Rapporteur, ensc, Montpellier

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2006, 46, 1806-1818.

43. Agrafiotis, D. K. Stochastic Proximity Embedding. J. Comput. Chem. 2003, 24, 1215-1221.

44. Givehchi, A.; Dietrich, A.; Wrede, P.; Schneider, G. Chemspaceshuttle: A tool for data mining in drug discovery by classification, projection, and 3d visualization. QSAR Comb. Sci. 2003, 22 (5), 549-559.

45. Bayada, D.M.; Hamersma, H.; van Geerestein, V.J. Molecular Diversity and Representativity in Chemical Databases J. Chem. Inf. Comput. Sci. 1999, 39, 1-10.

46. Oprea, T.I.; Gottfries, J. Chemography: The Art of Navigating in Chemical Space. J. Comb. Chem. 2001, 3, 157-166.

47. Shi, L. M.; Fan, Y.; Lee, J. K.; Waltham, M.; Andrews, D. T.; Scherf, U.; Paull, K. D.; Weinstein, J. N. Mining and Visualizing Large Anticancer Drug Discovery Databases. J. Chem. Inf. Comput. Sci. 2000, 40, 367-379.

48. Natarajan, R.; Nirdosh, I.; Basak, S. C.; Mills, D. R. QSAR Modeling of Flotation Collectors Using Principal Components Extracted from Topological Indices. J. Chem. Inf. Comput. Sci. 2002, 42, 1425-1430.

49. Xue, L.; Godden, J.; Gao, H.; Bajorath, J. Identification of a Preferred Set of Molecular Descriptors for Compound Classification Based on Principal Component Analysis. J. Chem. Inf. Comput. Sci. 1999, 39, 699-704.

50. Karthikeyan, M.; Glen, R. C.; Bender, A. General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks. J. Chem. Inf. Model. 2005, 45, 581-590.

51. Kohonen, T. Self-Organizing and Associative Memory, 3rd ed.; Springer-Verlag: Berlin, 1989.

52. 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.

53. Selzer, P.; Ertl, P. Applications of Self-Organizing Neural Networks in Virtual Screening and Diversity Selection. J. Chem. Inf. Model. 2006, ASAP.

54. Wagner, S.; Hofmann, A.; Siedle, B.; Terfloth, L.; Merfort, I.; Gasteiger, J. Development of a Structural Model for NF-B Inhibition of Sesquiterpene Lactones Using Self-Organizing Neural Networks. J. Med. Chem. 2006, 49, 2241-2252.

55. Polanski, J.; Zouhiri, F.; Jeanson, L.; Desmaele, D.; d'Angelo, J.; Mouscadet, J-F.; Gieleciak, R.; Gasteiger, J.; Le Bret, M. Use of the Kohonen Neural Network for Rapid Screening of Ex Vivo Anti-HIV Activity of Styrylquinolines. J. Med. Chem. 2002, 45, 4647-4654.

56. Polanski, J.; Gasteiger, J.; Jarzembek, K. Self-Organizing Neural Networks for Screening and Development of Novel Artificial Sweetener Candidates. Comb Chem High Throughput Screen 2000, 3, 481-495.

57. Mazzatorta, P.; Vracko, M.; Jezierska, A.; Benfenati, E. Modeling Toxicity by Using Supervised Kohonen Neural Networks J. Chem. Inf. Comput. Sci. 2003, 43, 485-492.

58. Yang, Z. R.; Chou, K.-C. Mining Biological Data Using Self-Organizing Map. J. Chem. Inf. Comput. Sci. 2003, 43, 1748-1753.

59. Otaki, J. M.; Mori, A.; Itoh, Y.; Nakayama, T.; Yamamoto, H. Alignment-Free Classification of G-Protein-Coupled Receptors Using Self-Organizing Maps. J. Chem. Inf. Model. 2006, 46, 1479-1490.

60. Guha, R.; Serra, J.R.; Jurs, P.C. Generation of QSAR sets with a self-organizing map. J Mol Graph Model. 2004, 23, 1-14.

61. Spencer, R. W. Diversity Analysis in high throughput screening. J. Biomol. Screening, 1997, 2 , 69-70.

62. Potter, T.; Matter, H. Random or Rational Design? Evaluation of Diverse Compound Subsets from Chemical Structure Databases. J.Med.Chem 1998, 41, 478-488.

63. Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. Do Structurally Similar Molecules Have Similar Biological Activity? J. Med. Chem. 2002, 45, 4350-4358.

64. Maldonado, A.G.; Doucet, J.P.; Petitjean, M.; Fan, B.T. Molecular similarity and diversity in chemoinformatics: From theory to applications. Mol. Divers. 2006, 10, 39-79.

65. Willett, P. Chemoinformatics – similarity and diversity in chemical libraries. Current Opinion in Biotechnology 2000, 11, 85-88.

66. Aggrafiotis, D. K.; Lobanov, V. S.; Rassokhin, D. N.; Izrailev, S. The Measurement of Molecular Diversity, in Virtual Screening for Bioactive Molecules, Böhm, H. G.; Schneider, G., 2000, 265-300.

67. Hansch, C.; Fujita, T. rsp Analysis – a Method for the Correlation of Biological Activity and Chemical Structure, J. Amer. Chem. Soc., 1964, 86, 1616-1626.

68. Agrafiotis, D. K.; Rassokhin, D. N. Design and Prioritization of Plates for High-Throughput Screening. J. Chem. Inf. Comput. Sci. 2001, 41, 798-805.

69. Agrafiotis, D. K. A Constant Time Algorithm for Estimating the Diversity of Large Chemical Libraries. J. Chem. Inf. Comput. Sci. 2001, 41, 159-167.

70. Xu, H.; Agrafiotis, D. K. Nearest Neighbor Search in General Metric Spaces Using a Tree Data Structure with a Simple Heuristic. J. Chem. Inf. Comput. Sci. 2003, 43, 1933-1941.

71. Daylight, Fingerprints – Screening and Similarity, http://www.daylight.com/dayhtml/doc/theory/ theory.finger.html

72. Schneider, G; Neidhart, W; Giller, T; Schmid, G. Scaffold-Hopping by Topological Pharmacophore Search: A Contribution to Virtual Screening. Angew. Chem. Int. Ed. 1999, 38, 2894-2896.

73. Nærum, L.; Nørskov-Lauritsen, L.; Olesen, P.H. Scaffold hopping and optimization towards libraries of glycogen synthase kinase-3 inhibitors. Bioorg. Med. Chem. Lett. 2002, 12, 1525-1528.

74. Gillet, V. J.; Willett, P.; Bradshaw, J. Similarity Searching Using Reduced Graphs. J. Chem. Inf. Comput. Sci. 2003, 0, 338-345.

75. Barker, E. J.; Gardiner, E. J.; Gillet, V. J.; Kitts, P.; Morris, J. Further Development of Reduced Graphs for Identifying Bioactive Compounds. J. Chem. Inf. Comput. Sci. 2003, 43, 346-356.

76. Harper, G.; Bravi, G. S.; Pickett, S. D.; Hussain, J.; Green, D. V. S. The Reduced Graph Descriptor in Virtual Screening and Data-Driven Clustering of High-Throughput Screening Data. J. Chem. Inf. Comput. Sci. 2004, 44, 2145-2156.

77. Barker, E. J.; Buttar, D.; Cosgrove, D. A.; Gardiner, E. J.; Kitts, P.; Willett, P.; Gillet, V. J. Scaffold Hopping Using Clique Detection Applied to Reduced Graphs. J. Chem. Inf. Model. 2006, 46, 503-511.

78. Willett, P.; Barnard, J. M.; Downs, G. M. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 1998, 38, 983-996.

79. Holliday, J. D.; Salim, N.; Whittle, M.; Willett, P. Analysis and Display of the Size Dependence of Chemical Similarity Coefficients. J. Chem. Inf. Comput. Sci. 2003, 43, 819-828.

80. Whittle, M.; Willett, P.; Klaffke, W.; van Noort, P. Evaluation of Similarity Measures for Searching the Dictionary of Natural Products Database. J. Chem. Inf. Comput. Sci. 2003, 43, 449-457.

81. Whittle, M.; Gillet, V. J.; Willett, P.; Alex, A.; Loesel, J. Enhancing the Effectiveness of Virtual Screening by Fusing Nearest Neighbor Lists: A Comparison of Similarity Coefficients. J. Chem. Inf. Comput. Sci. 2004, 44, 1840-1848.

82. Xue, L.; Godden, J. W.; Stahura, F. L.; Bajorath, J. Similarity Search Profiling Reveals Effects of Fingerprint Scaling in Virtual Screening. J. Chem. Inf. Comput. Sci. 2004, 44, 2032-2039.

83. M. S. Lajiness, in QSAR : Rational Aproaches to the Design of Bioactive Compounds, C. Silipo, A. Vittoria (Eds.), Elsevier, Amsterdam 1991, 201-204.

84. Stahl, M.; Mauser, H. Database Clustering with a Combination of Fingerprint and Maximum Common Substructure Methods. J. Chem. Inf. Model. 2005, 45, 542-548.

85. Rishton, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 2003, 8, 86-96.

86. Rishton, G.M. Reactive Compounds and In Vitro False Positives. 1999, presented for Vision in Business, Integrated Drug Discovery, Geneva, Switzerland.

87. McGovern, S.L.; Caselli, E.; Grigorieff, N.; Shoichet B.K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 2002, 45, 1712-1722.

88. Ryan, A. J.; Gray, N. M.; Lowe, P. N.; Chung, C.-w. Effect of Detergent on "Promiscuous" Inhibitors. J. Med. Chem. 2003, 46, 3448-3451.

89. McGovern, S.L.; Helfand, B.T.; Feng, B.; Shoichet, B.K. A specific mechanism of nonspecific inhibition. J.Med.Chem 2003, 46, 4265-4272.

90. Seidler, J.; McGovern, S. L.; Doman, T. N.; Shoichet, B. K.; Identification and Prediction of Promiscuous Aggregating Inhibitors among Known Drugs. J.Med.Chem 2003, 46, 4477-4486.

91. 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. 2001, 46, 3-26.

92. Lipinski, C.A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today 2004, 1, 337-341.

93. Frimurer, T.M.; Bywater, R.; Nærum, L.; Lauritsen, L.N.; Brunak, S. Improving the Odds in Discriminating “Drug-like” from “Non Drug-like” Compounds. J. Chem. Inf. Comput. Sci. 2000, 40, 1315-1324.

94. Oprea, T.I. Property distribution of drug-related chemical databases. J. Comput. Aided Mol. Des. 2000, 14, 251-264.

95. Veber, D. F.; Johnson, S. R.; Cheng, H.-Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J.Med.Chem 2002, 45, 2615-2623.

96. Lajiness, M. S.; Vieth, M.; Erickson, J. Molecular properties that influence oral drug-like behavior, Curr. Opin. Drug Discov. Devel., 2004, 7, 470-477.

97. Walters ,W.P.; Murcko M.A. Prediction of ‘drug-likeness’. Adv. Drug. Deliv. Rev. 2002, 54, 255-271.

98. Clark, D. E., Pickett, S. D., Computational methods for the prediction of ‘druglikeness’. Drug Discov. Today, 2000, 5, 49–58.

99. Muegge, I. Selection criteria for drug-like compounds, Med. Res. Rev., 2003, 23 ,302–321.

100. Sirois, S.; Hatzakis, G.; Wei, D.; Du, Q.; Chou, K.C. Assessment of chemical libraries for their druggability. Comput. Biol. Chem. 2005, 29, 55-67.

101. Xu, J.; Stevenson, J. Drug-like Index: A New Approach To Measure Drug-like Compounds and Their. J. Chem. Inf. Comput. Sci. 2000, 40, 1177-1187.

102. Zheng, S.; Luo, X.; Chen, G.; Zhu, W.; Shen, J.; Chen, K.; Jiang, H. A New Rapid and Effective Chemistry Space Filter in Recognizing a Druglike Database J. Chem. Inf. Comput. Sci. 2005, 45, 856-862.

103. Muegge, I.; Heald, S.L.; Brittelli, D. Simple Selection Criteria for Drug-like Chemical Matter J.Med.Chem 2001, 44, 1841-1846.

104. Zernov, V. V.; Balakin, K. V.; Ivaschenko, A. A.; Savchuk, N. P.; Pletnev, I. V. Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.

105. Ajay, A; Walters, W.P.; Murcko, M.A. Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? J.Med.Chem 1998, 41, 3314-3324.

106. Sadowski, J.; Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J.Med.Chem 1998, 41, 3325-3329.

107. Hann, M. M.; Leach, A. R.; 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.

108. Oprea, T.I. Current trends in lead discovery: Are we looking for the appropriate properties? J. Comput. Aided Mol. Des. 2002, 16, 325-334.

109. Davis, A.M.; Teague, S.J.; Kleywegt, G.J. Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. J. Chem. Inf. Comput. Sci. 2003, 42, 2718-2736.

110. Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A ‘Rule of Three’ for fragment-based lead discovery? Drug Discov. Today 2003, 0, 876-877.

111. DeSimone, R.W.; Currie, K.S.; Mitchell, S.A.; Darrow, J.W.; Pippin, D.A. Privileged Structures: Applications in Drug Discovery. Comb Chem High Throughput Screen 2004, 7, 473-493.

112. Horton, D.A.; Bourne, G.T.; Smythe, M.L. The Combinatorial Synthesis of Bicyclic Privileged Structures or Privileged Substructures. Chem. Rev. 2003, 103, 893-930.

113. Chen, W.L. Chemoinformatics: Past, Present, and Future. J. Chem. Inf. Model. 2006, ASAP.

114. Brown, F. Chemoinformatics: What is it and How does it Impact Drug Discovery. Annu. Rep. Med. Chem. 1998, 33, 375-384.

115. Handbook of Chemoinformatics; Gasteiger, J., Ed.; Wiley-VCH: Weinheim, Germany,
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