Similarity methods in chemoinformatics

Annual Review of Information Science and Technology

Volumn 43, Issue , 2009, Pages 1-117

  Willett, Peter ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 57849168939     PISSN: 00664200     EISSN: None     Source Type: Book Series    
DOI: 10.1002/aris.2009.1440430108     Document Type: Review

References (447)

1. A-Razzak, M., & Glen, R. C. (1992). Applications of rule-induction in the derivation of quantitative structure-activity relationships. Journal of Computer-Aided Molecular Design, 6, 349-383.
2. Adamson, G. W., & Bush, J. A. (1973). A method for the automatic classification of chemical structures. Information Storage and Retrieval, 9, 561-568.
3. Adamson, G. W., Cowell, J., Lynch, M. F., McLure, A. H. W., Town, W. G., & Yapp, A. M. (1973). Strategic considerations in the design of screening systems for substructure searches of chemical structure files. Journal of Chemical Documentation, 13, 153-157.
4. Agrafiotis, D. K. (1997). Stochastic algorithms for maximising molecular diversity. Journal of Chemical Information and Computer Sciences, 37, 841-851.
5. Agrafiotis, D. K. (2002). Multiobjective optimization of combinatorial libraries. Journal of Computer-Aided Molecular Design, 16, 335-356.
6. Agrafiotis, D. K., Bandyopadhyay, D., Wegner, J. K., & van Vlijmen, H. (2007). Recent advances in chemoinformatics. Journal of Chemical Information and Modeling, 47, 1279-1293.
7. Agrafiotis, D. K., & Lobanov, V. S. (1999). An efficient implementation of distance-based diversity measures based on k-d trees. Journal of Chemical Information and Computer Sciences, 39, 51-58.
8. Ajay, Walters, W. P., & Murcko, M. A. (1998). Can we learn to distinguish between "druglike" and "nondrug-like" molecules? Journal of Medicinal Chemistry, 41, 3314-3324.
9. Allen, F. H. (2002). The Cambridge Structural Database: A quarter of a million crystal structures and rising. Acta Crystallographica, B58, 380-388.
10. Alvarez, J., & Shoichet, B. (Eds.). (2005). Virtual screening in drug discovery. Boca Raton, FL: CRC Press.
11. Ash, J. E., & Hyde, E. (Eds.). (1975). Chemical information systems. Chichester, UK: Ellis Horwood.
12. Ash, J. E., Warr, W. A., & Willett, P. (Eds.). (1991). Chemical structure systems. Chichester, UK: Ellis Horwood.
13. Attias, R. (1983). DARC substructure search system: A new approach to chemical information. Journal of Chemical Information and Computer Sciences, 23, 102-108.
14. Baber, J. C., Shirley, W. A., Gao, Y., & Feher, M. (2006). The use of consensus scoring in ligand-based virtual screening. Journal of Chemical Information and Modeling, 46, 277-288.
15. Bajorath, J. (2002). Integration of virtual and high-throughput screening. Nature Reviews Drug Discovery, 1, 882-894.
16. Ballester, P. J., & Richards, W. G. (2007a). Ultrafast shape recognition for similarity search in molecular databases. Proceedings of the Royal Society A, 463, 1307-1321.
17. Ballester, P. J., & Richards, W. G. (2007b). Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chemistry, 28, 1711-1723.
18. Barker, E. J., Cosgrove, D. A., Gardiner, E. J., Gillet, V. J., Kitts, P., & Willett, P. (2006). Scaffold-hopping using clique detection applied to reduced graphs. Journal of Chemical Information and Modeling, 46, 503-511.
19. Barker, E. J., Gardiner, E. J., Gillet, V. J., Kitts, P., & Morris, J. (2003). Further development of reduced graphs for identifying bioactive compounds. Journal of Chemical Information and Computer Sciences, 43, 346-356.
20. Barnard, J. M. (1990). Draft specification for revised version of the standard molecular data (SMD) format. Journal of Chemical Information and Computer Sciences, 30, 81-96.
21. Barnard, J. M. (1993). Substructure searching methods: Old and new. Journal of Chemical Information and Computer Sciences, 33, 532-538.
22. Barnard, J. M. (Ed.). (1984). Computer handling of generic chemical structures. Aldershot, UK: Gower.
23. Barton, I. J., Creasey, S. E., Lynch, M. F., & Snell, M. J. (1974). An information-theoretic approach to text-searching in direct-access systems. Communications of the ACM, 17, 345-350.
24. Bawden, D. (1993). Molecular dissimilarity in chemical information systems. In W. A. Warr (Ed.), Chemical structures 2 (pp. 383-388). Heidelberg, Germany: Springer-Verlag.
25. Bayley, M. J., & Willett, P. (1999). Binning schemes for partition-based compound selection. Journal of Molecular Graphics and Modelling, 17, 10-18.
26. Beitzel, S. M., Jensen, E. C., Chowdhury, A., Grossman, D., Goharian, N., & Frieder, O. (2004). Fusion of effective retrieval strategies in the same information retrieval system. Journal of the American Society for Information Science and Technology, 55, 859-868.
27. Belkin, N. J., Kantor, P., Fox, E. A., & Shaw, J. B. (1995). Combining the evidence of multiple query representations for information retrieval. Information Processing & Management, 31, 431-448.
28. Ben-Dor, A., Shamir, R., & Yakhini, Z. (1999). Clustering gene expression patterns. Journal of Computational Biology, 6, 281-297.
29. Bender, A., & Glen, R. C. (2004). Molecular similarity: A key technique in molecular informatics. Organic and Biomolecular Chemistry, 2, 3204-3218.
30. Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004). Molecular similarity searching using atom environments: Information-based feature selection and a naive Bayesian classifier. Journal of Chemical Information and Computer Sciences, 44, 170-178.
31. Berks, A. H. (2001). Current state of the art of Markush topological search systems. World Patent Information, 23, 5-13.
32. Berman, H. M., Battistuz, T., Bhat, T. N., Blum, W. F., Bourne, P. E., Burkhardt, K., et al. (2002). The Protein Data Bank. Acta Crystallographica, D58, 899-907.
33. Blair, J., Gasteiger, J., Gillespie, C., Gillespie, P. D., & Ugi, I. (1974). Representation of the constitutional and stereochemical features of chemical systems in the computer-assisted design of syntheses. Tetrahedron, 30, 1845-1859.
34. Boecker, A., Derksen, S., Schmidt, E., Teckentrup, A., & Schneider, G. (2005). A hierarchical clustering approach for large compound libraries. Journal of Chemical Information and Modeling, 45, 807-815.
35. Bohl, M., Dunbar, J., Gifford, E. M., Heritage, T., Wild, D. J., Willett, P., et al. (2002). Scaffold searching: Automated identification of similar ring systems for the design of combinatorial libraries. Quantitative Structure-Activity Relationships, 21, 590-597.
36. Bohl, M., Wendt, B., Heritage, T. H., Richmond, N., & Willett, P. (2006). Unsupervised 3D ring template searching as an ideas generator for scaffold hopping by LAMDA, RigFit and Field-Based Similarity Search (FBSS) methods. Journal of Chemical Information and Modeling, 46, 1882-1890.
37. Böhm, H.-J. (1992). The computer-program LUDI: A new method for the de novo design of enzyme-inhibitors. Journal of Computer-Aided Molecular Design, 6, 61-78.
38. Böhm, H.-J., Flohr, A., & Stahl, M. (2004). Scaffold hopping. Drug Discovery Today: Technologies, 1, 217-224.
39. Böhm, H.-J., & Schneider, G. (Eds.). (2000). Virtual screening for bioactive molecules. Weinheim, Germany: Wiley-VCH.
40. Bostrom, J., Hogner, A., & Schmitt, S. (2006). Do structurally similar ligands bind in a similar fashion? Journal of Medicinal Chemistry, 49, 6716-6725.
41. Bottle, R. T., & Rowland, J. F. B. (Eds.). (1993). Information sources in chemistry (4th ed.). London: Bowker-Saur.
42. Boyd, D., & Marsh, M. M. (2006). History of computers in pharmaceutical research and development: A narrative. In S. Ekins (Ed.), Computer applications in pharmaceutical research and development (pp. 3-50). Hoboken, NJ: Wiley.
43. Bradshaw, J. (1997). Introduction to Tversky similarity measure. Retrieved July 22, 2007, from www.daylight.com/meetings/mug97/Bradshaw/MUG97/tv- tversky.html
44. Bradshaw, J., & Sayle, R. A. (1997). Some thoughts on significant similarity and sufficient diversity. Retrieved July 22, 2007, from www.daylight.com/meetings/emug97/Bradshaw/ Significant-Similarity/Significant- Similarity.html
45. Breiman, L. (2001). Random forests. Machine Learning, 36, 5-32.
46. Bremser, W., Klier, M., & Meyer, E. F. (1975). Mutual assignment of subspectra and substructures: A way to structure elucidation by 13C-NMR spectroscopy. Organic Magnetic Resonance, 7, 97-105.
47. Briem, H., & Lessel, U. F. (2000). In vitro and in silico affinity fingerprints: Finding similarities beyond structural classes. Perspectives in Drug Discovery and Design, 20, 231-244.
48. Brint, A. T., & Willett, P. (1987). Algorithms for the identification of three-dimensional maximal common substructures. Journal of Chemical Information and Computer Sciences, 27, 152-158.
49. Brown, F. K. (1998). Chemoinfbrmatics: What is it and how does it impact drug discovery? Annual Reports in Medicinal Chemistry, 33, 375-384.
50. Brown, N., & Jacoby, E. (2006). On scaffolds and hopping in medicinal chemistry. MiniReviews in Medicinal Chemistry, 6, 1217-1229.
51. Brown, N., Willett, P., Wilton, D. J., & Lewis, R. A. (2003). Generation and display of activityweighted chemical hyperstructures. Journal of Chemical Information and Computer Sciences, 43, 288-297.
52. Brown, R. D., Hassan, M., & Waldman, M. (2000). Combinatorial library design for diversity, cost efficiency and druglike character. Journal of Molecular Graphics and Modelling, 18, 427-437.
53. Brown, R. D., & Martin, Y. C. (1996). Use of structure-activity data to compare structurebased clustering methods and descriptors for use in compound selection. Journal of Chemical Information and Computer Sciences, 36, 572-584.
54. Brown, R. D., & Martin, Y. C. (1997a). Designing combinatorial library mixtures using a genetic algorithm. Journal of Medicinal Chemistry, 40, 2304-2313.
55. Brown, R. D., & Martin, Y. C. (1997b). The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. Journal of Chemical Information and Computer Sciences, 37, 1-9.
56. Brown, R. D., & Martin, Y. C. (1998). An evaluation of structural descriptors and clustering methods for use in diversity selection. SAR and QSAR in Environmental Research, 8, 23-39.
57. Bunin, B. A., Bajorath, J., Siesel, B., & Morales, G. (2007). Chemoinformatics: Theory, practice, & products. Dordrecht, The Netherlands: Springer.
58. Burbridge, R., Trotter, M., Buxton, B., & Holden, S. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computers and Chemistry, 26, 5-14.
59. Butina, D. (1999). Unsupervised data base clustering based on Daylight's fingerprint, Tanimoto similarity: A fast and automated way to cluster small and large data sets. Journal of Chemical Information and Computer Sciences, 39, 747-750.
60. Byvatov, E., Fechner, U., Sadowski, J., & Schneider, G. (2003). Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. Journal of Chemical Information and Computer Sciences, 43, 1882-1889.
61. Cannon, E. O., Bender, A., Palmer, D. S., & Mitchell, J. B. O. (2006). Chemoinformatics-based classification of prohibited substances employed for doping in sport. Journal of Chemical Information and Modeling, 46, 2369-2380.
62. Capelli, A. M., Feriani, A., Tedesco, G., & Pozzan, A. (2006). Generation of a focused set of GSK compounds biased toward ligand-gated ion-channel ligands. Journal of Chemical Information and Modeling, 46, 659-664.
63. Carbó, R., Leyda, L., & Arnau, M. (1980). How similar is a molecule to another? An electron density measure of similarity between two molecular structures. International Journal of Quantum Chemistry, 17, 1185-1189.
64. Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985). Atom pairs as molecularfeatures in structure activity studies-definition and applications. Journal of Chemical Information and Computer Sciences, 25, 64-73.
65. Charifsen, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999). Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of Medicinal Chemistry, 42, 5100-5109.
66. Cheeseright, T., Mackey, M., Rose, S., & Vinter, A. (2006). Molecular field extrema as descriptors of biological activity: Definition and validation. Journal of Chemical Information and Modeling, 46, 6650-6676.
67. Chen, B., Harrison, R. F., Pasupa, K., Wilton, D. J., Willett, P., Wood, D. J., et al. (2006). Virtual screening using binary kernel discrimination: Effect of noisy training data and the optimization of performance. Journal of Chemical Information and Modeling, 46, 478-486.
68. Chen, L., Nourse, J. G., Christie, B. D., Leland, B. A., & Grier, D. L. (2002). Over 20 years of reaction access systems from MDL: A novel reaction substructure search algorithm. Journal of Chemical Information and Computer Sciences, 42, 1296-1310.
69. Chen, W. L. (2006). Chemoinformatics: Past, present and future. Journal of Chemical Information and Modeling, 46, 2230-2255.
70. Chen, X., & Brown, F. K. (2006). Asymmetry of chemical similarity. ChemMedChem, 2, 180-182.
71. Chen, X., & Reynolds, C. H. (2002). Performance of similarity measures in 2D fragmentbased similarity searching: Comparison of structural descriptors and similarity coefficients. Journal of Chemical Information and Computer Sciences, 42, 1407-1414.
72. Chen, X., Rusinko,A.,&Young, S. S. (1998). Recursive partitioning analysis of a large structure-activity data set using three-dimensional descriptors. Journal of Chemical Information and Computer Sciences, 38, 1054-1062.
73. Cheng, C., Maggiora, G., Lajiness, M., & Johnson, M. A. (1996). Four association coefficients for relating molecular similarity measures. Journal of Chemical Information and Computer Sciences, 36, 909-915.
74. Christianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.
75. Clark, D. E. (2005). Computational prediction of ADMET properties: Recent developments and future changes. Annual Reports in Computational Chemistry, 1, 133-151.
76. Clark, D. E., & Grootenhuis, P. D. J. (2002). Progress in computational methods for the prediction of ADMET properties. Current Opinion in Drug Discovery & Development, 5, 382-390.
77. Clark, D. E., Willett, P., & Kenny, P. W. (1992). Pharmacophore pattern matching in files of 3D chemical structures: Use of smoothed bounded-distance matrices for the representation and searching of conformationally-flexible molecules. Journal of Molecular Graphics, 10, 194-204.
78. Clark, R. D. (1997). OptiSim: An extended dissimilarity selection method for finding diverse representative subsets. Journal of Chemical Information and Computer Sciences, 37, 1181-1188.
79. Clark, R. D., & Langton, W. J. (1998). Balancing representativeness against diversity using optimizable k-dissimilarity and hierarchical clustering. Journal of Chemical Information and Computer Sciences, 38, 1079-1086.
80. Clark, R. D., Strizhev, A., Leonard, J. M., Blake, J. F., & Matthew, J. B. (2002). Consensus scoring for ligand/protein interactions. Journal of Molecular Graphics and Modelling, 20, 281-295.
81. Clark, T. (2004). A handbook of computational chemistry: A practical guide to chemical structure and energy calculations (2nd edition). Chichester, UK: Wiley.
82. Cleves, A. E., & Jain, A. N. (2006). Robust ligand-based modeling of the biological targets of known drugs. Journal of Medicinal Chemistry, 49, 2921-2938.
83. Cole, J. C., Murray, C. W., Nissink, J. W. M., Taylor, R. D., & Taylor, R. (2005). Comparing protein-ligand docking programs is difficult. Proteins, 60, 325-332.
84. Coles, S. J., Day, N. E., Murray-Rust, P., Rzepa, H. S., & Zha, Y (2005). Enhancement of the chemical semantic web through the use of InChI identifiers. Organic and Biomolecular Chemistry, 3, 1832-1834.
85. Congreve, M., Murray, C. W., & Blundell, T. L. (2005). Structural biology and drug discovery. Drug Discovery Today, 10, 895-907.
86. Corey, E. J., & Wipke, W. T. (1969). Computer-assisted design of complex organic syntheses. Science, 166, 178-193.
87. Corey, E. J., Wipke, W. T., Cramer, R. D., & Howe, W. J. (1972). Computer-assisted synthetic analysis: Facile man-machine communication of chemical structure by interactive computer graphics. Journal of the American Chemical Society, 94, 421-431.
88. Cornell, W. D. (2006). Recent evaluations of high-throughput docking methods for pharmaceutical lead finding: Consensus and caveats. Annual Reports in Computational Chemistry, 2, 297-323.
89. Cosgrove, D. A., & Willett, P. (1998). SLASH: A program for analysing the functional groups in molecules. Journal of Molecular Graphics and Modelling, 16, 19-32.
90. Cramer, C. J. (2004). Essentials of computational chemistry: Theories and models (2nd ed.). Hoboken, NJ: Wiley.
91. Cramer, R. D., DePriest, S. A., Patterson, D. E., & Hecht, P. (1993). The developing practice of Comparative Molecular Field Analysis. In H. Kubinyi (Ed.), 3D QSAR in drug design: Theory, methods and applications (pp. 443-485). Leiden, The Netherlands: ESCOM Science Publishers.
92. Cramer, R. D., Jilek, R. J., Guessregen, S., Clark, S. J., Wendt, B., & Clark, R. D. (2004). "Lead hopping": Validation of topomer similarity as a superior predictor of similar biological activities. Journal of Medicinal Chemistry, 47, 6777-6791.
93. Cramer, R. D., Patterson, D. E., & 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.
94. Cramer, R. D., Redl, G., & Berkoff, C. E. (1974). Substructural analysis: A novel approach to the problem of drug design. Journal of Medicinal Chemistry, 17, 533-535.
95. Cramer, R. D., & Wendt, B. (2007). Pushing the boundaries of 3D-QSAR. Journal of Computer-Aided Molecular Design, 21, 23-32.
96. Crandell, C. W., & Smith, D. H. (1983). Computer-assisted examination of compounds for common three-dimensional substructures. Journal of Chemical Information and Computer Sciences, 23, 186-197.
97. Croft, W. B. (2000). Combining approaches to information retrieval. In W. B. Croft (Ed.), Advances in information retrieval (pp. 1-36). Dordrecht, The Netherlands: Kluwer.
98. Cruciani, G., Pastor, M., & Mannhold, R. (2002). Suitability of molecular descriptors for database mining: A comparative analysis. Journal of Medicinal Chemistry, 45, 2685-2694.
99. Cuissart, B., Touffet, F., Crémilleux, B., Bureau, R., & Rault, S. (2002). The maximum common substructure as a molecular depiction in a supervised classification context: Experiments in quantitative structure/biodegradability relationships. Journal of Chemical Information and Computer Sciences, 42, 1043-1052.
100. Dalby, A., Nourse, J. G., Hounshell, W. D., Gushurst, A. K. I., Grier, D. L., Leland, B. A., et al. (1992). Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. Journal of Chemical Information and Computer Sciences, 32, 244-255.
101. Danziger, D. J., & Dean, P. M. (1989). Automated site-directed drug design: A general algorithm for knowledge acquisition about hydrogen-bonding regions at protein surfaces. Proceedings of the Royal Society B, 236, 101-113.
102. Davis, C. H., & McKim, G. W. (1999). Systematic weighting and ranking: Cutting the Gordian knot. Journal of the American Society for Information Science, 50, 626-628.
103. Dean, P. M. (Ed.). (1994). Molecular similarity in drug design. Glasgow, UK: Chapman and Hall.
104. Dean, P. M., & Lewis, R. A. (Eds.). (1999). Molecular diversity in drug design. Amsterdam: Kluwer.
105. DesJarlais, R. L., Seibel, G. L., Kuntz, I. D., Fürth, P. S., Alvarez, J. C., de Montellano, P. R. O., et al. (1990). Structure-based design of nonpeptidic inhibitors specific for the human immunodeficiency virus 1 protease. Proceedings of the National Academy of Sciences (USA), 87, 6644-6648.
106. Diestel, R. (2000). Graph theory. New York: Springer-Verlag.
107. DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22, 151-185.
108. Dittmar, P. G., Farmer, N. A., Fisanick, W., Haines, R. C., & Mockus, J. (1983). The CAS Online search system I: General system design and selection, generation and use of search screens. Journal of Chemical Information and Computer Sciences, 23, 93-102.
109. Dixon, S. L., & Koehler, R. T. (1999). The hidden component of size in two-dimensional fragment descriptors: Side effects on sampling in bioactive libraries. Journal of Medicinal Chemistry, 42, 2887-2900.
110. Dixon, S. L., & Merz, K. M. (2001). One-dimensional molecular representations and similarity calculations: Methodology and validation. Journal of Medicinal Chemistry, 44, 3795-3809.
111. Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006). PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening 1: Methodology and preliminary results. Journal of Computer-Aided Molecular Design, 20, 647-671.
112. Dixon, S. L., & Villar, H. O. (1998). Bioactive diversity and screening library selection via affinity fingerprinting. Journal of Chemical Information and Computer Sciences, 38, 1192-1203.
113. Doman, T. N., Cibulskis, J. M., Cibulskis, M. J., McCray, P. D., & Spangler, D. P. (1996). Algorithm5: A technique for fuzzy similarity clustering of chemical inventories. Journal of Chemical Information and Computer Sciences, 36, 1195-1204.
114. Doweyko, A. M. (2004). 3D-QSAR illusions. Journal of Computer-Aided Molecular Design, 18, 587-596.
115. Downie, J. S. (2003). Music information retrieval. Annual Review of Information Science and Technology, 37, 295-340.
116. Downs, G. M., & Barnard, J. M. (2002). Clustering methods and their uses in computational chemistry. Reviews in Computational Chemistry, 18, 1-40.
117. Downs, G. M., & Willett, P. (1994). Clustering of chemical-structure databases for compound selection. In H. van de Waterbeemd (Ed.), Advanced computer-assisted techniques in drug discovery (pp. 111-130). New York: VCH.
118. Downs, G. M., & Willett, P. (1995). Similarity searching in databases of chemical structures. Reviews in Computational Chemistry, 7, 1-66.
119. Eckert, H., & Bajorath, J. (2006). Determination and mapping of activity-specific descriptor value ranges for the identification of active compounds. Journal of Medicinal Chemistry, 49, 2284-2293.
120. Eckert, H., & Bajorath, J. (2007). Molecular similarity analysis in virtual screening: Foundations, limitation and novel approaches. Drug Discovery Today, 12, 225-233.
121. Edgar, S. J., Holliday, J. D., & Willett, P. (2000). Effectiveness of retrieval in similarity searches of chemical databases: A review of performance measures. Journal of Molecular Graphics and Modelling, 18, 343-357.
122. Egan, W. J., Walters, W. P., & Murcko, M. A. (2002). Guiding molecules towards drug-likeness. Current Opinion in Drug Discovery & Development, 5, 540-549.
123. Ekins, S. (Ed.). (2006). Computer applications in pharmaceutical research and development. Hoboken, NJ: Wiley-Interscience.
124. El-Hamdouchi, A., & Willett, P. (1989). Comparison of hierarchic agglomerative clustering methods for document retrieval. Computer Journal, 32, 220-227.
125. Engels, M. F. M., Gibbs, A. C., Jaeger, E. P., Verbinnen, D., Lobanov, V. S., & Agrafiotis, D. K. (2006). A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition. Journal of Chemical Information and Modeling, 46, 2651-2660.
126. Erickson, J. A., Jalaie, M., Robertson, D. H., Lewis, R. A., & Vieth, M. (2004). Lessons in molecular recognition: The effects of ligand and protein flexibility on molecular docking accuracy. Journal of Medicinal Chemistry, 47, 45-55.
127. Estrada, E., & Uriarte, E. (2001). Recent advances on the use of topological indices in drug discovery research. Current Medicinal Chemistry, 8, 1573-1588.
128. Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London: Edward Arnold.
129. Ewing, T. J. A., Baber, J. C., & Feher, F. (2006). Novel 2D fingerprints for ligand-based virtual screening. Journal of Chemical Information and Modeling, 46, 2423-2431.
130. Ewing, T. J. A., Makino, S., Skillman, A. G., & Kuntz, I. D. (2001). Dock 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 15, 411-428.
131. Feher, M. (2006). Consensus scoring for protein-ligand interactions. Drug Discovery Today, 11, 421-428.
132. Feldman, A., & Hodes, L. (1975). An efficient design for chemical structure searching I: The screens. Journal of Chemical Information and Computer Sciences, 15, 147-152.
133. Fetchner, U., Paetz, J., & Schneider, G. (2005). Comparison of three holographic fingerprint descriptors and their binary counterparts. QSAR and Combinatorial Science, 24, 961-967.
134. Fisanick, W. (1990). The Chemical Abstracts Service generic chemical (Markush) storage and retrieval capability, part 1: Basic concepts. Journal of Chemical Information and Computer Sciences, 30, 145-155.
135. Fligner, M.A., Verducci, J. S., & Blower, P. E. (2002). A modification of the Jaccard-Tanimoto similarity index for diverse selection of chemical compounds using binary strings. Technometrics, 44, 110-119.
136. Flower, D. R. (1988). On the properties of bit string based measures of chemical similarity. Journal of Chemical Information and Computer Sciences, 38, 379-386.
137. Free, S. M., & Wilson, J. W. (1964). A mathematical contribution to structure-activity studies. Journal of Medicinal Chemistry, 7, 395-399.
138. Freeland, R., Funk, S., O'Korn, L., & Wilson, G. (1979). The Chemical Abstracts Service Chemical Registry System II: Augmented Connectivity Molecular Formula. Journal of Chemical Information and Computer Sciences, 19, 94-98.
139. Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., et al. (2004). GLIDE: A new approach for rapid, accurate docking and scoring 1: Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47, 1739-1749.
140. Frimurer, T. M., Bywater, R., Naerum, L., Lauritsen, L. N., & Brunak, S. (2000). Improving the odds in discriminating "drug-like" from non "drug-like" compounds. Journal of Chemical Information and Computer Sciences, 40, 1315-1324.
141. Gasteiger, J. (Ed.). (2003). Handbook of chemoinformatics. Weinheim, Germany: Wiley-VCH.
142. Gasteiger, J., & Engel, T. (Eds.). (2003). Chemoinformatics: A textbook. Weinheim, Germany: Wiley-VCH.
143. Gasteiger, J., & Jochum, C. (1978). EROS: A computer program for generating sequences of reactions. Topics in Current Chemistry, 74, 93-126.
144. Gedeck, P., Rhode, B., & Bartels, C. (2006). QSAR: How good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. Journal of Chemical Information and Modeling, 46, 1924-1936.
145. Ghose, A. K., & Viswanadhan, V. N. (Eds.). (2001). Combinatorial library design and evaluation: Principles, software tools and applications in drug discovery. New York: Dekker.
146. Gillet, V. J. (2004). Designing combinatorial libraries optimized on multiple objectives. Methods in Molecular Biology, 275, 335-354.
147. Gillet, V. J., & Johnson, A. P. (1998). Structure generation for de novo design. In Y. C. Martin & P. Willett (Eds.), Designing bioactive molecules: Three-dimensional techniques and applications (pp. 149-174). Washington, DC: American Chemical Society.
148. Gillet, V. J., Johnson, A. P., Mata, P., & Sike, S. (1990). Automated structure design in 3D. Tetrahedron Computer Methodology, 3, 681-696.
149. Gillet, V. J., Khatib, W., Willett, P., Fleming, P. J., & Green, D. V. S. (2002). Combinatorial library design using a multiobjective genetic algorithm. Journal of Chemical Information and Computer Sciences, 42, 375-385.
150. Gillet, V. J., Willett, P., & Bradshaw, J. (1997). The effectiveness of reactant pools for generating structurally-diverse combinatorial libraries. Journal of Chemical Information and Computer Sciences, 37, 731-740.
151. Gillet, V. J., Willett, P., & Bradshaw, J. (1998). Identification of biological activity profiles using substructural analysis and genetic algorithms. Journal of Chemical Information and Computer Sciences, 38, 165-179.
152. Gillet, V. J., Willett, P., & Bradshaw, J. (2003). Similarity searching using reduced graphs. Journal of Chemical Information and Computer Sciences, 43, 338-345.
153. Gillet, V. J., Willett, P., Bradshaw, J., & Green, D. V. S. (1999). Selecting combinatorial libraries to optimize diversity and physical properties. Journal of Chemical Information and Computer Sciences, 39, 169-177.
154. Ginn, C. M. R., Turner, D. B., Willett, P., Ferguson, A. M., & Heritage, T. W. (1997). Similarity searching in files of three-dimensional chemical structures: Evaluation of the EVA descriptor and combination of rankings using data fusion. Journal of Chemical Information and Computer Sciences, 37, 23-37.
155. Ginn, C. M. R., Willett, P., & Bradshaw, J. (2000). Combination of molecular similarity measures using data fusion. Perspectives in Drug Discovery and Design, 20, 1-16.
156. Glen, R. C., & Adams, S. E. (2006). Similarity metrics and descriptor spaces: Which combinations to choose? QSAR and Combinatorial Science, 25, 1133-1142.
157. Godden, J. W., Furr, J. R., Xue, L., Stahura, F. L., & Bajorath, J. (2004). Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality. Journal of Chemical Information and Computer Sciences, 44, 21-29.
158. Godden, J. W., Stahura, F. L., & Bajorath, J. (2004). POT-DMC: A virtual screening method for the identification of potent hits. Journal of Medicinal Chemistry, 47, 5608-5611.
159. Godden, J. W., Xue, L., & Bajorath, J. (2000). Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. Journal of Chemical Information and Computer Sciences, 40, 163-166.
160. Godden, J. W., Xue, L., Kitchen, D. B., Stahura, F. L., Schermerhorn, E. J., & Bajorath, J. (2002). Median partitioning: A novel method for the selection of representative subsets from large compound pools. Journal of Chemical Information and Computer Sciences, 42, 885-893.
161. Goldman, B. B., & Walters, W. P. (2006). Machine learning in computational chemistry. Annual Reports in Computational Chemistry, 2, 127-140.
162. Good, A. C., Cho, S. J., & Mason, J. S. (2004). Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening. Journal of Computer-Aided Molecular Design, 18, 523-527.
163. Good, A. C., Hermsmeier, M. A., & Hindle, S. A. (2004). Measuring CAMD technique performance: A virtual screening case study in the design of validation experiments. Journal of Computer-Aided Molecular Design, 18, 529-536.
164. Good, A. C., Hodgkin, E. E., & Richards, W. G. (1992). Utilization of Gaussian functions for the rapid evaluation of molecular similarity. Journal of Chemical Information and Computer Sciences, 32, 188-191.
165. Good, A. C., & Lewis, R. A. (1997). New methodology for profiling combinatorial libraries and screening sets: Cleaning up the design with HARPick. Journal of Medicinal Chemistry, 40, 3926-3936.
166. Good, A. C., & Richards, W. G. (1993). Rapid evaluation of shape similarity using Gaussian functions. Journal of Chemical Information and Computer Sciences, 33, 112-116.
167. Gorse, A.-D. (2006). Diversity in medicinal chemistry space. Current Topics in Medicinal Chemistry, 6, 3-18.
168. Graf, W., Kaindl, H. K., Kniess, H., Schmidt, B., & Warszawski, R. (1979). Substructure retrieval by means of the BASIC Fragment Search Dictionary based on the Chemical Abstracts Service Chemical Registry III System. Journal of Chemical Information and Computer Sciences, 19, 51-55.
169. Grant, J. A., Gallardo, M. A., & Pickup, B. T. (1996). A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape. Journal of Computational Chemistry, 17, 1653-1666.
170. Gray, N. A. B. (1986). Computer assisted structure elucidation. New York: Wiley.
171. Greco, G., Novellino, E., & Martin, Y. C. (1998). 3D-QSAR methods. In Y. C. Martin & P. Willett (Eds.), Designing bioactive molecules: Three-dimensional techniques and applications. Washington, DC: American Chemical Society.
172. Green, D. V. S. (1998). Automated three-dimensional structure generation. In Y. C. Martin & P. Willett (Eds.), Designing bioactive molecules: Three-dimensional techniques and applications (pp. 47-71). Washington, DC: American Chemical Society.
173. Griffiths, A., Robinson, L. A., & Willett, P. (1984). Hierarchic agglomerative clustering methods for automatic document classification. Journal of Documentation, 40, 175-205.
174. Gund, P. (1977). Three-dimensional pharmacophoric pattern searching. Progress in Molecular and Subcellular Biology, 5, 117-143.
175. Gund, P., Andose, J. D., Rhodes, J. B., & Smith, G. M. (1980). Three-dimensional molecular modeling and drug design. Science, 208, 1425-1431.
176. Gund, P., Wipke, W. T., & Langridge, R. (1974, July). Computer searching of a molecular structure file for pharmacophoric patterns. Paper presented at the International Conference on Computers in Chemical Research and Education, Ljubljana, Slovenia.
177. Güner, O. (Ed.). (2000). Pharmacophore perception, development and use in drug design. La Jolla, CA: International University Line.
178. Hagadone, T. R. (1992). Molecular substructure similarity searching: Efficient retrieval intwo-dimensional structure databases. Journal of Chemical Information and Computer Sciences, 32, 515-521.
179. Haigh, J. A., Pickup, B. T., Grant, J. A., & Nicholls, A. (2005). Small molecule shape-fingerprints. Journal of Chemical Information and Modeling, 45, 673-684.
180. Hall, D. L. (1992). Mathematical techniques in multisensor data fusion. Northwood, MA: Artech House.
181. Hann, M., & Green, R. (1999). Chemoinformatics: A new name for an old problem? Current Opinion in Chemical Biology, 3, 379-383.
182. Hann, M., Leach, A. R., & Harper, G. (2001). Molecular complexity and its impact on the probability of finding leads for drug discovery. Journal of Chemical Information and Computer Sciences, 41, 856-864.
183. Hansch, C., & Fujita, T. (1964). Rho sigma pi analysis: A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86, 1616-1626.
184. Hansch, C., Hoekman, D., Leo, A., Weininger, D., & Selassie, C. D. (2002). Chem-bioinformatics: Comparative QSAR at the interface between chemistry and biology. Chemical Reviews, 102, 783-812.
185. Hansch, C., & Leo, A. (1995). Exploring QSAR: Fundamentals and applications in chemistry and biology. Washington, DC: American Chemical Society.
186. Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature, 194, 178-180.
187. Harper, G., Bradshaw, J., Gittins, J. C., Green, D. V. S., & Leach, A. R. (2001). Prediction of biological activity for high-throughput screening using binary kernel discrimination. Journal of Chemical Information and Computer Sciences, 41, 1295-1300.
188. Harper, G., Bravi, G. S., Pickett, S. D., Hussain, J., & Green, D. V. S. (2004). The reduced graph descriptor in virtual screening and data-driven clustering of high-throughput screening data. Journal of Chemical Information and Computer Sciences, 44, 2145-2156.
189. Hassan, M., Bielawski, J. P., Hempel, J. C., & Waldman, M. (1996). Optimization and visualization of molecular diversity of combinatorial libraries. Molecular Diversity, 2, 64-74.
190. Hassan, M., Brown, R. D., Varma-O'Brien, S., & Rogers, D. (2006). Cheminformatics analysis and learning in a data pipelining environment. Molecular Diversity, 10, 283-299.
191. Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44, 1-12.
192. Hawkins, D. M., Young, S. S., & Rusinko, A. (1997). Analysis of a large structure-activity data set using recursive partitioning. Quantitative Structure-Activity Relationships, 16, 296-302.
193. Hawkins, P. D. C., Skillman, A. G., & Nicholls, A. (2007). Comparison of shape-matching and docking as virtual screening tools. Journal of Medicinal Chemistry, 50, 74-82.
194. He, L., & Jurs, P. C. (2005). Assessing the reliability of a QSAR model's predictions. Journal of Molecular Graphics and Modelling, 23, 503-523.
195. Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., et al. (2004a). Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. Journal of Chemical Information and Computer Sciences, 44, 1177-1185.
196. Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., et al. (2004b). Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Organic and Biomolecular Chemistry, 2, 3256-3266.
197. Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., et al. (2005). Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. Journal of Medicinal Chemistry, 48, 7049-7054.
198. Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., et al. (2006). New methods for ligand-based virtual screening: Use of data-fusion and machine-learning techniques to enhance the effectiveness of similarity searching. Journal of Chemical Information and Computer Sciences, 46, 462-470.
199. Hertzberg, R. P., & Pope, A. J. (2000). High-throughput screening: New technology for the 21st century. Current Opinion in Chemical Biology, 4, 445-451.
200. Hessler, G., Zimmermann, M., Matter, H., Evers, A., Naumann, T., Lengauer, T., et al. (2005). Multiple-ligand-based virtual screening: Methods and applications of the MTREE approach. Journal of Medicinal Chemistry, 48, 6575-6584.
201. Hiller, C., & Gasteiger, J. (1987). Ein automatisierter Molekülbaukasten. In J. Gasteiger (Ed.), Software-entwicklung in der Chemie 1 (pp. 53-66). Berlin, Germany: Springer Verlag.
202. Hinchcliffe, A. (2003). Molecular modelling for beginners. Chichester, UK: Wiley.
203. Holliday, J. D., Hu, C.-Y., & Willett, P. (2002). Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Combinatorial Chemistry and High-Throughput Screening, 5, 155-166.
204. Holliday, J. D., Rodgers, S. L., Willett, P., Chen, M.-Y., Mahfouf, M., Lawson, K., et al. (2004). Clustering files of chemical structures using the fuzzy k-means clustering method. Journal of Chemical Information and Computer Sciences, 44, 894-902.
205. Holliday, J. D., Salim, N., Whittle, M., & Willett, P. (2003). Analysis and display of the size dependence of chemical similarity coefficients. Journal of Chemical Information and Computer Sciences, 43, 819-828.
206. Holliday, J. D., Salim, N., & Willett, P. (2005). On the magnitudes of coefficient values in the calculation of chemical similarity and dissimilarity. American Chemical Society Symposium Series, 894, 77-95.
207. Hsu, D. F., & Taksa, I. (2005). Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval, 8, 449-480.
208. Huang, N., Shoichet, B. K., & Irwin, J. J. (2006). Benchmarking sets for molecular docking. Journal of Medicinal Chemistry, 49, 6789-6801.
209. Hudson, B. D., Hyde, R. M., Rahr, E., & Wood, J. (1996). Parameter based methods for compound selection from chemical databases. Quantitative Structure-Activity Relationships, 15, 285-289.
210. Hurst, T. (1994). Flexible 3D searching: The directed tweak technique. Journal of Chemical Information and Computer Sciences, 34, 190-196.
211. Huser, J. (Ed.). (2006). High-throughput screening in drug discovery. Weinheim, Germany: Wiley-VCH.
212. Hyland, R., Jones, B., & van de Waterbeemd, H. (2006). Utility of human/human-derived reagents in drug discovery and development: An industrial perspective. Environmental Toxicology and Pharmacology, 21, 179-183.
213. InfoChem GmbH (2007). CLASSIFY. The InfoChem Reaction Classification Program. Retrieved December 21, 2007, from www.infochem.de/content/ downloads/classify.pdf
214. International Union of Pure and Applied Chemistry. (2007). The IUPAC International Chemical Identifier (InChl™). Retrieved December 21, 2007, from www.iupac.org/inchi
215. Jacoby, E. (Ed.). (2006). Chemogenomics. Knowledge-based approaches to drug discovery. London: Imperial College Press.
216. Jakes, S. E., Watts, N., Willett, P., Bawden, D., & Fisher, J. D. (1987). Pharmacophoric pattern-matching in files of 3D chemical structures: Evaluation of search performance. Journal of Molecular Graphics, 5(1), 41-48.
217. Jakes, S. E., & Willett, P. (1986). Pharmacophoric pattern-matching in files of 3D chemical structures: Selection of interatomic distance screens. Journal of Molecular Graphics, 4, 12-20.
218. Jarvis, R. A., & Patrick, E. A. (1973). Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers, C-22, 1025-1034.
219. Johnson, M. A., & Maggiora, G. M. (Eds.). (1990). Concepts and applications of molecular similarity. New York: Wiley.
220. Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267, 727-748.
221. Jorissen, R. N., & Gilson, M. K. (2005). Virtual screening of molecular databases using a support vector machine. Journal of Chemical Information and Computer Sciences, 45, 549-561.
222. Kauvar, L. M., Higgins, D. L., Villar, H. O., Sportsman, J. R., Engqvist-Goldstein, A., Bukar, R., et al. (1995). Predicting ligand binding to proteins by affinity fingerprinting. Chemistry & Biology, 2, 107-118.
223. Kearsley, S. K., Sallamack, S., Fluder, E. M., Andose, J. D., Mosley, R. T., & Sheridan, R. P. (1996). Chemical similarity using physicochemical property descriptors. Journal of Chemical Information and Computer Sciences, 36, 118-127.
224. Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K. (2007). Relating protein pharmacology by ligand chemistry. Nature Biotechnology, 25, 197-206.
225. Kelley, L. A., Gardner, S. P., & Sutcliffe, M. J. (1996). An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally-related subfamilies. Protein Engineering, 9, 1063-1065.
226. Kier, L. B., & Hall, H. L. (1986). Molecular connectivity in structure-activity analysis. New York: Wiley.
227. Klebe, G. (Ed.). (2000). Virtual screening: An alternative or complement to high throughput screening. Dordrecht, The Netherlands: Kluwer.
228. Klein, C. T., Kaiser, D., Kopp, S., Chiba, P., & Ecker, G. F. (2002). Similarity based SAR (SIBAR) as tool for early ADME profiling. Journal of Computer-Aided Molecular Design, 16, 785-793.
229. Klein, L. A. (1999). Sensor and data fusion concepts and applications (2nd ed.). Bellingham, WA: SPIE Optical Engineering Press.
230. Kogej, T., Engkvist, O., Blomberg, N., & Muresan, S. (2006). Multifingerprint based similarity searches for targeted class compound selection. Journal of Chemical Information and Modeling, 46, 1201-1213.
231. Kubinyi, H. (1997a). QSAR and 3D QSAR in drug design, Part 1. Drug Discovery Today, 2, 457-467.
232. Kubinyi, H. (1997b). QSAR and 3D QSAR in drug design, Part 2. Drug Discovery Today, 2, 538-546.
233. Kubinyi, H. (1998). Similarity and dissimilarity: A medicinal chemist's view. Perspectives in Drug Discovery and Design, 9-11, 225-232.
234. Kubinyi, H., Folkers, G., & Martin, Y. C. (Eds.). (1998). 3D QSAR in drug design. Leiden, The Netherlands: Kluwer/ESCOM.
235. Kubinyi, H., & Muller, G. (Eds.). (2004). Chemogenomics in drug design. Weinheim, Germany: Wiley VCH.
236. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 161, 269-288.
237. Kurogi, Y., & Güner, O. F. (2001). Pharmacophore modeling and three-dimensional database searching for drug design using CATALYST. Current Medicinal Chemistry, 8, 1035-1055.
238. Lajiness, M. S. (1990). Molecular similarity-based methods for selecting compounds for screening. In D. H. Rouvray (Ed.), Computational chemical graph theory (pp. 299-316). New York: Nova Science Publishers.
239. Langridge, R., Ferrin, T. E., Kuntz, I. D., & Connolly, M. L. (1981). Real-time color graphics in studies of molecular interactions. Science, 211, 661-666.
240. Leach, A. R. (2001). Molecular modelling: Principles and applications (2nd ed.). Harlow, UK: Pearson Education.
241. Leach, A. R., & Gillet, V. J. (2003). An introduction to chemoinformatics. Dordrecht, The Netherlands: Kluwer.
242. Leach, A. R., Shoichet, B. K., & Peishoff, C. E. (2006). Prediction of protein-ligand interactions: Docking and scoring: Successes and gaps. Journal of Medicinal Chemistry, 49, 5851-5855.
243. Lee, J. H. (1997). Analyses of multiple evidence combination. Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 20, 267-276.
244. Leeson, P. D., & Springthorpe, B. (2007). The influence of drug-like concepts on decision-making in medicinal chemistry. Nature Reviews Drug Discovery, 6, 881-890.
245. Leiter, D. P., Morgan, H. L., & Stobaugh, R. E. (1965). Installation and operation of a registry for chemical compounds. Journal of Chemical Documentation, 5, 238-242.
246. Lemmen, C., & Lengauer, T. (2000). Computational methods for the structural alignment of molecules. Journal of Computer-Aided Molecular Design, 14, 215-232.
247. Lengauer, T., Lemmen, C., Rarey, M., & Zimmermann, M. (2004). Novel technologies for virtual screening. Drug Discovery Today, 9, 27-34.
248. Lesk, A. M. (2005). An introduction to bioinformatics (2nd ed.). Oxford, UK: Oxford University Press.
249. Lewis, R. A., Pickett, S. D., & Clark, D. E. (2000). Computer-aided molecular diversity analysis and combinatorial library design. Reviews in Computational Chemistry, 16, 1-51.
250. Lind, P., & Maltseva, T. (2003). Support vector machines for the estimation of aqueous solubility. Journal of Chemical Information and Computer Sciences, 43, 1855-1859.
251. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1980). Applications of artificial intelligence for organic chemistry: The DENDRAL project. New York: McGrawHill.
252. Lipinski, C. A. (2005). Filtering in drug discovery. Annual Reports in Computational Chemistry, 1, 155-168.
253. Lipinski, C. A., Lombardo, F., Dominy, B. W., & 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.
254. Lipscomb, K. J., Lynch, M. F., & Willett, P. (1989). Chemical structure processing. Annual Review of Information Science and Technology, 24, 189-238.
255. Loftus, F. (1991). Computer-aided synthesis design. In J. E. Ash, W. A. Warr, & P. Willett (Eds.), Chemical structure systems (pp. 222-262). Chichester, UK: Ellis Horwood.
256. Lombardino, J. G., & Lowe, J. A. (2004). The role of the medicinal chemist in drug discovery: Then and now. Nature Reviews Drug Discovery, 3, 853-862.
257. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2005). Geographic information systems and science. Chichester, UK: Wiley.
258. Low, C. M. R., Buck, I. M., Cooke, T., Cushnir, J. R., Kalindjian, S. B., Kotecha, A., et al. (2005). Scaffold hopping with molecular field points: Identification of a cholecystokinin2 (CCK2) receptor pharmacophore and its use in the design of a prototypical series of pyrrole- and imidazole-based CCK2 antagonists. Journal of Medicinal Chemistry, 48, 6790-6802.
259. Lynch, M. F. (1977). Variety generation: Are-interpretation of Shannon's mathematical theory of communication and its implications for information science. Journal of the American Society for Information Science, 28, 19-25.
260. Lynch, M. F., & Holliday, J. D. (1996). The Sheffield generic structures project: A retrospective review. Journal of Chemical Information and Computer Sciences, 36, 930-936.
261. Lynch, M. F., & Willett, P. (1987). Information retrieval research in the Department of Information Studies, University of Sheffield: 1965-1985. Journal of Information Science, 13, 221-234.
262. Lyne, P. D. (2002). Structure-based virtual screening: An overview. Drug Discovery Today, 7, 1047-1055.
263. Maggiora, G. M. (2006). On outliers and activity cliffs: Why QSAR often disappoints. Journal of Chemical Information and Modeling, 46, 1535.
264. Maggiora, G. M., Mestres, J., Hagadone, T. R., & Lajiness, M. S. (1997, April). Asymmetric similarity and molecular diversity. Paper presented at the 213th National Meeting of the American Chemical Society, San Francisco, CA.
265. Mahe, P., Ueda, N., Akutsu, T., Perret, J. L., & Vert, J. P. (2005). Graph kernels for molecular structure-activity relationship analysis with support vector machines. Journal of Chemical Information and Modeling, 45, 939-951.
266. Maizel, R. E. (1998). How to find chemical information (3rd ed.). New York: Wiley.
267. Makara, G. M. (2001). Measuring molecular similarity and diversity: Total pharmacophore diversity. Journal of Medicinal Chemistry, 44, 3563-3571.
268. Maldonado, A. G., Doucet, J. P., Petitjean, M., & Fan, B.-T. (2006). Molecular similarity and diversity in chemoinformatics: From theory to applications. Molecular Diversity, 10, 39-79.
269. Manmatha, R., Rath, T., & Feng, F. (2001). Modeling score distributions for combining the outputs of search engines. Proceedings of the 24th ACM SIGIR Conference on Research and Development in Information Retrieval, 267-275.
270. Marshall, R. R., Barry, C. D., Bosshard, H. E., Dammkoehler, R. A., & Dunn, D. A. (1979). The conformational parameter in drug design: The active analogue approach in computer-assisted drug design. In E. C. Olson & R. E. Christoffersen (Eds.), Computer-assisted drug design (pp. 205-226). Washington, DC: American Chemical Society.
271. Martin, E. J., Blaney, J. M., Siani, M. A., Spellmeyer, D. C., Wong, A. K., & Moos, W. H. ( 1995). Measuring diversity: Experimental-design of combinatorial libraries for drug discovery. Journal of Medicinal Chemistry, 38, 1431-1436.
272. Martin, Y. C. (1978). Quantitative drug design: A critical introduction. New York: Dekker.
273. Martin, Y. C., Bures, M. G., Danaher, E. A., Delazzer, J., Lico, I., & Pavlik, P. A. (1993). A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. Journal of Computer-Aided Molecular Design, 7, 83-102.
274. Martin, Y. C., Kofron, J. L., & Traphagen, L. M. (2002). Do structurally similar molecules have similar biological activities? Journal of Medicinal Chemistry, 45, 4350-4358.
275. Martin, Y. C., Willett, P., Lajiness, M., Johnson, M., Maggiora, G. M., Martin, E., et al. (2001). Diverse viewpoints on computational aspects of molecular diversity. Journal of Combinatorial Chemistry, 3, 231-250.
276. Mason, J. S., Morize, I., Menard, P. R., Cheney, D. L., Hulme, C., & 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.
277. Mason, J. S., & Pickett, S. D. (1997). Partition-based selection. Perspectives in Drug Discovery and Design, 7-8, 85-114.
278. Matter, H. (1997). Selecting optimally diverse compounds from structure databases: A validation study of two-dimensional and three-dimensional molecular descriptors. Journal of Medicinal Chemistry, 40, 1219-1229.
279. Medina-Franco, J. L., Petit, J., & Maggiora, G. M. (2006). Hierarchical strategy for identifying active chemotype classes in compound databases. Chemical Biology & Drug Design, 67, 395-408.
280. Mestres, J., & Knegtel, R. M. A. (2000). Similarity versus docking in 3D virtual screening. Perspectives in Drug Discovery and Design, 20, 191-207.
281. Mestres, J., Rohrer, D. C., & Maggiora, G. M. (1997). MIMIC, a molecular-field matching program: Exploiting applicability of molecular similarity approaches. Journal of Computational Chemistry, 18, 934-954.
282. Mestres, J., Rohrer, D. C., & Maggiora, G. M. (2000). A molecular-field-based similarity study of non-nucleoside HIV-1 reverse transcriptase inhibitors, 2: The relationship between alignment solutions obtained from conformationally rigid and flexible matching. Journal of Computer-Aided Molecular Design, 14, 39-51.
283. Monge, A., Arrault, A., Marot, C., & Morin-Allory, L. (2006). Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers. Molecular Diversity, 10, 389-403.
284. Moock, T. E., Grier, D. L., Hounshell, W. D., Grethe, G., Cronin, K., Nourse, J. G., et al. (1988). Similarity searching in the organic reaction domain. Tetrahedron Computer Methodology, 1, 117-128.
285. Moock, T. E., Henry, D. R., Ozkaback, A. G., & Alamgir, M. (1994). Conformational searching in ISIS/3D databases. Journal of Chemical Information and Computer Sciences, 34, 184-189.
286. Moon, J. M., & Howe, W. J. (1991). Computer design of bioactive molecules: A method for receptor-based de novo ligand design. Proteins, 11, 314-328.
287. Morgan, H. (1965). The generation of a unique machine description for chemical structures: A technique developed at Chemical Abstracts Service. Journal of Chemical Documentation, 5, 107-113.
288. Muller, K. R., Ratsch, G., Sonnenburg, S., Mika, S., Grimm, M., & Heinrich, N. (2005). Classifying "drug-likeness" with kernel-based learning methods. Journal of Chemical Information and Computer Sciences, 45, 249-253.
289. Munk, M. E. (1998). Computer-based structure determination: Then and now. Journal of Chemical Information and Computer Sciences, 38, 997-1009.
290. Murrall, N. W., & Davies, E. K. (1990). Conformational freedom in 3-D databases, 1: Techniques. Journal of Chemical Information and Computer Sciences, 30, 312-316.
291. Murtagh, F. (1985). Multidimensional clustering algorithms. Vienna, Austria: Physica Verlag.
292. Ng, K. B., & Kantor, P. B. (2000). Predicting the effectiveness of naïve data fusion on the basis of system characteristics. Journal of the American Society for Information Science, 51, 1177-1189.
293. Nikolova, N., & Jaworska, J. (2003). Approaches to measure chemical similarity: A review. Quantitative Structure-Activity Relationships and Combinatorial Science, 22, 1006-1026.
294. Nishibata, Y., & Itai, A. (1991). Automatic creation of drug candidate structures based on receptor structure. Tetrahedron, 47, 8985-8990.
295. Nübling, W., & Steidle, W. (1970). Documentation Ring of the chemical/pharmaceutical industry: Aims and methods. Angewandte Chemie International Edition in English, 9, 596-598.
296. Oda, A., Tsuchida, K., Takakura, T., Yamaotsu, N., & Hirono, S. (2006). Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes. Journal of Chemical Information and Modeling, 46, 380-391.
297. Onodera, N. (2001). A bibliometric study on chemical information and computer sciences focusing on literature of JCICS. Journal of Chemical Information and Computer Sciences, 41, 878-888.
298. Oprea, T. I. (2000). Property distribution of drug-related chemical databases. Journal of Computer-Aided Molecular Design, 14, 251-264.
299. Oprea, T. I. (2002). Virtual screening in lead discovery: A viewpoint. Molecules, 7, 51-62.
300. Oprea, T. I., Davis, A. M., Teague, S. J., & Leeson, P. D. (2001). Is there a difference between leads and drugs? A historical perspective. Journal of Chemical Information and Computer Sciences, 41, 1308-1315.
301. Oprea, T. I., & Matter, H. (2004). Integrating virtual screening in lead discovery. Current Opinion in Chemical Biology, 8, 349-358.
302. Orengo, C. A., Thornton, J. M., & Jones, D. Y. (Eds.). (2002). Bioinformatics. Abingdon, UK: Bios Scientific Publishers Ltd.
303. Ott, M. A. (2004). Cheminformatics and organic chemistry: Computer-assisted synthetic analysis. In J. H. Noordik (Ed.), Cheminformatics developments: History, reviews and current research (pp. 83-109). Amsterdam: IOS Press.
304. Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006). Global mapping of pharmacological space. Nature Biotechnology, 24, 805-815.
305. Paris, C. G. (1997). Chemical structure handling by computer. Annual Review of Information Science and Technology, 32, 271-337.
306. Patterson, D. E., Cramer, R. D., Ferguson, A. M., Clark, R. D., & Weinberger, L. E. (1996). Neighborhood behavior: A useful concept for validation of "molecular diversity" descriptors. Journal of Medicinal Chemistry, 39, 3049-3059.
307. Pearlman, R. S. (1987). Rapid generation of high quality approximate 3D molecular structures. Chemical Design Automation News, 2, 1-7.
308. Pearlman, R. S., & Smith, K. M. (1998). Novel software tools for chemical diversity. Perspectives in Drug Discovery and Design, 9-11, 339-353.
309. Pearlman, R. S., & Smith, K. M. (1999). Metric validation and the receptor-relevant subspace concept. Journal of Chemical Information and Computer Sciences, 39, 28-35.
310. Perekhodtsev, G. D. (2007). Neighborhood behavior: Validation of two-dimensional molecular similarity as a predictor of similar biological activities and docking scores. QSAR and Combinatorial Science, 26, 346-351.
311. Pickett, S. D., Mason, J. S., & McLay, I. M. (1996). Diversity profiling and design using 3D pharmacophores: Pharmacophore-derived queries (PDQ). Journal of Chemical Information and Computer Sciences, 36, 1214-1223.
312. Prathipati, P., Dixit, A., & Saxena, A. K. (2007). Computer-aided drug design: Integration of structure-based and ligand-based approaches in drug design. Current Computer-Aided Drug Design, 3, 133-148.
313. Pretsch, E., Tóth, G., Munk, M. E., & Badertscher, M. (2002). Computer-aided structure elucidation: Spectra interpretation and structure generation. Weinheim, Germany: Wiley-VCH.
314. Proudfoot, J. R. (2002). Drugs, leads and drug-likeness: An analysis of some recently launched drugs. Bioorganic Medicinal Chemistry Letters, 12, 1647-1650.
315. Raha, K., & Merz, K. M. (2005). Calculating binding free energy in protein-ligand interactions. Annual Reports in Computational Chemistry, 1, 113-130.
316. Rang, H. P. (Ed.). (2006). Drug discovery and development: Technology in transition. Edinburgh, UK: Churchill Livingstone.
317. Rarey, M., & Dixon, J. S. (1998). Feature trees: A new molecular similarity measure based on tree matching. Journal of Computer-Aided Molecular Design, 12, 471-490.
318. Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996). A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology, 261, 470-489.
319. Rarey, M., & Stahl, M. (2001). Similarity searching in large combinatorial chemistry spaces. Journal of Computer-Aided Molecular Design, 15, 497-520.
320. Rasmussen, E. M. (1997). Indexing images. Annual Review of Information Science and Technology, 32, 169-196.
321. Ray, L. C., & Kirsch, R. A. (1957). Finding chemical records by digital computers. Science, 126, 814-819.
322. Raymond, J. W., Blankley, C. J., & Willett, P. (2003). Comparison of chemical clustering methods using graph-based and fingerprint-based similarity measures. Journal of Molecular Graphics and Modelling, 21, 421-433.
323. Raymond, J. W., Gardiner, E. J., & Willett, P. (2002a). Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm. Journal of Chemical Information and Computer Sciences, 42, 305-316.
324. Raymond, J. W., Gardiner, E. J., & Willett, P. (2002b). RASCAL: Calculation of graph similarity using maximum common edge subgraphs. Computer Journal, 45, 631-644.
325. Raymond, J. W., Jalaie, M., & Bradley, P. P. (2004). Conditional probability: A new fusion method for merging disparate virtual screening results. Journal of Chemical Information and Computer Sciences, 44, 601-609.
326. Raymond, J. W., & Willett, P. (2002). Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. Journal of Computer-Aided Molecular Design, 16, 59-71.
327. Raymond, J. W., & Willett, P. (2003). Similarity searching in databases of flexible 3D structures using smoothed bounded distance matrices. Journal of Chemical Information and Computer Sciences, 43, 908-916.
328. Robertson, S. E., & Spärck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129-146.
329. Rogers, D., Brown, R. D., & Hahn, M. (2005). Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. Journal of Biomolecular Screening, 10, 682-686.
330. Rössler, S., & Kolb, A. (1970). The GREMAS system, an integral part of the IDC system for chemical documentation. Journal of Chemical Documentation, 10, 128-134.
331. Rush, J. E. (1978). Handling chemical structure information. Annual Review of Information Science and Technology, 13, 209-262.
332. Rush, T. S., Grant, J. A., Mosyak, L., & Nicholls, A. (2005). A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. Journal of Medicinal Chemistry, 48, 1489-1495.
333. Rusinko, A., Farmen, M. W., Lambert, C. G., Brown, P. L., & Young, S. S. (1999). Analysis of a large structure/biological activity data set using recursive partitioning. Journal of Chemical Information and Computer Sciences, 39, 1017-1026.
334. Sadowski, J., & Kubinyi, H. (1998). A scoring scheme for discriminating between drugs and nondrugs. Journal of Medicinal Chemistry, 41, 3325-3329.
335. Saeh, J. C., Lyne, P. D., Takasaki, B. K., & Cosgrove, D. A. (2005). Lead hopping using SVM and 3D pharmacophore fingerprints. Journal of Chemical Information and Modeling, 45, 1122-1133.
336. Salim, N., Holliday, J. D., & Willett, P. (2003). Combination of fingerprint-based similarity coefficients using data fusion. Journal of Chemical Information and Computer Sciences, 43, 435-142.
337. Salton, G. (1989). Automatic text processing. Reading, MA: Addison-Wesley.
338. Sasaki, S. I., Abe, H., Ouki, T., Sakamoto, M., & Ochiai, S. (1968). Automated structure elucidation of several kinds of aliphatic and alicyclic compounds. Analytical Chemistry, 40, 2220-2223.
339. Schneider, G., & Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4, 649-663.
340. Schneider, G., Neidhart, W., Giller, T., & Schmid, G. (1999). "Scaffold-hopping" by topological pharmacophore search: A contribution to virtual screening. Angewandte Chemie-International Edition in English, 38, 2894-2896.
341. Schneider, G., Schneider, P., & Renner, S. (2006). Scaffold-hopping: How far can you jump? QSAR and Combinatorial Science, 25, 1162-1171.
342. Schofield, H., Wiggins, G., & Willett, P. (2001). Recent developments in chemoinformatics education. Drug Discovery Today, 6, 931-934.
343. Schreyer, S. K., Parker, C. N., & Maggiora, G. M. (2004). Data shaving: A focused screening approach. Journal of Chemical Information and Computer Sciences, 44, 470-479.
344. Schuffenhauer, A., Brown, N. A., Ertl, P., Jenkins, J. L., Selzer, P., & Hamon, J. (2007). Clustering and rule-based classifications of chemical structures evaluated in the biological activity space. Journal of Chemical Information and Modeling, 47, 325-336.
345. Schuffenhauer, A., Brown, N., Selzer, P., Ertl, P., & Jacoby, E. (2006). Relationships between molecular complexity, biological activity, and structural diversity. Journal of Chemical Information and Modeling, 46, 525-535.
346. Schuffenhauer, A., Floersheim, P., Acklin, P., & Jacoby, E. (2003). Similarity metrics for ligands reflecting the similarity of the target proteins. Journal of Chemical Information and Computer Sciences, 43, 391-405.
347. Schuffenhauer, A., Gillet, V. J., & Willett, P. (2000). Similarity searching in files of threedimensional chemical structures: Analysis of the BIOSTER database using twodimensional fingerprints and molecular field descriptors. Journal of Chemical Information and Computer Sciences, 40, 295-307.
348. Shanmugasundaram, V., Maggiora, G. M., & Lajiness, M. S. (2005). Hit-directed nearestneighbor searching. Journal of Medicinal Chemistry, 48, 240-248.
349. Shelley, C. A., Hays, T. R., Munk, M. E., & Roman, H. V. (1978). An approach to automated partial structure expansion. Analytica Chimica Acta, 103, 121-132.
350. Shemetulskis, N. E., Dunbar, J. B., Dunbar, B. W., Moreland, D. W., & Humblet, C. (1995). Enhancing the diversity of a corporate database using chemical database clustering and analysis. Journal of Computer-Aided Molecular Design, 9, 407-416.
351. Shenton, K., Norton, P., & Fearns, E. A. (1988). Generic searching of patent information. In W. A. Warr (Ed.), Chemical structures: The international language of chemistry (pp. 169-178). Berlin, Germany: Springer Verlag.
352. Sheridan, R. P. (2000). The centroid approximation for mixtures: Calculating similarity and deriving structure-activity relationships. Journal of Chemical Information and Computer Sciences, 40, 1456-1469.
353. Sheridan, R. P. (2007). Chemical similarity searches: When is complexity justified? Expert Opinion on Drug Discovery, 2, 423-430.
354. Sheridan, R. P., Feuston, B. P., Maiorov, V. N., & Kearsley, S. K (2004). Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. Journal of Chemical Information and Computer Sciences, 44, 1912-1928.
355. Sheridan, R. P., & Kearsley, S. K. (1995). Using a genetic algorithm to suggest combinatorial libraries. Journal of Chemical Information and Computer Sciences, 35, 310-320.
356. Sheridan, R. P., & Kearsley, S. K. (2002). Why do we need so many chemical similarity search methods? Drug Discovery Today, 7, 903-911.
357. Sheridan, R. P., & Miller, M. D. (1998). A method for visualizing recurrent topological substructures in sets of active molecules. Journal of Chemical Information and Computer Sciences, 38, 915-924.
358. Sheridan, R. P., Miller, M. D., Underwood, D. J., & Kearsley, S. K (1996). Chemical similarity using geometric atom pair descriptors. Journal of Chemical Information and Computer Sciences, 36, 128-136.
359. Sheridan, R. P., Nilakantan, R., Rusinko, A., Bauman, N., Haraki, K. S., & Venkataraghavan, R. (1989). 3Dsearch: A system for three-dimensional substructure searching. Journal of Chemical Information and Computer Sciences, 29, 255-260.
360. Shively, E. (2007). CAS surveys its first 100 years. Chemical and Engineering News, 84(24), 41-53.
361. Sirois, S., Hatzakis, G., Wei, D., Du, Q., & Chou, K.-C. (2005). Assessment of chemical libraries for their drugability. Computational Biology and Chemistry, 29, 55-67.
362. Smeaton, A. F. (2004). Indexing, browsing, and searching of digital video. Annual Review of Information Science and Technology, 38, 371-407.
363. Snarey, M., Terrett, N. K, Willett, P., & Wilton, D. J. (1997). Comparison of algorithms for dissimilarity-based compound selection. Journal of Molecular Graphics and Modelling, 15, 372-385.
364. Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy. San Francisco: Freeman.
365. Spärck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11-21.
366. Spärck Jones, K, Walker, S., & Robertson, S. E. (2000). A probabilistic model of retrieval: Development and comparative experiments. Information Processing & Management, 36, 779-840.
367. Spärck Jones, K, & Willett, P. (Eds.). (1997). Readings in information retrieval. San Francisco: Morgan Kaufmann.
368. Stahl, M., & Rarey, M. (2001). Detailed analysis of scoring functions for virtual screening. Journal of Medicinal Chemistry, 44, 1035-1042.
369. Stahura, F. L., & Bajorath, J. (2002). Bio- and chemo-informatics beyond data management: Crucial challenges and future opportunities. Drug Discovery Today, 7, S41-S47.
370. Steinbach, M., Karypis, G., & Kumar, V. A. (2000). Comparison of document clustering techniques (Technical Report 00-034). Minneapolis: Department of Computer Science & Engineering, University of Minnesota.
371. Steindl, T. M., Schuster, D., Wolber, G., Laggner, C., & Langer, T. (2006). High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening. Journal of Computer-Aided Molecular Design, 20, 703-715.
372. Stiefl, N., Watson, I. A., Baumann, K., & Zaliani, A. (2006). ERG: 2D pharmacophore descriptions for scaffold hopping. Journal of Chemical Information and Modeling, 46, 208-220.
373. Stiefl, N., & Zaliani, A. (2006). A knowledge-based weighting approach to ligand-based virtual screening. Journal of Chemical Information and Modeling, 46, 587-596.
374. Sultan, M., Wigle, D. A., Cumbaa, C. A., Maziarz, M., Glasgow, J., Tsao, M. S., et al. (2002). Binary tree-structured vector quantization approach to clustering and visualizing microarray data. Bioinformatics, 18, 111-119.
375. Sussenguth, E. H. (1965). A graph-theoretic algorithm for matching chemical structures. Journal of Chemical Documentation, 5, 36-43.
376. Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003). Random forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43, 1947-1958.
377. Takahashi, Y., Sukekawa, M., & Sasaki, S. (1992). Automatic identification of molecular similarity using reduced-graph representation of chemical-structure. Journal of Chemical Information and Computer Sciences, 32, 639-643.
378. Tate, F. A. (1967). Handling chemical compounds in information systems. Annual Review of Information Science and Technology, 2, 285-309.
379. Taylor, R. (1995). Simulation analysis of experimental design strategies for screening random compounds as potential new drugs and agrochemicals. Journal of Chemical Information and Computer Sciences, 35, 59-67.
380. Teague, S. J., Davis, A. M., Leeson, P. D., & Oprea, T. (1999). The design of leadlike combinatorial libraries. Angewandte Chemie-International Edition in English, 38, 3743-3748.
381. Terrett, N. K. (1998). Combinatorial chemistry. Oxford, UK: Oxford University Press.
382. Thorner, D. A., Wild, D. J., Willett, P., & Wright, P. M. (1996). Similarity searching in files of three-dimensional chemical structures: Flexible field-based searching of molecular electrostatic potentials. Journal of Chemical Information and Computer Sciences, 36, 900-908.
383. Thorner, D. A., Willett, P., Wright, P. M., & Taylor, R. (1997). Similarity searching in files of three-dimensional chemical structures: Representation and searching of molecular electrostatic potentials using field-graphs. Journal of Computer-Aided Molecular Design, 11, 163-174.
384. Todeschini, R., & Consonni, V. (2002). Handbook of molecular descriptors. Weinheim, Germany: Wiley-VCH.
385. Tong, W., Hong, H., Fang, H., Xie, Q., & Perkins, R. (2003). Decision forest: Combining the predictions of multiple independent decision tree models. Journal of Chemical Information and Computer Sciences, 43, 525-531.
386. Tong, W., Lowis, D. R., Perkins, R., Chen, Y, Welsh, W. J., Goddette, D. W., Heritage, T. W., & Sheehan, D. M. (1998). Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. Journal of Chemical Information and Computer Sciences, 38, 669-677.
387. Triballeau, N., Acher, F., Brabet, I., Pin, J.-P., & Bertrand, H.-O. (2005). Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor type 4. Journal of Medicinal Chemistry, 48, 2534-2547.
388. Truchon, J.-F., & Bayly, C. I. (2007). Evaluating virtual screening methods: Good and bad metrics for the "early recognition" problem. Journal of Chemical Information and Modeling, 47, 488-508.
389. Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327-352.
390. Ullmann, J. R. (1976). An algorithm for subgraph isomorphism. Journal of the ACM, 16, 31-42.
391. van de Waterbeemd, H. (2005). From in vivo to in vitro/in silico ADME: Progress and challenges. Expert Opinion on Drug Metabolism & Toxicology, 1, 1-4.
392. van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: Towards prediction paradise? Nature Reviews Drug Discovery, 2(3), 192-204.
393. van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworth.
394. Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W, & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45, 2615-2623.
395. Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., et al. (2004). Virtual screening using protein-ligand docking: Avoiding artificial enrichment. Journal of Chemical Information and Computer Sciences, 44, 793-806.
396. Verheij, H. J. (2006). Leadlikeness and structural diversity of synthetic screening libraries. Molecular Diversity, 10, 377-388.
397. Vieth, M., & Sutherland, J. J. (2006). Dependence of molecular properties on proteomic family for marketed oral drugs. Journal of Medicinal Chemistry, 49, 3451-3453.
398. Vleduts, G. E. (1963). Concerning one system of classification and codification of organic reactions. Information Storage and Retrieval, 1, 117-146.
399. Wagener, M., & van Geerestein, V. J. (2000). Potential drugs and nondrugs: Prediction and identification of important structural features. Journal of Chemical Information and Computer Sciences, 40, 280-292.
400. Waldman, M., Li, H., & Hassan, M. (2000). Novel algorithms for the optimization of molecular diversity of combinatorial libraries. Journal of Molecular Graphics and Modelling, 18, 412-426.
401. Walters, W. P., Stahl, M. T., & Murcko, M. A. (1998). Virtual screening: An overview. Drug Discovery Today, 3, 160-178.
402. Wang, R., & Wang, S. (2001). How does consensus scoring work for virtual library screening? An idealized computer experiment. Journal of Chemical Information and Computer Sciences, 41, 1422-1426.
403. Wang, Y., Eckert, H., & Bajorath, J. (2007). Apparent asymmetry in fingerprint similarity searching is a direct consequence of differences in bit densities and molecular size. ChemMedChem, 2, 1037-1042.
404. Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236-244.
405. Warmuth, M. K, Liao, J., Ratsch, G., Mathieson, M., Putte, S., & Lemmen, C. (2003). Active learning with support vector machines in the drug discovery process. Journal of Chemical Information and Computer Sciences, 43, 667-673.
406. Warr, W. A. (1999, August). Balancing the needs of the recruiters and the aims of the educators. Paper presented at the 218th American Chemical Society National Meeting, New Orleans. Retrieved July 22, 2007, from www.warr.com/warrzone2000.html
407. Warr, W. A. (2003). IUPAC Project Meeting 12-14 November 2003: Extensible Markup Language (XML) Data Dictionaries and Chemical Identifier. Retrieved July 22, 2007, from www.warr.com/inchi.pdf
408. Warr, W. A., & Willett, P. (1997). The principles and practice of 3D database searching. In Y C. Martin & P. Willett (Eds.), Designing bioactive molecules: Three-dimensional techniques and applications (pp. 73-95). Washington, DC: American Chemical Society.
409. Warren, G. L., Andrews, C. W., Capelli, A.-M., Clarke, B., LaLonde, J., Lambert, M. H., et al. (2006). A critical assessment of docking programs and scoring functions. Journal of Medicinal Chemistry, 49, 5912-5931.
410. Weininger, D. (1988). SMILES, a chemical language and information-system, 1: Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28, 31-36.
411. Weisgerber, D. W. (1997). Chemical Abstracts Service Chemical Registry System: History, scope and impacts. Journal of the American Society for Information Science, 48, 349-360.
412. Whittle, M., Gillet, V. J., Willett, P., Alex, A., & Loesel, J. (2004). Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: A comparison of similarity coefficients. Journal of Chemical Information and Computer Sciences, 44, 1840-1848.
413. Whittle, M., Gillet, V. J., Willett, P., & Loesel, J. (2006a). Analysis of data fusion methods in virtual screening: Theoretical model. Journal of Chemical Information and Modeling, 46, 2193-2205.
414. Whittle, M., Gillet, V. J., Willett, P., & Loesel, J. (2006b). Analysis of data fusion methods in virtual screening: Similarity and group fusion. Journal of Chemical Information and Modeling, 46, 2206-2219.
415. Wild, D. J., & Blankley, C. J. (2000). Comparison of 2D fingerprint types and hierarchy level selection methods for structural grouping using Ward's clustering. Journal of Chemical Information and Computer Sciences, 40, 155-162.
416. Wild, D. J., & Wiggins, G. D. (2006). Challenges for chemoinformatics education in drug discovery. Drug Discovery Today, 11, 436-139.
417. Wild, D. J., & Willett, P. (1996). Similarity searching in files of three-dimensional chemical structures: Alignment of molecular electrostatic potential fields with a genetic algorithm. Journal of Chemical Information and Computer Sciences, 36, 159-167.
418. Willett, P. (1980). The evaluation of an automatically indexed, machine-readable chemical reactions file. Journal of Chemical Information and Computer Sciences, 20, 93-96.
419. Willett, P. (1981). A fast procedure for the calculation of similarity coefficients in automatic classification. Information Processing & Management, 17, 53-60.
420. Willett, P. (Ed.). (1986). Modern approaches to chemical reaction searching. Aldershot UK: Gower.
421. Willett, P. (1987). Similarity and clustering in chemical information systems. Letchworth, UK: Research Studies Press.
422. Willett, P. (1988). Recent trends in hierarchic document clustering: A critical review. Information Processing & Management, 24, 577-597.
423. Willett, P. (2000). Textual and chemical information retrieval: Different applications but similar algorithms. Information Research, 5. Retrieved July 22, 2007, from InformationR.net/ir/5-2/infres52.html
424. Willett, P. (2004). The evaluation of molecular similarity and molecular diversity methods using biological activity data. Methods in Molecular Biology, 275, 51-63.
425. Willett, P. (2006). Data fusion in ligand-based virtual screening. QSAR and Combinatorial Science, 25, 1143-1152.
426. Willett, P. (2008a). A bibliometric analysis of chemoinformatics. Aslib Proceedings, 60, 4-17.
427. Willett, P. (2008b). From chemical documentation to chemoinformatics: Fifty years of chemical information science. Journal of Information Science, 34(4) (in press).
428. Willett, P., Barnard, J. M., & Downs, G. M. (1998). Chemical similarity searching. Journal of Chemical Information and Computer Sciences, 38, 983-996.
429. Willett, P., Wilton, D. J., Hartzoulakis, B., Tang, R., Ford, J., & Madge, D. (2007). Prediction of ion channel activity using binary kernel discrimination. Journal of Chemical Information and Modeling, 47, 1961-1966.
430. Willett, P., & Winterman, V. (1986). A comparison of some measures of inter-molecular structural similarity. Quantitative Structure-Activity Relationships, 5, 18-25.
431. Willett, P., Winterman, V., & Bawden, D. (1986a). Implementation of nearest-neighbor searching in an online chemical structure search system. Journal of Chemical Information and Computer Sciences, 26, 36-41.
432. Willett, P., Winterman, V., & Bawden, D. (1986b). Implementation of non-hierarchic cluster analysis methods in chemical information systems: Selection of compounds for biological testing and clustering of substructure search output. Journal of Chemical Information and Computer Sciences, 26, 109-118.
433. Williams, C. (2006). Reverse fingerprinting, similarity searching by group fusion and fingerprint bit importance. Molecular Diversity, 10, 311-332.
434. Wilson, R. (1996). Introduction to graph theory (4th ed.). Harlow, UK: Longman.
435. Wilton, D. J., Harrison, R. F., Willett, P., Delaney, J., Lawson, K., & Mullier, G. (2006). Virtual screening using binary kernel discrimination: Analysis of pesticide data. Journal of Chemical Information and Modeling, 46, 471-477.
436. Wipke, W. T., & Dyott, T. M. (1974). Stereochemically unique naming algorithm. Journal of the American Chemical Society, 96, 4825-4834.
437. Wipke, W. T., & Howe, W. J. (Eds.). (1977). Computer assisted organic synthesis. Washington, DC: American Chemical Society.
438. Wipke, W T., Ouchi, G. I., & Krishnan, S. (1978). Simulation and evaluation of chemical synthesis-SECS: An application of artificial intelligence techniques. Artificial Intelligence, 11, 173-193.
439. Worboys, M. F. (1995). GIS: A computer perspective. London: Taylor and Francis.
440. Xia, X. Y., Maliski, E. G., Gallant, P., & Rogers, D. (2004). Classification of kinase inhibitors using a Bayesian model. Journal of Medicinal Chemistry, 47, 4463-4470.
441. Xue, L., Stahura, F. L., Godden, J. W., & Bajorath, J. (2001). Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations. Journal of Chemical Information and Computer Sciences, 41, 746-753.
442. Yang, J.-M., Chen, Y-F., Shen, T.-W., Kristal, B. S., & Hsu, D. F. (2005). Consensus scoring criteria for improving enrichment in virtual screening. Journal of Chemical Information and Modeling, 45, 1134-1146.
443. Yin, P. Y., & Chen, L. H. (1994). A new non-iterative approach for clustering. Pattern Recognition Letters, 15, 125-133.
444. Yu, H., & Adedoyin, A. (2003). ADME-Tox in drug discovery: Integration of experimental and computational techniques. Drug Discovery Today, 8, 852-861.
445. Zernov, V. V., Balakin, K. V., Ivaschenko, A. A., Savchuk, N. P., & Pletnev, I. V. (2003). Drug discovery using support vector machines: Case studies of drug-likeness, agrochemicallikeness, and enzyme inhibition predictions. Journal of Chemical Information and Computer Sciences, 43, 2048-2056.
446. Zhang, Q., & Muegge, I. (2006). Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: Ranking, voting, and consensus scoring. Journal of Medicinal Chemistry, 49, 1536-1548.
447. Zupan, J., & Gasteiger, J. (1999). Neural networks in chemistry and drug design. New York: Wiley-VCH.