论文信息 - Enhancing the diversity of a corporate database using chemical database clustering and analysis

Enhancing the diversity of a corporate database using chemical database clustering and analysis

SummaryThe contribution that the Chemical Abstracts structural database (CAST-3D) and the Maybridge database (MAY) would make to diversifying the structural information and property space spanned by our corporate database (CBI) is assessed. A subset of the CAST-3D database has been selected to augment the structural diversity of various electronic databases used in computer-assisted drug design projects. The analysis of the MAY database directly offers the potential to expand the CBI compound library, but also provides a source for structural diversity in a format suitable for computer-assisted database searching and molecular design. The analysis performed is twofold. First, a nonhierarchical clustering technique available in the Daylight clustering package is applied to evaluate the structural differences between databases. The comparison is then extended to analyze various structure-derived property spaces calculated from molecular descriptors such as the logarithm of the octanol-water partition coefficient (CLOGP), the molar refractivity (CMR) and the electronic dipole moment (CDM). The diversity contribution of each database to these property spaces is quantified in relation to our corporate database.

[1] Anil K. Jain,et al. Validity studies in clustering methodologies , 1979, Pattern Recognit..

[2] John M. Barnard,et al. Clustering of chemical structures on the basis of two-dimensional similarity measures , 1992, J. Chem. Inf. Comput. Sci..

[3] D C Spellmeyer,et al. Measuring diversity: experimental design of combinatorial libraries for drug discovery. , 1995, Journal of medicinal chemistry.

[4] Peter Willett,et al. Implementation of nearest-neighbor searching in an online chemical structure search system , 1986, J. Chem. Inf. Comput. Sci..

[5] Ray A. Jarvis,et al. Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.

[6] Peter Willett,et al. Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data , 1994, J. Chem. Inf. Comput. Sci..

[7] P. Willett,et al. Implementation of nonhierarchic cluster analysis methods in chemical information structure search , 1986 .

[8] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..