Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept
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Success in small molecule screening relies heavily on the preselection of compounds. Here, we present a strategy for the enrichment of chemical libraries with potentially bioactive compounds integrating the collected knowledge of medicinal chemistry. Employing a genetic algorithm, substructures typically occurring in bioactive compounds were identified using the World Drug Index. Availability of compounds containing the selected substructures was analysed in vendor libraries, and the substructure-specific sublibraries were assembled. Compounds containing reactive, undesired functional groups were omitted. Using a diversity filter for both physico-chemical properties and the substructure composition, the compounds of all the sublibraries were ranked. Accordingly, a screening collection of 16,671 compounds was selected. Diversity and chemical space coverage of the collection indicate that it is highly diverse and well-placed in the chemical space spanned by bioactive compounds. Furthermore, secondary assay-validated hits presented in this study show the practical relevance of our library design strategy.
KeywordsBio informatics Drug design High throughput screening Library design Molecular diversity
We thank Hans-Dieter Höltje and Victoria Higman for critical reading of the manuscript. The screening data were provided by Jörn Saupe, Samuel Beligny, Svantje Behnken and Susann Matthes. Three institutes, namely the Helmholtz Centre for Infection Research (HZI), the Max Delbrück Centrum (MDC), and the Leibniz Institut für Molekulare Pharmakologie (FMP) co-financed the described screening library, which is now ready to use for supporting screening projects. Extensions to this library are currently made by the MPI in Dortmund, the University of Oslo and the University of Konstanz.
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- 9.Schmidt MF, Isidro-Llobet A, Lisurek M, El-Dahshan A, Tan J, Hilgenfeld R, Rademann J (2008) Sensitized detection of inhibitory fragments and iterative development of non-peptidic protease inhibitors by dynamic ligation screening. Angew Chem Int Ed Engl 47: 3275–3278. doi: 10.1002/anie.200704594 CrossRefPubMedGoogle Scholar
- 17.Evans BE, Rittle KE, Bock MG, DiPardo RM, Freidinger RM, Whiter WL, Lundell GF, Veber DF, Anderson PS, Chang RSL, Lotti VJ, Cerino DJ, Chen TB, Kling PJ, Kunkel KA, Springer JP, Hirshfield J (1988) Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J Med Chem 31: 2235–2246. doi: 10.1021/jm00120a002 CrossRefPubMedGoogle Scholar
- 20.WDI, Derwent World Drug Index, Release 2005, Derwent Information Ltd., LondonGoogle Scholar
- 21.ChemDiv Inc., ChemDiv Chemical Database, http://www.chemdiv.com, 6605 Nancy Ridge Drive, San Diego, CA, 92121, USA
- 22.MOE Molecular Operating Environment, version 2005.06, Chemical Computing Group Inc., Montreal, Quebec, CanadaGoogle Scholar
- 25.ChemACX, CambridgeSoft, http://www.chemacx.com, 100 CambridgePark Drive, Cambridge, MA, 02140, USA
- 26.Dictionary of Natural Products, version 14.1 (2005). Chapman & Hall/CRC Informa, LondonGoogle Scholar