; Property predictions

Indexed keywords

BINDING ENERGY; DATA VISUALIZATION; DRUG DELIVERY; FORECASTING; LIBRARIES; MOLECULAR GRAPHICS; MOLECULES; SOLUBILITY;

DRUGLIKENESS; FREQUENT HITTERS; LIBRARY DESIGNS; PROPERTY PREDICTIONS; SMALL-MOLECULE LIBRARY;

DIGITAL LIBRARIES;

EID: 84890641969     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/3527603743.ch15     Document Type: Chapter

References (199)

1. BOLTEN, B.M. and DEGREGORIO, T. Trends in development cycles. Nat. Rev. Drug Disc. 2002, 1, 335-336.
2. OPREA, T.I. Current trends in lead discovery: Are we looking for the appropriate properties? J. Comput.-Aided Mol. Des. 2002, 16, 325-334.
3. BOYD, D.B. In Rational Molecular Design in Drug Research, LILJEFORS, T., JORGENSEN, F.S., and KROGSGAARDLARSEN, P. (Eds).Munksgaard, Copenhagen, 1998, 15-29.
4. Parexel's Pharmaceutical R&D Statistical Sourcebook 2001, High Performance Drug Discovery: Speed to Value, 2001, 139-140.
5. DEMASI, J.A., HANSEN, R.W., and GRABOWSKI, H.G. The price of innovation: new estimates of drug development costs. J. Health Econ. 2003, 22, 151-185.
6. HOPKINS, A.L. and GROOM, C.R. Opinion: the druggable genome. Nat. Rev. Drug Disc. 2002, 1, 727-730.
7. BAILEY, D. and BROWN, D. High throughput chemistry and structure-based design: survival of the smartest. Drug Disc. Today 2001, 6, 57-58.
8. MICHNE, W.F. Hit-to-lead chemistry: a key element in new lead generation. Pharm. News 1996, 3, 19-21.
9. CAMPBELL, S. and DIFFERDING, E. Drug discovery strategies: from leads to drugs. Chim. Nouv. 2001, 19, 3347-3352.
10. TROPSHA, A. and REYNOLDS, C.H. Designing focused libraries for drug discovery: hit to lead to drug. J. Mol. Graph. Model. 2002, 20, 427-428.
11. BAXTER, A.D. and LOCKEY, P.M. 'Hit' to 'lead' and 'lead' to 'candidate' optimisation using multi-parametric principles. Drug Disc. World 2001, 2, 9-15.
12. TEAGUE, S.J., DAVIS, A.M., LEESON, P.D., and OPREA, T. The design of leadlike combinatorial libraries. Angew. Chem., Int. Ed. Engl. 1999, 38, 3743-3748.
13. BLEICHER, K.H., BOHM, H.J., MULLER, K., and ALANINE, A.I. A guide to drug discovery: hit and lead generation: beyond high throughput screening. Nat. Rev. Drug Disc. 2003, 2, 369-378.
14. ALANINE, A., NETTEKOVEN, M., ROBERTS, E., and THOMAS, A.W. Lead generation - enhancing the success of drug discovery by investing in the hit to lead process. Comb. Chem. High Throughput Screen. 2003, 6, 51-66.
15. AMIDON, G.L., LENNERÄS, H., SHAH, V.P., and CRISON, J.R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailablity. Pharm. Res. 1995, 12, 413-420.
16. LIPINSKI, C.A., LOMBARDO, F., DOMINY, B.W., and 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.
17. LIVINGSTONE, D.J. Theoretical property predictions. Curr. Top. Med. Chem. 2003, 3, 1171-1192.
18. RAN, Y. and YALKOWSKY, S.H. Prediction of drug solubility by the general solubility equation (GSE). J. Chem. Inf. Comput. Sci. 2001, 41, 354-357.
19. ABRAHAM, M.H. and LE, J. The correlation and prediction of the solubility of compounds in water using an amended solvation energy relationship. J. Pharm. Sci. 1999, 88, 868-880.
20. JORGENSEN, W.L. and DUFFY, E.M. Prediction of drug solubility from structure. Adv. Drug Deliv. Rev. 2002, 54, 355-366.
21. MEYLAN, W.M., HOWARD, P.H., and BOETHLING, R.S. Improved method for estimating water solubility from octanol/water partition coefficient. Environ. Toxicol. Chem. 1996, 15, 100-106.
22. GAO, H., SHANMUGASUNDARAM, V., and LEE, P. Estimation of aqueous solubility of organic compounds with QSPR approach. Pharm. Res. 2002, 19, 497-503.
23. BUTINA, D. and GOLA, J.M.R. Modeling aqueous solubility. J. Chem. Inf. Comput. Sci. 2003, 43, 837-841.
24. CHEN, X.-Q., CHO, S.J., LI, Y., and VENKATESH, S. Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship. J. Pharm. Sci. 2002, 9, 1838-1852.
25. CHENG, A. and MERZ, K.M. Jr. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J. Med. Chem. 2003, 46, 3572-3580.
26. HUUSKONEN, J. Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J. Chem. Inf. Comput. Sci. 2000, 40, 773-777.
27. RAN, Y., JAIN, N., and YALKOWSKY, S.H. Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE). J. Chem. Inf. Comput. Sci. 2001, 41, 1208-1217.
28. MCELROY, N.R. and JURS, P.C. Prediction of aqueous solubility of heteroatom-containing organic compounds from molecular structure. J. Chem. Inf. Comput. Sci. 2001, 41, 1237-1247.
29. TETKO, I.V., TANCHUK, V.Yu., KASHEVA, T.N., and VILLA, A.E.P. Estimation of Aqueous solubility of chemical compounds using E-state indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1488-1493.
30. BRUNEAU, P. Search for predictive generic model of aqueous solubility using Bayesian neural nets. J. Chem. Inf. Comput. Sci. 2001, 41, 1605-1616.
31. LIU, R. and SO, S.-S. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility. J. Chem. Inf. Comput. Sci. 2001, 41, 1633-1639.
32. ENGKVIST, O. and WREDE, P. High throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors. J. Chem. Inf. Comput. Sci. 2002, 42, 1247-1249.
33. YAN, A. and GASTEIGER, J. Prediction of aqueous solubility of organic compounds based on a 3D structure representation. J. Chem. Inf. Comput. Sci. 2003, 43, 429-434.
34. WEGNER, J.K. and ZELL, A. Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J. Chem. Inf. Comput. Sci. 2003, 43, 1077-1084.
35. YAFFE, D., COHEN, Y., ESPINOSA, G., ARENAS, A., and GIRALT, F. A fuzzy ARTMAP based on quantitative structure-property relationships (QSPRs) for predicting aqueous solubility of organic compounds. J. Chem. Inf. Comput. Sci. 2001, 41, 1177-1207.
36. MANALLACK, D.T., TEHAN, B.G., GANCIA, E., HUDSON, B.D., FORD, M.G., LIVINGSTONE, F.J., WHITLEY, D.C., and PITT, W.R. A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J. Chem. Inf. Comput. Sci. 2003, 43, 674-679.
37. Syracuse Research Corporation, 6225 Running Ridge Road, North Syracuse, NY 13212.
38. Advanced Chemistry Development, Inc., 90 Adelaide Street West, Suite 600, Toronto, Ontario M5H 3V9, Canada.
39. Simulations Plus, Inc., 1220 W. Avenue, Lancaster, CA 93534.
40. ERTL, P., ROHDE, B., and SELZER, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43, 3714-3717.
41. BARONE, R. and CHANON, M. A new and simple approach to chemical complexity. Application to the synthesis of natural products. J. Chem. Inf. Comput. Sci. 2001, 41, 269-272.
42. BRADSHAW, J. MUG'01-15th Daylight User Group Meeting. March 6, 2001. Website: http://www.daylight.com/meetings/mug01/Bradshaw/History/.
43. WALTERS, W.P., AJAY, and MURCKO, M.A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 1999, 3, 384-387.
44. CLARK, D.E. and PICKETT, S.D. Computational methods for the prediction of ''Drug-Likeness''. Drug Disc. Today 2000, 5, 49-58.
45. BLAKE, J.F. Chemoinformatics - predicting the physicochemical properties of ''Drug-like'' molecules. Curr. Opin. Biotech. 2000, 11, 104-107.
46. AJAY. Predicting drug-likeness: Why and How? Curr. Top. Med. Chem. 2002, 2, 1273-1286.
47. WALTERS, W.P. and MURCKO, M.A. Prediction of 'Drug-likeness'. Adv. Drug Deliv. Rev. 2002, 54, 255-271.
48. EGAN, W.J., WALTERS, W.P., and MURCKO, M.A. Guiding molecules towards drug-likeness. Curr. Opin. Drug Disc. Dev. 2002, 5, 540-549.
49. GHOSE, A.K., VISWANADHAN, V.N., and WENDOLOSKI, J.J. Prediction of hydrophobic (Lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J. Phys. Chem. A 1998, 102, 3762-3772.
50. SADOWSKI, J. and KUBINYI, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 1998, 41, 3325-3329.
51. PROUDFOOT, J.R. Exploitation of HTS lead structures: lessons from the successes. Abstracts of Papers, 225th ACS National Meeting. New Orleans, LA, March 23-27, 2003, MEDI-193.
52. GERSHELL, L.J. and ATKINS, J.H. Timeline: a brief history of novel drug discovery technologies. Nat. Rev. Drug Disc. 2003, 2, 321-327.
53. ANDREWS, C.W., BENNETT, L., and YU, L.X. Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship. Pharm. Res. 2000, 17, 639-644.
54. STENBERG, P., BERGSTRÖM, C.A.S., LUTHMAN, K., and ARTURSSON, P. Theoretical predictions of drug absorption in drug discovery and development. Clin. Pharmcokinet. 2002, 41, 877-899.
55. CLARK, D.E. and GROOTENHUIS, P.D. Progress in computational methods for the prediction of ADMET properties. Curr. Opin. Drug. Disc. Dev. 2002, 5, 382-390.
56. VAN DE WATERBEEMD, H. and GIFFORD, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Disc. 2003, 2, 192-204.
57. MASIMIREMBWA, C.M., RIDDERSTROEM, M., ZAMORA, I., and ANDERSSON, T.B. Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates. Methods Enzymol. 2002, 357 (Cytochrome P450, Part C), 133-144.
58. WESSEL, M.D. and MENTE, S. ADME by computer. Annu. Rep. Med. Chem. 2001, 36, 257-266.
59. YAN, Z. and CALDWELL, G.W. Metabolism profiling, and cytochrome P450 inhibition & induction in drug discovery. Curr. Top. Med. Chem. 2001, 1, 403-425.
60. SUSNOW, R.G. and DIXON, S.L. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J. Chem. Inf. Comput. Sci. 2003, 43, 1308-1315.
61. KOROLEV, D., BALAKIN, K.V., NIKOLSKY, Y., KIRILLOV, E., IVANENKOV, Y.A., SAVCHUK, N.P., IVASHCHENKO, A.A., and NIKOLSKAYA, T. Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach. J. Med. Chem. 2003, 46, 3631-3643.
62. SANGHVI, T., NI, N., MAYERSOHN, M., and YALKOWSKY, S.H. Predicting passive intestinal absoprtion using a single parameter. QSAR Comb. Sci. 2003, 22, 247-257.
63. MANGA, N., DUFFY, J.C., ROWE, P.H., and CRONIN, M.T.D. A hierarchical QSAR model for urinary excretion of drugs in humans as a predictive tool for biotransformation. QSAR Comb. Sci. 2003, 22, 263-273.
64. HOU, T. and XU, X. ADME evaluation in drug discovery. J. Mol. Model. 2002, 8, 337-349.
65. NIWA, T. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures. J. Chem. Inf. Comput. Sci. 2003, 43, 113-119.
66. KLOPMAN, G., STEFAN, L.R., and SAIAKHOV, R.D. ADME Evaluation 2. A computer model for the prediction of intestinal absorption in humans. Eur. J. Pharm. Sci. 2002, 17, 253-263.
67. BURTON, P.S., GOODWIN, J.T., VIDMAR, T.J., and AMORE, B.M. Predicting drug absorption: How nature made it a difficult problem. J. Pharm. Exp. Ther. 2002, 303, 889-895.
68. ZHAO, Y.H., LE, J., ABRAHAM, M.H., HERSEY, A., EDDERSHAW, P.J., LUSCOMBE, C.N., BOUTINA, D., BECK, G., SHERBORNE, B., COOPER, I., and PLATIS, 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.
69. SHEN, M., XIAO, Y., GOLBRAIKH, A., GOMBAR, V.K., and TROPSHA, A. Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. J. Med. Chem. 2003, 46, 3013-3020.
70. TURNER, J.V., GLASS, B.D., and AGATANOVIC-KUSTRIN, S. Prediction of drug bioavailability based on molecular structure. Anal. Chim. Acta 2003, 485, 90-102.
71. BENFENATI, E. and GINI, G. Computational predictive programs (Expert Systems) in toxicology. Toxicology 1997, 119, 213-225.
72. CRONIN, M.T.D. Computer-aided prediction of drug toxicity in high throughput screening. Pharm. Pharmacol. Commun. 1998, 4, 157-163.
73. MATTHEWS, E.J. and CONTRERA, J.F. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Reg. Tox. Pharm. 1998, 28, 242-264.
74. ROSENKRANZ, H.S., CUNNINGHAM, A.R., ZHANG, Y.P., CLAYCAMP, H.G., MACINA, O.T., SUSSMAN, N.B., GRANT, S.G., and KLOPMAN, G. Development, characterization and application of predictive-toxicology models. SAR QSAR Env. Res. 1999, 10, 277-298.
75. GREENE, N., JUDSON, P.N., LANGOWSKI, J.J., and MARCHANT, C.A. Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR QSAR Env. Res. 1999, 10, 299-314.
76. JOHNSON, D.E. and WOLFGANG, G.H.I. Assessing the potential toxicity of new pharmaceuticals. Curr. Top. Med. Chem. 2001, 1, 233-245.
77. ROCHE, O., TRUBE, G., ZUEGGE, J., PFLIMLIN, P., ALANINE, A., and SCHNEIDER, G. A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. ChemBioChem 2002, 3, 455-459.
78. BARRATT, M.D. and RODFORD, R.A. The computational prediction of toxicity. Curr. Opin. Chem. Biol. 2001, 5, 383-388.
79. ADMANS, G., TAKAHASHI, Y., BAN, S., KATO, H., ABE, H., and HANAI, S. Artificial neural network for predicting the toxicity of organic molecules. Bull. Chem. Soc. Japan 2001, 74, 2451-2461.
80. GREENE, N. Computer systems for the prediction of toxicity: an update. Adv. Drug Del. Rev 2002, 54, 417-431.
81. FENG, J., LURATI, L., OUYANG, H., ROBINSON, T., WANG, Y., YUAN, S., and YOUNG, S.S. Predictive toxicology: benchmarking molecular descriptors and statistical methods. J. Chem. Inf. Comput. Sci. 2003, 43, 1463-1470.
82. CAVALLI, A., POLUZZI, E., DE PONTI, F., and RECANATINI, M. Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem. 2002, 45, 3844-3853.
83. KESERU, G.M. Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods. Bioorg. Med. Chem. Lett. 2003, 13, 2773-2775.
84. DOBLER, M., LILL, M.A., and VEDANI, A. From crystal structures and their analysis to the in silico prediction of toxic phenomena. Helv. Chim. Acta 2003, 86, 1554-1568.
85. MUSKAL, S.M., JHA, S.K., KISHORE, M.P., and TYAGI, P. A simple and readily integratable approach to toxicity prediction. J. Chem. Infor. Comput. Sci. 2003, 43, 1673-1678.
86. LHASA Limited, Department of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
87. MultiCASE Inc., 23811 Chagrin Blvd. Ste 305, Beachwood, OH 44122.
88. Accelrys Inc., 9685 Scranton Road, San Diego, CA 92121.
89. MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA 94577.
90. Derwent World Drug Index, Derwent Information, 14 Great Queen Street, London WC2B 5DF, UK.
91. Website: http://www.fda.gov/cder/ob/default.htm.
92. HARDMAN, J.G., LIMBIRD, L.E., MOLINOFF, P.B., RUDDON, R.W., and GOODMAN GILMAN, A. (Eds.) Goodman and Gilman's: The Pharmacological Basis of Therapeutics. McGraw Hill, New York, 1996.
93. OPREA, T.I. Property distribution of drug-related chemical databases. J. Comput.-Aided Mol. Des. 2000, 14, 251-264.
94. HANSCH, C., SAMMES, P.G., and TAYLOR, J.B. (Eds.) Comprehensive Medicinal Chemistry: The Rational Design, Mechanistic Study, and the Therapeutic Application of Chemical Compounds. Pergamon Press, Oxford, 1990.
95. PALM, K., STENBERG, P., LUTHMAN, K., and ARTURSSON, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res. 1997, 14, 568-571.
96. GHOSE, A.K., VISWANADHAN, V.N., and WENDOLOSKI, J.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1999, 1, 55-68.
97. STEVENSON, J. Drug-like index: a new approach to measure drug-like compounds and their diversity. J. Chem. Inf. Comput. Sci. 2000, 40, 1177-1187.
98. MUEGGE, I., HEALD, S.L., and BRITTELLI, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 2001, 44, 1841-1846.
99. HANN, M.M., LEACH, A.R., and HARPER, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 2001, 41, 856-864.
100. MUEGGE, I. Pharmacophore features of potential drugs. Chem. Eur. Journal 2002, 8, 1976-1981.
101. VEBER, D.F., JOHNSON, S.R., CHENG, H.-Y., SMITH, B.R., WARD, K.W., and KOPPLE, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615-2623.
102. FEHER, M. and SCHMIDT, J.M. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci. 2003, 43, 218-227.
103. BLAKE, J.F. Examination of the computed molecular properties of compounds selected for clinical development. BioTechniques 2003, 34, S16-S20.
104. FRIMURER, T.M., BYWATER, R., NÆRUM, L., LAURITSEN, L.N., and BRUNAK, S. Improving the odds in discriminating ''Drug-like'' from ''Non Drug-like'' compounds. J. Chem. Inf. Comput. Sci. 2000, 40, 1315-1324.
105. MURCIA-SOLER, M., PÉREZ-GIMÉNEZ, F., GARCÍA-MARCH, F.J., SALABERTSALVADOR, M.T., DÍAZ-VILLANUEVA, W., and CASTRO-BLEDA, M.J. Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks. J. Chem. Inf. Comput. Sci. 2003, 43, 1688-1702.
106. WAGENER, M. and VAN GEERESTEIN, V.J. Potential drugs and nondrugs: prediction and identification of important structural features. J. Chem. Inf. Comput. Sci. 2000, 40, 280-292.
107. ANZALI, S., BARNICKEL, G., CEZANNE, B., KRUG, M., FILIMONOV, D., and POROIKOV, V. Discriminating between drugs and nondrugs by prediction of activity spectra for substances (PASS). J. Med. Chem. 2001, 44, 3325-3329.
108. TAKAOKA, Y., ENDO, Y., YAMANOBE, S., KAKINUMA, H., OKUBO, T., SHIMAZAKI, Y., OTA, T., and SUMIYA, S. Development of a method for evaluating drug-likeness and ease of synthesis using a data det in which compounds are assigned scores based on chemists' intuition. J. Chem. Inf. Comput. Sci. 2003, 43, 1269-1275.
109. RISHTON, G.M. Reactive compounds and in vitro false positives in HTS. Drug Disc. Today 1997, 2, 382-384.
110. MCGOVERN, S.L., CASELLI, E., GRIGORIEFF, N., and SHOICHET, B.K. A common mechanism underlying promiscuous inhibitors from virtual and high throughput screening. J. Med. Chem. 2002, 45, 1712-1722.
111. MCGOVERN, S.L. and SHOICHET, B.K. Kinase inhibitors: not just for kinases anymore. J. Med. Chem. 2003, 46, 1478-1483.
112. RISHTON, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Disc. Today 2003, 8, 86-96.
113. ROCHE, O., SCHNEIDER, P., ZUEGGE, J., GUBA, W., KANSY, M., ALANINE, A., BLEICHER, K., DANEL, F., GUTKNECHT, E., ROGERS-EVANS, M., NEIDHART, W., STALDER, H., DILLON, M., SJOEGREN, E., FOTOUHI, N., GILLESPIE, P., GOODNOW, R., HARRIS, W., JONES, P., TANIGUCHI, M., TSUJII, S., VON DER SAAL, W., ZIMMERMANN, G., and SCHNEIDER, G. Development of a virtual screening method for identification of ''Frequent Hitters'' in compound libraries. J. Med. Chem. 2002, 45, 137-142.
114. EVANS, B.E., RITTLE, K.E., BOCK, M.G., DIPARDO, R.M., FREIDINGER, R.M., WHITTER, W.L., LUNDELL, G.F., VEBER, D.F., ANDERSON, P.S., CHANG, R.S.L., LOTTI, V.J., CERINO, D.J., CHEN, T.B., KLING, P.J., KUNKEL, K.A., SPRINGER, J.P., and HIRSHFIELD, J. Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J. Med. Chem. 1988, 31, 2235-2246.
115. ChemNavigator, Inc., 6126 Nancy Ridge Drive, Suite 117, San Diego, CA 92121. Website: http://www.chemnavigator.com.
116. ChemBridge Corporation, 16981 Via Tazon, Suite G, San Diego, CA 92127. Website: http://www.hit21ead.com.
117. Specs, Fleminglaan 16, 2289 CP Rijswijk, The Netherlands. Website: http://www.specs.net.
118. Maybridge plc, Trevillett, Tintagel, Cornwall PL34 OHW, UK. Website: http://www.maybridge.com.
119. ARTURSSON, P., PALM, K., and LUTHMAN, K. Caco-2 monolayers in experimental and theoretical predictions of drug transport. Adv. Drug Deliv. Rev 2001, 46, 27-43.
120. KANSY, M., SENNER, F., and GUBERNATOR, K. Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J. Med. Chem. 1998, 41, 1007-1010.
121. MDS-Panlabs International, 11804 North Creek Parkway South, Bothell, WA 98011.
122. Cerep, 128 Rue Danton, BP 50601 92506 Rueil-Malmaison, France.
123. Zenas Technologies L.L.C., 5896 Fleur de Lis Drive, New Orleans, LA70124.
124. DOLLE, R.E. Comprehensive survey of combinatorial library synthesis: 2001. J. Comb. Chem. 2002, 4, 369-418.
125. DOLLE, R.E. Comprehensive survey of combinatorial library synthesis: 2000. J. Comb. Chem. 2001, 3, 477-517.
126. DOLLE, R.E. Comprehensive survey of combinatorial library synthesis: 1999. J. Comb. Chem. 2000, 2, 383-433.
127. DOLLE, R.E. and NELSON, K.H. Jr. Comprehensive survey of combinatorial library synthesis: 1998. J. Comb. Chem. 1999, 1, 235-282.
128. MERRITT, A.T. and GERRITZ, S.W. Combinatorial chemistry rumors of the demise of combinatorial chemistry are greatly exaggerated. Curr. Opin. Chem. Biol. 2003, 7, 305-307.
129. VALLER, M.J. and GREEN, D. Diversity screening versus focussed screening in drug discovery. Drug Disc. Today 2000, 5, 286-293.
130. ROSE, S. and STEVENS, A. Computational design strategies for combinatorial libraries. Curr. Opin. Chem. Biol. 2003, 7, 331-339.
131. GOLEBIOWSKI, A., KLOPFENSTEIN, S.R., and PORTLOCK, D.E. Lead compounds discovered from libraries: Part 2. Curr. Opin. Chem. Biol. 2003, 7, 308-325.
132. GOLEBIOWSKI, A., KLOPFENSTEIN, S.R., and PORTLOCK, D.E. Lead compounds discovered from libraries. Curr. Opin. Chem. Biol. 2001, 5, 273-284.
133. GHOSE, A.K. and VISWANADHAN, V.N. (Eds) Combinatorial Library Design and Evaluation - Principles, Software Tools and Applications in Drug Discovery. Marcel Dekker, New York, 2001.
134. GOOD, A.C. and LEWIS, R.A. New methodology for profiling combinatorial libraries and screening sets: cleaning up the design process with HARPick. J. Med. Chem. 1997, 40, 3926-3936.
135. MARTIN, E.J. and CRITCHLOW, R.E. Beyond mere diversity: tailoring combinatorial libraries for drug discovery. J. Comb. Chem. 1999, 1, 32-45.
136. GILLET, V.J., WILLETT, P., BRADSHAW, J., and GREEN, D. Selecting combinatorial libraries to optimize diversity and physical properties. J. Chem. Inf. Comput. Sci. 1999, 39, 169-177.
137. BROWN, R.D., HASSAN, M., and WALDMAN, M. Combinatorial library design for diversity, cost efficiency, and drug-like character. J. Mol. Graph. Model. 2000, 18, 427-437.
138. GILLET, V.J., KHATIB, W., WILLETT, P., FLEMING, P.J., and GREEN, D.V.S. Combinatorial library design using a multiobjective genetic algorithm. J. Chem. Inf. Comput. Sci. 2002, 42, 375-385.
139. GILLET, V.J., WILLETT, P., FLEMING, P.J., and GREEN, D.V.S. Designing focused libraries using MoSELECT. J. Mol. Graph. Model. 2002, 20, 491-498.
140. AGRAFIOTIS, D.K. Multiobjective optimization of combinatorial libraries. IBM J. Res. Dev. 2001, 45, 545-566.
141. PEARLMAN, R.S. and SMITH, K.M. Novel software tools for chemical diversity. Perspect. Drug Disc. Des. 1998, 9-11, 339-353.
142. MARTIN, Y.C. Diverse viewpoints on computational aspects of molecular diversity. J. Comb. Chem. 2001, 3, 231-250.
143. WRIGHT, T., GILLET, V.J., GREEN, D.V.S., and PICKETT, S.D. Optimizing the size and configuration of combinatorial libraries. J. Chem. Inf. Comput. Sci. 2003, 43, 381-390.
144. BEMIS, G.W. and MURCKO, M.A. Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095-5099.
145. BEMIS, G.W. and MURCKO, M.A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-2893.
146. ERTL, P. Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J. Chem. Inf. Comput. Sci. 2003, 43, 374-380.
147. KENNEDY, T. Managing the drug discovery/development interface. Drug Disc. Today 1997, 2, 436-444.
148. KUBINYI, H. Opinion: Drug research: myths, hype and reality. Nat. Rev. Drug Disc. 2003, 2, 665-668.
149. PEARLMAN R.S. and SMITH K.M. Combinatorial library design in the new century. Abstracts of Papers, 221st ACS National Meeting. SanDiego, CA, April 1-5, 2001, BTEC-039.
150. JAMOIS, E.A., HASSAN, M., and WALDMAN, M. Evaluation of reagent-based and product-based strategies in the design of combinatorial library subsets. J. Chem. Inf. Comput. Sci. 2000, 40, 63-70.
151. FEINBERG, J. and GOODNOW, R.A. unpublished method.
152. ANDREWS, P.R., CRAIK, D.J., and MARTIN, J.L. Functional group contributions to drug-receptor interactions. J. Med. Chem. 1984, 27, 1648-1657.
153. OPREA, T.I. Chemoinformatics in lead discovery. In Chemoinformatics in Drug Discovery, OPREA, T.I. (Ed.). Wiley-VCH, Weinheim, 2004, 25-41.
154. OPREA, T.I., DAVIS, A.M., TEAGUE, S.J., and LEESON, P.D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 2001, 41, 1308-1315.
155. LIPINSKI, C.A. Integration of physicochemical property considerations into the design of combinatorial libraries. Pharm. News 2002, 9, 195-202.
156. LIPINSKI, C.A. Avoiding investment in doomed drugs. Is poor solubility an industry wide problem? Curr. Drug Disc. 2001, 17-19.
157. NORINDER, U., ÖSTERBERG, T., and ARTURSSON, P. Theoretical calculation and prediction of Caco-2 cell permeability using MolSurf parametrization and PLS statistics. Pharm. Res. 1997, 14, 1786-1791.
158. STENBERG, P., LUTHMAN, K., ELLENS, H., LEE, C.P., SMITH, P.L., LAGO, A., ELLIOTT, J.D., and ARTURSSON, P. Prediction of the intestinal absorption of endothelin receptor antagonists using three theoretical methods of increasing complexity. Pharm. Res. 1999, 16, 1520-1526.
159. PALM, K., LUTHMAN, K., UNGELL, A.-L., STRANDLUND, G., and ARTURSSON, P. Correlation of drug absorption with molecular surface properties. J. Pharm. Sci. 1996, 85, 32-39.
160. STENBERG, P., LUTHMAN, K., and ARTURSSON, P. Prediction ofmembrane permeability to peptides from calculated dynamic molecular surface properties. Pharm. Res. 1999, 16, 205-212.
161. 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.
162. PALM, K., STENBERG, P., LUTHMAN, K., and ARTURSSON, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res. 1997, 14, 568-571.
163. EGA, W.J., MERZ, K.M. Jr., and BALDWIN, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000, 43, 3867-3877.
164. ERICKSON, J.A., JALAIE, M., ROBERTSON, D.H., LEWIS, R.A., and VIETH, M. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J. Med. Chem. 2004, 47, 45-55.
165. CRAIG, P.N. Interdependence between physical parameters and selection of substituent groups for correlation studies. J. Med. Chem. 1971, 14, 680-684.
166. TOPLISS, J.G. Utilization of operational schemes for analog synthesis in drug design. J. Med. Chem. 1972, 15, 1006-1011.
167. CHIU, T.-L. and SO, S.-S. Development of neural network QSPR models for Hansch substituent constants. 1. Method and validations. J. Chem. Inf. Comput Sci. 2003, 43, 147-153.
168. WEININGER, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 1988, 28, 31-36.
169. HANSCH, C. and LEO, A. Exploring QSAR, Fundamentals and Applications in Chemistry and Biology. ACS Professional Reference Book, ACS, Washington, DC, 1995, Chapter 1.
170. BÖHM, H.J. and SCHNEIDER, G. (Eds) Virtual Screening for Bioactive Molecules. Wiley-VCH, Weinheim, 2000.
171. TOLEDO-SHERMAN, L.M. and CHEN, D. High throughput virtual screening for drug discovery in parallel. Curr. Opin. Drug Disc. Dev. 2002, 5, 414-421.
172. DILLER, D.J. and MERZ, K.M. Jr. High throughput docking for library design and library prioritization. Proteins: Struct., Funct. Genet. 2001, 43, 113-124.
173. BOEHM, H.J., BOEHRINGER, M., BUR, D., GMUENDER, H., HUBER, W., KALUS, W., KOSTREWA, D., KUEHNE, H., LUEBBERS, T., MEUNIER-KELLER, N., and MUELLER, F. Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J. Med. Chem. 2000, 43, 2664-2674.
174. BAJORATH, J. Integration of virtual and high throughput screening. Nat. Rev. Drug Disc. 2002, 1, 882-894.
175. WALTERS, W.P., STAHL, M.T., and MURCKO, M.A. Virtual screening - an overview. Drug Disc. Today 1998, 3, 160-178.
176. BISSANTZ, C., FOLKERS, G., and ROGNAN, D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J. Med. Chem. 2000, 43, 4759-4767.
177. ABAGYAN, R. and TOTROV, M. High throughput docking for lead generation. Curr. Opin. Chem. Biol. 2001, 5, 375-382.
178. BÖHM, H.J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput.-Aided Mol. Des. 1992, 6, 61-78.
179. ROE, D.C. and KUNTZ, I.D. BUILDER v.2: improving the chemistry of a de novo design strategy. J. Comput.-Aided Mol. Des. 1995, 9, 269-282.
180. KUNTZ, I.D., BLANEY, J.M., OATLEY, S.J., LANGRIDGE, R., and FERRIN, T.E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 1982, 161, 269-288.
181. JONES, G., WILLETT, P., GLEN, R.C., LEACH, A.R., and TAYLOR, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997, 267, 727-748.
182. RAREY, M., KRAMER, B., LENGAUER, T., and KLEBE, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 261, 470-489.
183. RAREY, M., LEMMEN, C., and MATTER, H. Algorithmic engines in virtual screening. In Chemoinformatics in Drug Discovery, OPREA, T.I. (Ed.). Wiley-VCH, Weinheim, 2004, 59-115.
184. BÖHM, H.-J., BANNER, D.W., and WEBER, L. Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J. Comput.-Aided Mol. Des. 1999, 13, 51-56.
185. BOEHM, H.J. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput.-Aided Mol. Des. 1992, 6, 593-606.
186. WANG, R., LU, Y., and WANG, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46, 2287-2303.
187. SCHULZ-GASCH, T. and STAHL, M. Binding site characteristics in structure-based virtual screening: evaluation of current docking tools. J. Mol. Model. 2003, 9, 47-57.
188. BÖHM, H.-J. and STAHL, M. The use of scoring functions in drug discovery applications. Rev. Comput. Chem. 2002, 18, 41-87.
189. MUEGGE, I. and RAREY, M. Small molecule docking and scoring. Rev. Comput. Chem. 2001, 17, 1-60.
190. STAHL, M. and RAREY, M. Detailed analysis of scoring functions for virtual screening. J. Med. Chem. 2001, 44, 1035-1042.
191. MASON, J.S., GOOD, A.C., and MARTIN, E.J. 3-D Pharmacophores in drug discovery. Curr. Pharm. Des. 2001, 7, 567-597.
192. SCHNEIDER, G., CLEMENT-CHOMIENNE, O., HILFIGER, L., SCHNEIDER, P., KIRSCH, S., BOHM, H.-J., and NEIDHART, W. Virtual screening for bioactive molecules by evolutionary de novo design. Angew. Chem., Int. Ed. Engl. 2000, 39, 4130-4133.
193. SCHNEIDER, G., NEIDHART, W., and ADAM, G. Integrating virtual screening methods to the quest for novel membrane protein ligands. Curr. Med. Chem. CNS Agents 2001, 1, 99-112.
194. DANHO, W., SWISTOK, J., KHAN, W., TRUITT, T., KURYLKO, G., FRY, D., GREELEY, D., SUN, H., DVOROZNIAK, M., MACKIE, G., SPENCE, C., and GOODNOW, Jr, R. Structure-activity relationship and bioactive conformation of prolactin releasing peptides (PrRPs), ligand for a potential obesity target. In Proceedings of the 18th American Peptide Symposium, Boston, July 19-23, CHOREV, M. and SAWYER, R.K. (Eds). 2003.
195. Catalyst Tutorial, Release 4.6, Accelrys Inc., 9685 Scranton Road, San Diego, CA 92121.
196. FLOHR, S., KURZ, M., KOSTENIS, E., BRKOVICH, A., FOURNIER, A., and KLABUNDE, T. Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure-activity relationships and nuclear magnetic resonance studies on urotensin II. J. Med. Chem. 2002, 45, 1799-1805.
197. PATCHETT, A. and NARGUND, R.P. Privileged structures - an update. Annu. Rev. Med. Chem. 2000, 35, 289-298.
198. MASON, J.S., MORIZE, I., MENARD, P.R., CHENEY, D.L., HULME, C., and LABAUDINIERE, R.F. 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. J. Med. Chem. 1999, 42, 3251-3264.
199. A. SMELLIE, unpublished method.