Some Trends in Chem(o)informatics

Methods in Molecular Biology

Volumn 672, Issue , 2011, Pages 1-37

  Warr, Wendy A ^a  

^a Wendy Warr and Associates   (United Kingdom)

Author keywords

2D searching; 3D searching; Chemical structures; Cheminformatics; Computing infrastructure; De novo design; Fragment based drug design; History of cheminformatics; Ligand based drug design; Metabolite prediction; Open systems; Pharmaceutical industry; Pharmacophore; Protein ligand docking; QSAR; Similarity; Text mining; Virtual high throughput screening; Visualization

Indexed keywords

EID: 85139037180     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-60761-839-3_1     Document Type: Chapter

References (273)

1. Warr, W. A. Cheminformatics education. http://www.qsarworld.com/cheminformatics-education.php (accessed October 2, 2009).
2. Willett, P. (2008) A bibliometric analysis of the literature of chemoinformatics. Aslib. Proc. 60, 4–17.
3. Cheminformatics or chemoinformatics? http:/www.molinspiration.com/chemoin-formatics.html (accessed October 2, 2009).
4. Warr, W. A. (1999) Balancing the needs of the recruiters and the aims of the educators, in Book of Abstracts, 218th ACS National Meeting, New Orleans, Aug. 22–26.
5. Warr, W. A. Extract from 218th ACS National Meeting and Exposition, New Orleans, Louisiana, August 1999 [chemin-formatics]. http:/www.warr.com/warrzone 2000.html (accessed October 2, 2009).
6. Willett, P. (2007) A bibliometric analysis of the Journal of Molecular Graphics and Modelling. J. Mol. Graphics Modell. 26, 602–606.
7. Leach, A. R., and Gillet, V. J. (2003) An Introduction to Chemoinformatics. Kluwer, Dordrecht, The Netherlands.
8. Gasteiger, J., and Engel, T., (Eds.) (2003) Chemoinformatics: A Textbook, Wiley-VCH, Weinheim, Germany.
9. Bajorath, J., (Ed.) (2004) Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery, Humana Press, Totowa, NJ.
10. Oprea, T. I., (Ed.) (2005) Chemoinformatics in Drug Discovery, Wiley, New York, NY.
11. Schneider, G., and Baringhaus, K.-H., (Eds.) (2008) Molecular Design: Concepts and Applications, Wiley-VCH, Weinheim, Germany.
12. Chen, W. L. (2006) Chemoinformatics: past, present, and future. J. Chem. Inf. Model. 46, 2230–2255.
13. Engel, T. (2006) Basic overview of chemoinformatics. J. Chem. Inf. Model. 46, 2267–2277.
14. Willett, P. (2008) From chemical documentation to chemoinformatics: 50 years of chemical information science. J. Inf. Sci. 34, 477–499.
15. Willett, P. (2003) A history of chemoinformatics in Handbook of Chemoinformatics: From Data to Knowledge Wiley-VCH, Weinheim, Germany, Vol. 1, pp 6–20.
16. Bishop, N., Gillet, V. J., Holliday, J. D., and Willett, P. (2003) Chemoinformatics research at the University of Sheffield: a history and citation analysis. J. Inf. Sci. 29, 249–267.
17. Ash, J. E., Warr, W. A., and Willett, P., (Eds.) (1991) Chemical structure systems: computational techniques for representation, searching, and processing of structural information, Ellis Horwood, Chichester, UK.
18. Paris, G. C. (1997) Chemical structure handling by computer. Ann. Rev. Inf. Sci. Technol. 32, 271–337.
19. Paris, G. C. (1998) Structure databases, in Encyclopedia of Computational Chemistry (Schleyer, P. v. R., Allinger, N. L., Clark, T., Gasteiger, J., Kollman, P. A., Schaefer, H. F., III, and Schreiner, P. R., Eds.), Wiley, Chichester, UK, Vol. 4, pp 2771–2785.
20. Paris, G. C. (2003) Databases of chemical structures, in Handbook of Cheminformatics: From Data to Knowledge (Gasteiger, J., Ed.), Wiley-VCH, Weinheim, Germany, Vol. 2, pp 523–555.
21. Warr, W. A., and Suhr, C. (1992) Chemical information management, Wiley-VCH, Weinheim, Germany.
22. Ott, M., A. (2004) Cheminformatics and organic chemistry. Computer-assisted synthetic analysis, in Cheminformatics Developments (Noordik, J., H., Ed.), IOS Press, Amsterdam, The Netherlands, pp 83–109.
23. Pearlman, R. S. (1987) Rapid generation of high quality approximate 3D molecular structures. Chem. Des. Autom. News 2, 5–7.
24. Gasteiger, J., Rudolph, C., and Sadowski, J. (1990) Automatic generation of 3D atomic coordinates for organic molecules. Tetrahedron Comput. Methodol. 3, 537–547.
25. Hiller, C., and Gasteiger, J. (1987) An automatic molecule builder, in Software Development in Chemistry 1. Proceedings of the Workshops on the Computer in Chemistry, Hochfilzen/Tirol, November 19–21, 1986 (Gasteiger, J., Ed.), Springer, Berlin, Vol. 1, pp 53–66.
26. Sadowski, J., Gasteiger, J., and Klebe, G. (1994) Comparison of automatic three-dimensional model builders using 639 X-ray structures. J. Chem. Inf. Comput. Sci. 34, 1000–1008.
27. Jakes, S. E., and Willett, P. (1986) Pharmacophoric pattern matching in files of 3-D chemical structures: selection of intera-tomic distance screens. J. Mol. Graphics 4, 12–20.
28. Jakes, S. E., Watts, N., Willett, P., Bawden, D., and Fisher, J. D. (1987) Pharmacophoric pattern matching in files of 3D chemical structures: evaluation of search performance. J. Mol. Graphics 5, 41–48.
29. Brint, A. T., and Willett, P. (1987) Pharmacophoric pattern matching in files of 3D chemical structures: comparison of geometric searching algorithms. J. Mol. Graphics 5, 49–56.
30. Cringean, J. K., Pepperrell, C. A., Poirrette, A. R., and Willett, P. (1990) Selection of screens for three-dimensional substructure searching. Tetrahedron Comput. Methodol. 3, 37–46.
31. Willett, P. (1991) Three-dimensional Chemical Structure Handling, Research Studies Press, Taunton, UK.
32. Martin, Y. C., and Willett, P., (Eds.) (1998) Designing Bioactive Molecules: Three-Dimensional Techniques and Applications, American Chemical Society, Washington, DC.
33. Martin, Y. C., Danaher, E. B., May, C. S., and Weininger, D. (1988) MENTHOR, a database system for the storage and retrieval of three-dimensional molecular structures and associated data searchable by substructural, biologic, physical, or geometric properties. J. Comput.-Aided Mol. Des. 2, 15–29.
34. Van Drie, J. H., Weininger, D., and Martin, Y. C. (1989) ALADDIN: an integrated tool for computer-assisted molecular design and pharmacophore recognition from geometric, steric, and substructure searching of three-dimensional molecular structures. J. Comput.-Aided Mol. Des. 3, 225–251.
35. Willett, P., Barnard, J. M., and Downs, G. M. (1998) Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38, 983–996.
36. Barnard, J. M. (1991) A comparison of different approaches to Markush structure handling. J. Chem. Inf. Comput. Sci. 31, 64–68.
37. Benichou, P., Klimczak, C., and Borne, P. (1997) Handling genericity in chemical structures using the Markush DARC software. J. Chem. Inf. Comput. Sci. 37, 43–53.
38. Corey, E. J., and Wipke, W. T. (1969) Computer-assisted design of complex organic syntheses. Science 166, 178–192.
39. THERESA. Molecular Networks. http:/www.molecular-networks.com/products/theresa (accessed October 2, 2009).
40. Dugundji, J., and Ugi, I. (1973) Algebraic model of constitutional chemistry as a basis for chemical computer programs. Fortschr. Chem. Forsch. 39, 19–64.
41. Bauer, J., Herges, R., Fontain, E., and Ugi, I. (1985) IGOR and computer assisted innovation in chemistry. Chimia 39, 43–53.
42. Gasteiger, J., Ihlenfeldt, W. D., Roese, P., and Wanke, R. (1990) Computer-assisted reaction prediction and synthesis design. Anal. Chim. Acta 235, 65–75.
43. Jorgensen, W. L., Laird, E. R., Gushurst, A. J., Fleischer, J. M., Gothe, S. A., Helson, H. E., Paderes, G. D., and Sinclair, S. (1990) CAMEO: a program for the logical prediction of the products of organic reactions. Pure Appl. Chem. 62, 1921–1932.
44. Sadowski, J., Wagener, M., and Gasteiger, J. (1996) Assessing similarity and diversity of combinatorial libraries by spatial autocorrelation functions and neural networks. Angew. Chem. Int. Ed. Engl. 34, 2674–2677.
45. Sadowski, J., Gasteiger, J., and Klebe, G. (2002) Comparison of automatic three-dimensional model builders using 639 X-ray structures. J. Chem. Inf. Comput. Sci. 34, 1000–1008.
46. Kennard, O., Watson, D. G., and Town, W. G. (1972) Cambridge Crystallographic Data Centre. I. Bibliographic file. J. Chem. Doc. 12, 14–19.
47. Allen, F. H., Kennard, O., Motherwell, W. D. S., Town, W. G., and Watson, D. G. (1973) Cambridge Crystallographic Data Centre. II. Structural data file. J. Chem. Doc. 13, 119–123.
48. Allen, F. H. (2002) The Cambridge Structural Database: a quarter of a million crystal structures and rising. Acta Crystallogr. Sect. B Struct. Sci. B58, 380–388.
49. Allen, F. H., Battle, G., and Robertson, S. (2007) The Cambridge Structural Database, in Comprehensive Medicinal Chemistry II. (Triggle, D. J., and Taylor, J. B., Eds.), Elsevier, Amsterdam, The Netherlands, Vol. 3, pp 389–410.
50. Berman, H. M. (2008) The Protein Data Bank: a historical perspective. Acta Crystallogr., Sect. A Found. Crystallogr. A64, 88–95.
51. Martin, Y. C. (1998) Pharmacophore mapping, in Designing Bioactive Molecules (Martin, Y. C., and Willett, P., Eds.), American Chemical Society, Washington, DC, pp 121–148.
52. Martin, Y. C., Bures, M. G., Danaher, E. A., DeLazzer, J., Lico, I., and Pavlik, P. A. (1993) A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J. Comput.-Aided Mol. Des. 7, 83–102.
53. Greene, J., Kahn, S., Savoj, H., Sprague, P., and Teig, S. (1994) Chemical function queries for 3D database search. J. Chem. Inf. Comput. Sci. 34, 1297–1308.
54. Sprague, P. (1995) Automated chemical hypothesis generation and database searching with Catalyst. Perspect. Drug Discov. Des. 3, 1–20.
55. Jones, G., Willett, P., and Glen, R. C. (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J. Comput.-Aided Mol. Des. 9, 532–549.
56. Jones, G., Willett, P., and Glen, R. C. (1995) Molecular recognition of a receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245, 43–53.
57. Jones, G., Willett, P., Glen, R. C., Leach, A. R., and Taylor, R. (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748.
58. Winkler, D. A. (2002) The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery. Briefings Bioinf. 3, 73–86.
59. Tropsha, A. (2003) Recent trends in quantitative structure-activity relationships, in Burger’s Medicinal Chemistry and Drug Discovery, Volume 1, Drug Discovery (Abraham, D. J., Ed.) 6th ed., Wiley, New York, NY.
60. Tropsha, A. (2005) Application of predictive QSAR models to database mining, in Cheminformatics in Drug Discovery (Oprea, T. I., Ed.), Wiley, New York, NY, pp 437–455.
61. Gramatica, P. A short history of QSAR evolution. http:/www.qsarworld.com/Temp_Fileupload/Shorthistoryofqsar.pdf (accessed September 23, 2009).
62. Hawkins, D. M. QSAR approaches, models and statistics relating to toxicity prediction. In W. A Warr. Proceedings of New Horizons in Toxicity Prediction. Lhasa Limited symposium event in collaboration with the University of Cambridge, December 2008. http://www.qsarworld.com/files/Lhasa_ Symposium_2008_Report.pdf (accessed September 23, 2009).
63. Cramer, R. D., Patterson, D. E., and Bunce, J. D. (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959–5967.
64. Klebe, G., Abraham, U., and Mietzner, T. (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 37, 4130–4146.
65. Lemmen, C., Lengauer, T., and Klebe, G. (1998) FlexS: a method for fast flexible ligand superposition. J. Med. Chem. 41, 4502–4520.
66. Kennedy, T. (1997) Managing the drug dis-covery/development interface. Drug Discov. Today 2, 436–444.
67. Benigni, R., (Ed.) (2003) Quantitative Structure-Activity Relationship (QSAR) Models of Mutagens and Carcinogens, CRC Press, Boca Raton, FL.
68. Cronin, M. T. D., and Livingstone, D. J., (Eds.) (2004) Predicting Chemical Toxicity and Fate, CRC Press, Boca Raton, FL.
69. Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25.
70. Cramer, R. (1995) Unpublished work.
71. G€uner, O. F., (Ed.) (2000) Pharmacophore: Perception, Development, and Use in Drug Design. [In: IUL Biotechnol. Ser., 2000; 2], International University Line, La Jolla, CA.
72. Willett, P., (Ed.) (1997) Computational Methods for the Analysis of Molecular Diversity (Perspectives in Drug Discovery and Design 1997, Volumes 7/8), Kluwer/Escom, Dordrecht, The Netherlands.
73. Brown, R. D., and Martin, Y. C. (1996) Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci. 36, 572–584.
74. Brown, R. D., and Martin, Y. C. (1997) The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J. Chem. Inf. Comput. Sci. 37, 1–9.
75. Martin, E. J., Blaney, J. M., Siani, M. A., Spellmeyer, D. C., Wong, A. K., and Moos, W. H. (1995) Measuring diversity: experimental design of combinatorial libraries for drug discovery. J. Med. Chem. 38, 1431–1436.
76. Gillet, V. J., Willett, P., and Bradshaw, J. (1997) The effectiveness of reactant pools for generating structurally-diverse combinatorial libraries. J. Chem. Inf. Comput. Sci. 37, 731–740.
77. Cottrell, S. J., Gillet, V. J., Taylor, R., and Wilton, D. J. (2004) Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques. J. Comput.-Aided Mol. Des. 18, 665–682.
78. Cottrell, S., Gillet, V., and Taylor, R. (2006) Incorporating partial matches within multiobjective pharmacophore identification. J. Comput.-Aided Mol. Des. 20, 735–749.
79. Teague, S., J., Davis, A., M., Leeson, P., D., and Oprea, T. I. (1999) The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed. Engl. 38, 3743–3748.
80. Oprea, T. I., Davis, A. M., Teague, S. J., and Leeson, P. D. (2001) Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315.
81. Hann, M. M., Leach, A. R., and Harper, G. (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41, 856–864.
82. Sadowski, J., and Kubinyi, H. (1998) A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329.
83. Wagener, M., and Van Geerestein, V. J. (2000) Potential drugs and nondrugs: prediction and identification of important structural features. J. Chem. Inf. Comput. Sci. 40, 280–292.
84. Willett, P. (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11, 1046–1053.
85. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., and Ferrin, T. E. (1982) A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 161, 269–288.
86. Rarey, M., Kramer, B., Lengauer, T., and Klebe, G. (1996) A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261, 470–489.
87. McMeeking, B., and Fletcher, D. (2004) The United Kingdom Chemical Database Service: CDS, in Cheminformatics Developments (Noordik, J., H., Ed.), IOS Press, Amsterdam, The Netherlands, pp 37–67.
88. (Multiple authors) (2006) Focus on Cyber-infrastructure (“e-science”), the Enabling Platform for Cheminformatics. J. Chem. Inf. Model. 46.
89. Zsoldos, Z., Reid, D., Simon, A., Sadjad, S. B., and Johnson, A. P. (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J. Mol. Graphics Modell. 26, 198–212.
90. Salamone, S. GTA4: enabler of life sciences research? http:/www.bio-itworld.com/inside-it/2008/2005/gta2004-and-life-sciences.html (accessed October 2, 2009).
91. Murray-Rust, P. (2008) Chemistry for everyone. Nature (London, U. K.) 451, 648–651.
92. Williams, A. J. (2008) Internet-based tools for communication and collaboration in chemistry. Drug Discov. Today 13, 502–506.
93. Warr, W. A. (2008) Social software: fun and games, or business tools? J. Inf. Sci. 34, 591–604.
94. Coles, S. J., Day, N. E., Murray-Rust, P., Rzepa, H. S., and Zhang, Y. (2005) Enhancement of the chemical semantic web through the use of InChI identifiers. Org. Biomol. Chem. 3, 1832–1834.
95. Guha, R., Howard, M. T., Hutchison, G. R., Murray-Rust, P., Rzepa, H., Steinbeck, C., Wegner, J., and Willighagen, E. L. (2006) The Blue Obelisk. Interoperability in chemical informatics. J. Chem. Inf. Model. 46, 991–998.
96. Taylor, K. R., Gledhill, R. J., Essex, J. W., Frey, J. G., Harris, S. W., and De Roure, D. C. (2006) Bringing chemical data onto the semantic web. J. Chem. Inf. Model. 46, 939–952.
97. Frey, J. G. (2009) The value of the Semantic Web in the laboratory. Drug Discov. Today 14, 552–561.
98. Gardner, B. Approaches to information integration. Paper given at ICIC 2007, Sitges, Spain, October 21–24, 2007. http:/www. infonortics.eu/chemical/ch07/slides/gardner. pdf (accessed September 18, 2009).
99. Symyx Technologies. DiscoveryGate. http://www.discoverygate.com (accessed October 2, 2009).
100. ChemSpider. http:/www.chemspider.com (accessed September 18, 2009).
101. Warr, W. A. Workflow and pipelining in cheminformatics. http://www.qsarworld. com/qsar-workflow1.php (accessed October 2, 2009).
102. Jorgensen, W. L. (2004) The many roles of computation in drug discovery. Science 303, 1813–1818.
103. PubChem. http:/pubchem.ncbi.nlm.nih. gov/search/search.cgi (accessed September 18, 2009).
104. Oprea, T. I., Tropsha, A., Faulon, J.-L., and Rintoul, M. D. (2007) Systems chemical biology. Nat. Chem. Biol. 3, 447–450.
105. Therapeutics for Rare and Neglected Diseases. http:/www.nih.gov/news/health/may2009/nhgri-2020.htm (accessed October 7, 2009).
106. DeLano, W. L. (2005) The case for open-source software in drug discovery. Drug Discov. Today 10, 213–217.
107. Stahl, M. T. (2005) Open-source software: not quite endsville. Drug Discov. Today 10, 219–222.
108. The Pistoia Alliance. http:/pistoiaalliance. org/(accessed October 7, 2009).
109. Barnes, M. R., Harland, L., Foord, S. M., Hall, M. D., Dix, I., Thomas, S., Williams-Jones, B. I., and Brouwer, C. R. (2009) Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery. Nat. Rev. Drug Discov. 8, 701–708.
110. Lhasa Limited. Derek for Windows. http:/www.lhasalimited.org/(accessed October 7, 2009).
111. Irwin, J. J., and Shoichet, B. K. (2004) ZINC – a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182.
112. eMolecules. http:/www.emolecules.com (accessed September 18, 2009).
113. NIH/CADD chemical structure lookup service. http:/cactus.nci.nih.gov/lookup (accessed October 2, 2009).
114. Williams, A. J. (2008) A perspective of publicly accessible/open-access chemistry databases. Drug Discov. Today 13, 495–501.
115. Fink, T., and Reymond, J.-L. (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoi-somers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 47, 342–353.
116. Blum, L. C., and Reymond, J.-L. (2009) 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc. 131, 8732–8733.
117. Collaborative Drug Discovery. http:/www. collaborativedrug.com/(accessed September 18, 2009).
118. Morgan, H. L. (1965) The generation of a unique machine description for chemical structures – a technique developed at Chemical Abstracts Service. J. Chem. Doc. 5, 107–113.
119. Wipke, W. T., and Dyott, T. M. (1974) Stereochemically unique naming algorithm. J. Am. Chem. Soc. 96, 4834–4842.
120. Hillard, R., and Taylor, K. T. (2009) InChI keys as standard global identifiers in chemistry web services, in Abstracts of Papers, 237th ACS National Meeting, Salt Lake City, UT, United States, March 22–26, 2009.
121. The IUPAC International Chemical Identifier (InChI). http:/www.iupac.org/inchi/(accessed September 18, 2009).
122. Heller, S. R., and McNaught, A. D. (2009) The IUPAC international chemical identifier (InChI). Chem. Int. 31, 7–9.
123. Warr, W. A. The IUPAC International Chemical Identifier. http:/www.qsarworld. com/INCHI1.php (accessed September 18, 2009).
124. McKay, B. D. (1981) Practical graph isomorphism. Congressus Numeratium 30, 45–87.
125. Weininger, D. (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36.
126. Weininger, D., Weininger, A., and Weininger, J. L. (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci. 29, 97–101.
127. Leach, A. R., Shoichet, B. K., and Peishoff, C. E. (2006) Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 49, 5851–5855.
128. Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., and Corbeil, C. R. (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br. J. Pharmacol. 153, S7-S26.
129. Wang, R., Lai, L., and Wang, S. (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput.-Aided Mol. Des. 16, 11–26.
130. Waszkowycz, B. (2008) Towards improving compound selection in structure-based virtual screening. Drug Discov. Today 13, 219–226.
131. Jorgensen, W. L. (2009) Efficient drug lead discovery and optimization. Acc. Chem. Res. 42, 724–733.
132. Irwin, J. (2008) Community benchmarks for virtual screening. J. Comput.-Aided Mol. Des. 22, 193–199.
133. Liebeschuetz, J. (2008) Evaluating docking programs: keeping the playing field level. J. Comput.-Aided Mol. Des. 22, 229–238.
134. DUD. A Directory of Useful Decoys. http:/dud.docking.org/(accessed September 18, 2009).
135. Huang, N., Shoichet, B. K., and Irwin, J. J. (2006) Benchmarking sets for molecular docking. J. Med. Chem. 49, 6789–6801.
136. Good, A., and Oprea, T. (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J. Comput.-Aided Mol. Des. 22, 169–178.
137. Jain, A. (2008) Bias, reporting, and sharing: computational evaluations of docking methods. J. Comput.-Aided Mol. Des. 22, 201–212.
138. Jain, A., and Nicholls, A. (2008) Recommendations for evaluation of computational methods. J. Comput.-Aided Mol. Des. 22, 133–139.
139. Cole, J. C., Murray, C. W., Nissink, J. W. M., Taylor, R. D., and Taylor, R. (2005) Comparing protein-ligand docking programs is difficult. Proteins Struct., Funct., Bioinf. 60, 325–332.
140. Clark, R., and Webster-Clark, D. (2008) Managing bias in ROC curves. J. Comput.-Aided Mol. Des. 22, 141–146.
141. Nicholls, A. (2008) What do we know and when do we know it? J. Comput.-Aided Mol. Des. 22, 239–255.
142. Truchon, J.-F., and Bayly, C. I. (2007) Evaluating virtual screening methods: good and bad metrics for the “early recogni-tion” problem. J. Chem. Inf. Model. 47, 488–508.
143. Sheridan, R. P., Singh, S. B., Fluder, E. M., and Kearsley, S. K. (2001) Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J. Chem. Inf. Comput. Sci. 41, 1395–1406.
144. Hawkins, P. C. D., Skillman, A. G., and Nicholls, A. (2006) Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 50, 74–82.
145. Zhang, Q., and Muegge, I. (2006) Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J. Med. Chem. 49, 1536–1548.
146. McGaughey, G. B., Sheridan, R. P., Bayly, C. I., Culberson, J. C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.-F., and Cornell, W. D. (2007) Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 47, 1504–1519.
147. Rush, T. S., Grant, J. A., Mosyak, L., and Nicholls, A. (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein–protein interaction. J. Med. Chem. 48, 1489–1495.
148. Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., and Watson, P. (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 44, 793–806.
149. Klebe, G. (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580–594.
150. Böhm, H.-J. (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput.-Aided Mol. Des. 6, 61–78.
151. Law, J. M. S., Fung, D. Y. K., Zsoldos, Z., Simon, A., Szabo, Z., Csizmadia, I. G., and Johnson, A. P. (2003) Validation of the SPROUT de novo design program. Theochem 666–667, 651–657.
152. Boda, K., and Johnson, A. P. (2006) Molecular complexity analysis of de novo designed ligands. J. Med. Chem. 49, 5869–5879.
153. Barreiro, G., Kim, J. T., Guimaraes, C. R. W., Bailey, C. M., Domaoal, R. A., Wang, L., Anderson, K. S., and Jorgensen, W. L. (2007) From docking false-positive to active anti-HIV agent. J. Med. Chem. 50, 5324–5329.
154. Baber, J. C., and Feher, M. (2004) Predicting synthetic accessibility: application in drug discovery and development. Mini-Rev. Med. Chem. 4, 681–692.
155. Boda, K., Seidel, T., and Gasteiger, J. (2007) Structure and reaction based evaluation of synthetic accessibility. J. Comput.-Aided Mol. Des. 21, 311–325.
156. Zaliani, A., Boda, K., Seidel, T., Herwig, A., Schwab, C. H., Gasteiger, J., Claussen, H., Lemmen, C., Degen, J., Paern, J., and Rarey, M. (2009) Second-generation de novo design: a view from a medicinal chemist perspective. J. Comput.-Aided Mol. Des. 23, 593–602.
157. Patel, H., Bodkin, M. J., Chen, B., and Gillet, V. J. (2009) Knowledge-based approach to de novo design using reaction vectors. J. Chem. Inf. Model. 49, 1163–1184.
158. Shuker, S. B., Hajduk, P. J., Meadows, R. P., and Fesik, S. W. (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274, 1531–1534.
159. Allen, K. N., Bellamacina, C. R., Ding, X., Jeffery, C. J., Mattos, C., Petsko, G. A., and Ringe, D. (1996) An experimental approach to mapping the binding surfaces of crystalline proteins. J. Phys. Chem. 100, 2605–2611.
160. Carr, R. A. E., Congreve, M., Murray, C. W., and Rees, D. C. (2005) Fragment-based lead discovery: leads by design. Drug Discov. Today 10, 987–992.
161. Warr, W. (2009) Fragment-based drug discovery. J. Comput.-Aided Mol. Des. 23, 453-458.
162. Joseph-McCarthy, D. (2009) Challenges of fragment screening. J. Comput.-Aided Mol. Des. 23, 449–451.
163. Chen, I. J., and Hubbard, R. (2009) Lessons for fragment library design: analysis of output from multiple screening campaigns. J. Comput.-Aided Mol. Des. 23, 603–620.
164. Hopkins, A. L., Groom, C. R., and Alex, A. (2004) Ligand efficiency: a useful metric for lead selection. Drug Discovery Today 9, 430–431.
165. Abad-Zapatero, C., and Metz James, T. (2005) Ligand efficiency indices as guide-posts for drug discovery. Drug Discov. Today 10, 464–469.
166. Leeson, P. D., and Springthorpe, B. (2007) The influence of drug-like concepts on deci-sion-making in medicinal chemistry. Nat. Rev. Drug Discov. 6, 881–890.
167. Congreve, M., Chessari, G., Tisi, D., and Woodhead, A. J. (2008) Recent developments in fragment-based drug discovery. J. Med. Chem. 51, 3661–3680.
168. Congreve, M., Carr, R., Murray, C., and Jhoti, H. (2003) A “rule of three” for fragment-based lead discovery? Drug Discov. Today 8, 876–877.
169. Hajduk, P. J., and Greer, J. (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discov. 6, 211–219.
170. Erlanson, D. A., Wells, J. A., and Braisted, A. C. (2004) Tethering: fragment-based drug discovery. Annu. Rev. Biophys. Biomol. Struct. 33, 199–223, 194 plates.
171. Blomberg, N., Cosgrove, D., Kenny, P., and Kolmodin, K. (2009) Design of compound libraries for fragment screening. J. Comput.-Aided Mol. Des. 23, 513–525.
172. de Kloe, G. E., Bailey, D., Leurs, R., and de Esch, I. J. P. (2009) Transforming fragments into candidates: small becomes big in medicinal chemistry. Drug Discov. Today 14, 630–646.
173. Willett, P. (2009) Similarity methods in chemoinformatics. Ann. Rev. Inf. Sci. Technol. 43, 3–71.
174. Eckert, H., and Bajorath, J. (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 12, 225–233.
175. Raymond, J. W., and Willett, P. (2002) Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J. Comput.-Aided Mol. Des. 16, 521–533.
176. Ghose, A. K., Herbertz, T., Salvino, J. M., and Mallamo, J. P. (2006) Knowledge-based chemoinformatic approaches to drug discovery. Drug Discov. Today 11, 1107–1114.
177. Accelrys. Pipeline Pilot http:/accelrys. com/products/scitegic/(accessed October 2, 2009).
178. Simulations Plus. ClassPharmer. http:/www.simulations-plus.com/(accessed October 6, 2009).
179. ChemAxon. Library MCS. http:/www.che-maxon.com/shared/libMCS/(accessed October 2, 2009).
180. Cramer, R. D. (2003) Topomer CoMFA: a design methodology for rapid lead optimization. J. Med. Chem. 46, 374–388.
181. Jilek, R. J., and Cramer, R. D. (2004) Topo-mers: a validated protocol for their self-con-sistent generation. J. Chem. Inf. Comput. Sci. 44, 1221–1227.
182. Cramer, R., and Wendt, B. (2007) Pushing the boundaries of 3D-QSAR. J. Comput.-Aided Mol. Des. 21, 23–32.
183. Lill, M. A. (2007) Multi-dimensional QSAR in drug discovery. Drug Discov. Today 12, 1013–1017.
184. Hopfinger, A. J., Wang, S., Tokarski, J. S., Jin, B., Albuquerque, M., Madhav, P. J., and Duraiswami, C. (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J. Am. Chem. Soc. 119, 10509–10524.
185. Stouch, T. R., Kenyon, J. R., Johnson, S. R., Chen, X.-Q., Doweyko, A., and Li, Y. (2003) In silico ADME/Tox: why models fail. J. Comput.-Aided Mol. Des. 17, 83–92.
186. Kubinyi, H. Why models fail. http://amer-icanchemicalsociety.mediasite.com/acs/viewer/?peid¼7a194d147-baa199-19-4b192d-a823-191fd117bf5301c (accessed September 22, 2009).
187. Tropsha, A., and Golbraikh, A. (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13, 3494–3504.
188. Hawkins, D. M. (2003) The problem of overfitting. J. Chem. Inf. Comput. Sci. 44, 1–12.
189. Sheridan, R. P., Feuston, B. P., Maiorov, V. N., and Kearsley, S. K. (2004) Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J. Chem. Inf. Comput. Sci. 44, 1912–1928.
190. Kubinyi, H., Hamprecht, F. A., and Mietzner, T. (1998) Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem. 41, 2553–2564.
191. Kubinyi, H. (2006) Validation and predic-tivity of QSAR models, in QSAR & Molecular Modelling in Rational Design of Bioactive Molecules, Proceedings of the 15th European Symposium on Structure-Activity Relationships and Molecular Modelling, Istanbul, Turkey 2004, pp 30–33.
192. Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., and Tropsha, A. (2003) Rational selection of training and test sets for the development of validated QSAR models. J. Comput.-Aided Mol. Des. 17, 241–253.
193. Golbraikh, A., and Tropsha, A. (2002) Beware of q2! J. Mol. Graphics Modell. 20, 269–276.
194. Doweyko, A. M. (2004) 3D-QSAR illusions. J. Comput.-Aided Mol. Des. 18, 587–596.
195. Gramatica, P. (2007) Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26, 694–701.
196. Tropsha, A., Gramatica, P., and Gombar, V. K. (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Comb. Sci. 22, 69–77.
197. Jorgensen, W. L. (2006) QSAR/QSPR and proprietary data. J. Chem. Inf. Model. 46, 937–937.
198. Editorial. (2006) QSAR/QSPR and proprietary data. J. Med. Chem. 49, 3431–3431.
199. ChemMedChem notice to authors. http:/www3.interscience.wiley.com/journal/11048 5305/home/110482452_notice.html?CRE TRY¼110485301&SRETRY¼110485300 (accessed September 22, 2009).
200. Maggiora, G. M. (2006) On outliers and activity cliffs. Why QSAR often disappoints. J. Chem. Inf. Model. 46, 1535–1535.
201. Peltason, L., and Bajorath, J. (2007) Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem. Biol. 14, 489–497.
202. Peltason, L., and Bajorath, J. (2007) SAR Index: quantifying the nature of structure-activity relationships. J. Med. Chem. 50, 5571–5578.
203. Guha, R., and Van Drie, J. H. (2008) Assessing how well a modeling protocol captures a structure-activity landscape. J. Chem. Inf. Model. 48, 1716–1728.
204. Guha, R., and Van Drie, J. H. (2008) Structure-activity landscape index: identifying and quantifying activity cliffs. J. Chem. Inf. Model. 48, 646–658.
205. Medina-Franco, J. L., Martinez-Mayorga, K., Bender, A., Marin, R. M., Giulianotti, M. A., Pinilla, C., and Houghten, R. A. (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J. Chem. Inf. Model. 49, 477–491.
206. Johnson, S. R. (2007) The trouble with QSAR (or how I learned to stop worrying and embrace fallacy). J. Chem. Inf. Model. 48, 25–26.
207. Pavan, M., and Worth, A. P. Review of QSAR Models for Ready Biodegradation. http:/ecb.jrc.ec.europa.eu/documents/QSAR/QSAR_Review_Biodegradation.pdf (accessed September 23, 2009).
208. Warr, W. A. Proceedings of New Horizons in Toxicity Prediction. Lhasa Limited symposium event in collaboration with the University of Cambridge, December 2008. http:/www.qsarworld.com/files/Lhasa_Sympo-sium_2008_Report.pdf (accessed September 23, 2009).
209. Huynh, L., Masereeuw, R., Friedberg, T., Ingelman-Sundberg, M., and Manivet, P. (2009) In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: beyond the reduction of animal model use. Drug Discov. Today 14, 401–405.
210. Gramatica, P., Pilutti, P., and Papa, E. (2004) Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling. J. Chem. Inf. Comput. Sci. 44, 1794–1802.
211. Hewitt, M., Cronin, M. T. D., Madden, J. C., Rowe, P. H., Johnson, C., Obi, A., and Enoch, S. J. (2007) Consensus QSAR models: do the benefits outweigh the complexity? J. Chem. Inf. Model. 47, 1460–1468.
212. Matthews, E. J., Kruhlak, N. L., Benz, R. D., Contrera, J. F., Marchant, C. A., and Yang, C. (2008) Combined use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows software to achieve high-performance, high-confidence, mode of action-based predictions of chemical carcinogenesis in rodents. Toxicol. Mech. Methods 18, 189–206.
213. Abshear, T., Banik, G. M., D’Souza, M. L., Nedwed, K., and Peng, C. (2006) A model validation and consensus building environment. SAR QSAR Environ. Res. 17, 311–321.
214. Boyer, S., Arnby, C. H., Carlsson, L., Smith, J., Stein, V., and Glen, R. C. (2007) Reaction site mapping of xenobiotic biotransfor-mations. J. Chem. Inf. Model. 47, 583–590.
215. Terfloth, L., Bienfait, B., and Gasteiger, J. (2007) Ligand-based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substrates. J. Chem. Inf. Model. 47, 1688–1701.
216. Molecular Networks. BioPath. http:/www. mol-net.de/biopath/index.html (accessed September 25, 2009).
217. Schwaighofer, A., Schroeter, T., Mika, S., Hansen, K., ter Laak, A., Lienau, P., Reichel, A., Heinrich, N., and M€uller, K.-R. (2008) A probabilistic approach to classifying metabolic stability. J. Chem. Inf. Model. 48, 785–796.
218. Symyx Technologies. Metabolite. http:/www.symyx.com/products/databases/bio-activity/metabolite/index.jsp (accessed October 2, 2009).
219. Ridder, L., and Wagener, M. (2008) SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3, 821–832.
220. Button, W. G., Judson, P. N., Long, A., and Vessey, J. D. (2003) Using absolute and relative reasoning in the prediction of the potential metabolism of xenobiotics. J. Chem. Inf. Comput. Sci. 43, 1371–1377.
221. Leach, A. R., Gillet, V. J., Lewis, R. A., and Taylor, R. (2010) Three-Dimensional Pharmacophore Methods in Drug Discovery. J. Med. Chem. 53, 539–558
222. Wolber, G., Seidel, T., Bendix, F., and Langer, T. (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov. Today 13, 23–29.
223. Feng, J., Sanil, A., and Young, S. S. (2006) PharmID: pharmacophore identification using Gibbs sampling. J. Chem. Inf. Model. 46, 1352–1359.
224. Richmond, N. J., Willett, P., and Clark, R. D. (2004) Alignment of three-dimensional molecules using an image recognition algorithm. J. Mol. Graphics Modell. 23, 199–209.
225. Richmond, N., Abrams, C., Wolohan, P., Abrahamian, E., Willett, P., and Clark, R. (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J. Comput.-Aided Mol. Des. 20, 567–587.
226. Jain, A. N. (2004) Ligand-based structural hypotheses for virtual screening. J. Med. Chem. 47, 947–961.
227. Cole, J. C., Gardiner, E. J., Gillet, V. J., and Taylor, R. (2009) Development of test systems for pharmacophore elucidation, in Abstracts of Papers, 237th ACS National Meeting, Salt Lake City, UT, United States, March 22–26, 2009.
228. Patel, Y., Gillet, V. J., Bravi, G., and Leach, A. R. (2002) A comparison of the pharmacophore identification programs: catalyst, DISCO and GASP. J. Comput.-Aided Mol. Des. 16, 653–681.
229. Clark, R. D. (2009) Prospective ligand-and target-based 3D QSAR: state of the art 2008. Curr. Top. Med. Chem. 9, 791–810.
230. Deng, Z., Chuaqui, C., and Singh, J. (2003) Structural Interaction Fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding Interactions. J. Med. Chem. 47, 337–344.
231. Muegge, I., and Oloff, S. (2006) Advances in virtual screening. Drug Discov. Today: Technol. 3, 405–411.
232. Howe, T. J., Mahieu, G., Marichal, P., Tab-ruyn, T., and Vugts, P. (2006) Data reduction and representation in drug discovery. Drug Discov. Today 12, 45–53.
233. Ivanenkov, Y. A., Savchuk, N. P., Ekins, S., and Balakin, K. V. (2009) Computational mapping tools for drug discovery. Drug Discov. Today 14, 767–775.
234. Wagener, M., Sadowski, J., and Gasteiger, J. (1995) Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic AH receptor activity by neural networks. J. Am. Chem. Soc. 117, 7769–7775.
235. Bauknecht, H., Zell, A., Bayer, H., Levi, P., Wagener, M., Sadowski, J., and Gasteiger, J. (1996) Locating biologically active compounds in medium-sized heterogeneous datasets by topological autocorrelation vectors: dopamine and benzodiazepine agonists. J. Chem. Inf. Comput. Sci. 36, 1205–1213.
236. Shneiderman, B. (1992) Tree visualization with tree-maps: 2-D space-filling approach. ACM Trans. Graph. 11, 92–99.
237. Kibbey, C., and Calvet, A. (2005) Molecular property eXplorer: a novel approach to visualizing SAR using tree-maps and heat-maps. J. Chem. Inf. Model. 45, 523–532.
238. Yamashita, F., Itoh, T., Hara, H., and Hashida, M. (2006) Visualization of large-scale aqueous solubility data using a novel hierarchical data visualization technique. J. Chem. Inf. Model. 46, 1054–1059.
239. Lamping, J., Rao, R., and Pirolli, P. (1995) A focus þ context technique based on hyperbolic geometry for visualizing large hierarchies, in Proceedings of the SIGCHI conference on human factors in computing systems, Denver, Colorado, United States, ACM Press/Addi-son-Wesley Publishing Co.
240. Agrafiotis, D. K., Bandyopadhyay, D., and Farnum, M. (2006) Radial clustergrams: visualizing the aggregate properties of hierarchical clusters. J. Chem. Inf. Model. 47, 69–75.
241. Agrafiotis, D. K., and Xu, H. (2002) A self-organizing principle for learning non-linear manifolds. Proc. Natl. Acad. Sci. USA. 99, 15869–15872.
242. Agrafiotis, D. K., and Xu, H. (2003) A geodesic framework for analyzing molecular similarities. J. Chem. Inf. Comput. Sci. 43, 475–484.
243. Agrafiotis, D. K. (2003) Stochastic proximity embedding. J. Comput. Chem. 24, 1215–1221.
244. Patel, A., Chin, D. N., Singh, J., and Denny, R. A. (2006) Methods for describing a group of chemical structures. WO 2006023574.
245. Medina-Franco, J. L., Petit, J., and Maggiora, G. M. (2006) Hierarchical strategy for identifying active chemotype classes in compound databases. Chem. Biol. Drug Des. 67, 395–408.
246. Schuffenhauer, A., Ertl, P., Roggo, S., Wetzel, S., Koch, M. A., and Waldmann, H. (2006) The scaffold tree – visualization of the scaffold universe by hierarchical scaffold classification. J. Chem. Inf. Model. 47, 47–58.
247. Bemis, G. W., and Murcko, M. A. (1996) The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893.
248. Lipkus, A. H., Yuan, Q., Lucas, K. A., Funk, S. A., Bartelt, W. F., Schenck, R. J., and Trippe, A. J. (2008) Structural diversity of organic chemistry. A scaffold analysis of the CAS Registry. J. Org. Chem. 73, 4443–4451.
249. Schuffenhauer, A., Brown, N., Ertl, P., Jenkins, J. L., Selzer, P., and Hamon, J. (2007) Clustering and rule-based classifications of chemical structures evaluated in the biological activity space. J. Chem. Inf. Model. 47, 325–336.
250. Agrafiotis, D. K., Shemanarev, M., Connolly, P. J., Farnum, M., and Lobanov, V. S. (2007) SAR maps: a new SAR visualization technique for medicinal chemists. J. Med. Chem. 50, 5926–5937.
251. Kolpak, J., Connolly, P. J., Lobanov, V. S., and Agrafiotis, D. K. (2009) Enhanced SAR Maps: Expanding the Data Rendering Capabilities of a Popular Medicinal Chemistry Tool. J. Chem. Inf. Model. 49, 2221–2230.
252. Smellie, A. (2007) General purpose interactive physico-chemical property exploration. J. Chem. Inf. Model. 47, 1182–1187.
253. Krallinger, M., Erhardt, R. A.-A., and Valencia, A. (2005) Text-mining approaches in molecular biology and biomedicine. Drug Discov. Today 10, 439–445.
254. Ananiadou, S., Kell, D. B., and Tsujii, J.-I. (2006) Text mining and its potential applications in systems biology. Trends Biotechnol. 24, 571–579.
255. Erhardt, R. A. A., Schneider, R., and Blaschke, C. (2006) Status of text mining techniques applied to biomedical text. Drug Discov. Today 11, 315–325.
256. Banville, D. L. (2006) Mining chemical structural information from the drug literature. Drug Discov. Today 11, 35–42.
257. Banville, D. L. (2009) Mining chemical and biological information from the drug literature. Curr. Opin. Drug Discov. Dev. 12, 376–387.
258. Banville, D. L., (Ed.) (2009) Chemical Information Mining: Facilitating Litera-ture-Based Discovery, CRC Press, Boca Raton, FL.
259. RSC Prospect. http:/www.rsc.org/Publish-ing/Journals/ProjectProspect/index.asp (accessed October 2, 2009).
260. Batchelor, C. R., and Corbett, P. T. (2007) Semantic enrichment of journal articles using chemical named entity recognition, in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions Prague, Czech Republic, Association for Computational Linguistics, http://www. aclweb.org/anthology/W/W07/W07-1008. pdf (accessed October 2, 2009)
261. Corbett, P., Batchelor, C., and Teufel, S. (2007) Annotation of chemical named entities, in BioNLP 2007: Biological, translational, and clinical language processing, Prague, Czech Republic, Association for Computational Linguistics, http:/www.acl-web.org/anthology/W/W07/W07-1008.pdf (accessed October 2, 2009).
262. Kidd, R. Prospecting for chemistry in publishing. paper given at ICIC 2008, Nice France October 2008. http:/www.infonor-tics.eu/chemical/ch08/slides/kidd.pdf (accessed October 2, 2008).
263. RSC Ontologies. http:/www.rsc.org/ontologies/(accessed October 2, 2009).
264. Valko, A. T., and Johnson, A. P. (2009) CLiDE Pro: the latest generation of CLiDE, a tool for optical chemical structure recognition. J. Chem. Inf. Model. 49, 780–787.
265. Eigner-Pitto, V., Eiblmaier, J., U. Frieske, U., Isenko, L., Kraut, H., Saller, H., and Loew, P. Mining for chemistry in text and images. A real world example revealing the challenge, scope, limitation and usability of the current technology. paper given at Fraunhofer-Symposium on Text Mining, Bonn, September 29–30, 2008. http:/www.scai.fraunhofer.de/fileadmin/download/vortraege/tms_08/Valentina_ Eigner_Pitto.pdf (accessed October 2, 2009).
266. Filippov, I. V., and Nicklaus, M. C. (2009) Optical structure recognition software to recover chemical information: OSRA, an open source solution. J. Chem. Inf. Model. 49, 740–743.
267. Park, J., Rosania, G., Shedden, K., Nguyen, M., Lyu, N., and Saitou, K. (2009) Auto- mated extraction of chemical structure information from digital raster images. Chem. Cent. J. 3, 4.
268. Law, J., Zsoldos, Z., Simon, A., Reid, D., Liu, Y., Khew, S. Y., Johnson, A. P., Major, S., Wade, R. A., and Ando, H. Y. (2009) Route Designer: a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation. J. Chem. Inf. Model. 49, 593–602.
269. InfoChem. http:/www.infochem.de (accessed October 2, 2009).
270. Matthews, E. J. Current Approaches for Toxicity Prediction, in New Horizons in Toxicity Prediction. Lhasa Limited Symposium Event in Collaboration with the University of Cambridge. http:/www.qsarworld.com/lhasa_report1.php (accessed October 7, 2009).
271. Clark, R. D. (2009) At what point does docking morph into 3-D QSAR?, in Abstracts of Papers, 237th ACS National Meeting, Salt Lake City, UT, United States, March 22–26, 2009.
272. Scheiber, J., Chen, B., Milik, M., Sukuru, S. C. K., Bender, A., Mikhailov, D., Whitebread, S., Hamon, J., Azzaoui, K., Urban, L., Glick, M., Davies, J. W., and Jenkins, J. L. (2009) Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J. Chem. Inf. Model. 49, 308–317.
273. Schreiber, S. L., Kapoor, T. M., and Wess, G., (Eds.) (2007) Chemical Biology: From Small Molecules to Systems Biology and Drug Design, Wiley-VCH, Weinheim, Germany.