Abstract
The approach termed Determination and Mapping of Activity-Specific Descriptor Value Ranges (MAD) is a conceptually novel molecular similarity method for the identification of active compounds. MAD is based on mapping of compounds to different (multiple) activity class-selective descriptor value ranges. It was recently developed in our laboratory and successfully applied in initial virtual screening trials. We have been able to show that selected molecular property descriptors can display a strong tendency to respond to unique features of compounds having similar biological activity, thus providing a basis for the approach. Accordingly, a crucial step of the MAD algorithm is the identification of activity-sensitive descriptor settings. The second critical step of MAD is the subsequent mapping of database compounds to activity-sensitive descriptor value ranges in order to identify novel active molecules. This chapter describes the optimization of the MAD approach and evaluates the second-generation algorithm on a number of different compound activity classes with a particular focus on the recognition of remote molecular similarity relationships.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brooijmans, N., Kuntz, I. D. (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biolmol Struct 32, 335–373.
Shoichet, B. K. (2004) Virtual screening of chemical libraries. Nature 432, 862–865.
Kitchen, D. B., Decornez, H., Furr, J. R., et al. (2004) Structure-based vir tual screening and lead optimization: methods and applications. Nature Rev Drug Discov 3, 935–949.
Bajorath, J. (2002) Integration of virtual and high-throughput screening. Nature Rev Drug Discov 1, 882–894.
Green, D. V. (2003) Virtual screening of virtual libraries. Prog Med Chem 41, 61–97.
Stahura, F. L., Bajorath, J. (2005) New methodologies for ligand-based virtual screening. Curr Pharm Des 11, 1189–1202.
Johnson, M., Maggiora, G. M., eds. (1990) Concepts and Applications of Molecular Similarity. John Wiley & Sons, New York.
Martin, Y. C. (1992) 3D database searching in drug design. J Med Chem 35, 2145–2154.
Esposito, E. X., Hopfinger, A. J., Madura, J. D. (2004) Methods for applying the quantitative structure-activity relationship paradigm. Methods Mol Biol 275, 131–214.
Xue, L., Godden, J. W., Bajorath, J. (2003) Mini-fingerprints for virtual screening: design principles and generation of novel prototypes based on information theory. SAR QSAR Env. Res. 14, 27–40.
Bajorath, J. (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41, 233–245.
Gillet, V. J., Willett, P., Bradshaw, J. (2003) Similarity searching using reduced graphs. J Chem Inf Comput Sci 43, 338–345.
Stahura, F. L. and Bajorath, J. (2003) Partitioning methods for the identification of active molecules. Curr Med Chem 10, 707–715.
Pearlman, R. S., Smith, K. M. (1998) Novel software tools for chemical diversity. Perspect Drug Discov Design 9, 339–353.
Jorissen, R. N., Gilson, M. K. (2005) Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 44, 549–561.
Todeschini, R., Consonni, V. (2000) Handbook of molecular descriptors, in (Mannhold, R., Kubinyi, H., Timmer-man, H., eds.), Methods and Principles in Medicinal Chemistry, vol. 11. Wiley, New York.
Godden, J. W., Furr, J. R., Xue, L., et al. (2004) Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality. J Chem Inf Comput Sci 44, 21–29.
Eckert, H., Bajorath, J. (2006) Determination and mapping of activity-specific descriptor value ranges (MAD) for the identification of active compounds. J Med Chem 49, 2284–2293.
Cramer, R. D., Jilek, R. J., Guessregen, S., et al. (2004) “Lead hopping.” Validation of topomer similarity as a superior predictor of biological activities. J Med Chem 47, 6777–6791.
Molecular Drug Data Report (MDDR). MDL Information Systems, Inc., San Leandro, CA.
MOE (Molecular Operating Environment), Chemical Computing Group, Inc., Montreal, Quebec, Canada.
Irwin, J. J., Shoichet, B. K. (2005) ZINC: a free database of commercially available compounds for virtual screening. J Chem Inf Model 45, 177–182.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Humana Press, a part of Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Eckert, H., Bajorath, J. (2008). Optimization of the MAD Algorithm for Virtual Screening. In: Keith, J.M. (eds) Bioinformatics. Methods in Molecular Biology™, vol 453. Humana Press. https://doi.org/10.1007/978-1-60327-429-6_18
Download citation
DOI: https://doi.org/10.1007/978-1-60327-429-6_18
Publisher Name: Humana Press
Print ISBN: 978-1-60327-428-9
Online ISBN: 978-1-60327-429-6
eBook Packages: Springer Protocols