SpringerProtocols: Abstract: A Web-Based Chemoinformatics System for Drug Discovery

A Web-Based Chemoinformatics System for Drug Discovery

By: Scott D. Bembenek³, Brett A. Tounge⁴, Steven J. Coats⁵, Charles H. Reynolds⁴

Abstract

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One of the key questions that must be addressed when implementing a chemoinformatics system is whether the tools will be designed for use by the expert user or by the “bench scientist.” This decision can impact not only the style of tools that are rolled out, but is also a factor in terms of how these tools are delivered to the end users. The system that we outline here was designed for use by the non-expert user. As such, the tools that we discuss are in many cases simplified versions of some common algorithms used in chemoinformatics. In addition, the focus is on how to distribute these tools using a web-services interface, which greatly simplifies delivering new protocols to the end user.

Affiliation(s): (3) Pharmaceutical Research & Development, L.L.C., Computer Aided Drug Discovery and Cheminformatics, Johnson & Johnson, San Diego, California, USA
(4) Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Computer Aided Drug Discovery, Spring House, Pennsylvania, USA
(5) Johnson & Johnson Pharmaceutical Research & Development, L.L.C., High-Throughout Chemistry, Spring House, Pennsylvania, USA

Book Title: Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery

Series: Methods in Molecular Biology | Volume: 275 | Pub. Date: May-12-2004 | Page Range: 65-84 | DOI: 10.1385/1-59259-802-1:065

Subject: Bioinformatics

Key Words: Chemoinformatics - databases - information systems - web-based tools - computational tools - combinatorial chemistry

References

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1.	Brown, F. K. (1998) Chemoinformatics: what is it and how does it impact drug discovery. Annu. Rep. Med. Chem. 33, 375–384.

2.	Pipeline Pilot (2003) 2.5, SciTegic, Inc.: San Diego, CA.

3.	DIVA (2002) 2.1, Accelrys, Inc.: San Diego, CA.

4.	Accord for Excel2000 (2000) 3.1, Accelyrs, Inc.: San Diego, CA.

5.	RS³ Discovery for Excel (2000) 2.0, Accelyrs, Inc., San Diego, CA.

6.	Lipinski, C. A. (2001) Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249. <Occurrence Type="DOI"><Handle>10.1016/S1056-8719(00)00107-6</Handle></Occurrence>

8.	ACD (2000) 2000.2, MDL Information Systems, Inc., San Leandro, CA.

9.	Willett, P., Barnard, J. M., and Downs, G. M. (1998) Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38, 983–997.

10.	Randic, M. (1990) Design of molecules with desired properties: a molecular similarity approach to property optimization. In Concepts and applications of molecular similarity, Johnson, M. A. and Maggiora, G. M. (eds.), John Wiley & Sons, New York, pp. 77–146.

11.	ISIS (2000) 2.3; MDL Information Systems, Inc., San Leandro, CA.

12.	Chen, L., Nourse, J. G., Christie, B. D., Leland, B. A., and Grier, D. L. (2002) Over 20 years of reaction access systems from MDL: a novel reaction substructure search algorithm. J. Chem. Inf. Comput. Sci. 42, 1296–1310.

13.	MDL File Formats, MDL Information Systems, Inc., San Leandro, CA.

14.	Seijas, J. A., Vazquez-Tato, M. P., and Martinez, M. M. (2000) Microwaveenhanced synthesis of 4-aminoquinazolines. Tetrahedron Lett. 41, 2215–2217. <Occurrence Type="DOI"><Handle>10.1016/S0040-4039(00)00090-3</Handle></Occurrence>

16.	Leach, A. R., Bradshaw, J., Green, D. V. S., Hann, M. M., and Delany, J. J. I. (1999) Implementation of a system for reagent selection and library enumeration, profiling, and design. J. Chem. Inf. Comput. Sci. 39, 1161–1172.

17.	Walters, W. P. and Murcko, M. A. (2000) Library filtering systems and prediction of drug-like properties. Methods Princ. Med. Chem. 10, 15–32.

18.	Matter, H., Baringhaus, K., Naumann, T., Klabunde, T., and Pirard, B. (2001) Computational approaches towards the rational design of drug-like compound libraries. Comb. Chem. High Throughput Screen. 4, 453–475.

19.	SMILES Format, Daylight Chemical Information Systems, Inc., Mission Viejo, CA.

20.	Walters, W. P., Ajay, and Murcko, M. A. (1999) Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 3, 384–7. <Occurrence Type="DOI"><Handle>10.1016/S1367-5931(99)80058-1</Handle></Occurrence>

21.	Sadowski, J. and Kubinyi, H. (1998) A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329.

22.	Muegge, I., Heald, S. L., and Brittelli, D. (2001) Simple selection criteria for drug-like chemical matter. J. Med. Chem. 44, 1841–1846.

23.	Mitchell, T. and Showell, G. A. (2001) Design strategies for building drug-like chemical libraries. Curr. Opin. Drug Discovery Dev. 4, 314–318.

24.	Lewis, R. A., Pickett, S. D., and Clark, D. E. (2000) Computer-aided molecular diversity analysis and combinatorial library design. Rev. Comput. Chem. Lipkowitz, K. B. and Boyd, D. B. (eds.), VCH Publishers, Inc., NY, vol. 16.

25.	Frimurer, T. M., Bywater, R., Nrum, L., Lauritsen, L. N., and Brunak, S. (2000) Improving the odds in discriminating “drug-like” from “non drug-like” compounds. J. Chem. Inf. Comput. Sci. 40, 1315–1324.

26.	Ajay, Walters, W. P., and Murcko, M. A. (1998) Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? J. Med. Chem. 41, 3314–3324.

27.	Anzali, S., Barnickel, G., Cezanne, B., Krug, M., Filimonov, D., and Poroikov, V. (2001) Discriminating between drugs and nondrugs by prediction of activity spectra for substances (PASS). J. Med. Chem. 44, 2432–2437.

28.	Blake, J. F. (2000) Chemoinformatics—predicting the physicochemical properties of “drug-like” molecules. Curr. Opin. Biotechnol. 11, 104–107. <Occurrence Type="DOI"><Handle>10.1016/S0958-1669(99)00062-2</Handle></Occurrence>

29.	Böhm, H.-J. and Stahl, M. (2000) Structure-based library design: molecular modeling merges with combinatorial chemistry. Curr. Opin. Chem. Biol. 4, 283–286. <Occurrence Type="DOI"><Handle>10.1016/S1367-5931(00)00090-9</Handle></Occurrence>

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