Similarity searching efficiency

Hagadone, T.R. Molecular substructure similarity searching efficient retrieval in two-dimensional structure databases. 

Hagadone, T. R. (1992) Molecular substructure similarity searching Efficient retrieval in two-dimensional structure databases../. Chem. Inf. Comput. Sci. 32, 515-521. 

Hagadone, T.R. Molecular Substructure Similarity Searching - Efficient Retrieval in 2-Dimensional Structure Databases. J. Chem. Inf. Comput.Sci., 1992,32,515-521. 

TR Hagadone. Molecular substructure similarity searching—efficient retrieval in 2-dimensional structure databases. J Chem Inf Comput Sci 32 515-521, 1992. 

This ambiguous representation of chemical structures as a string allows a veiy efficient similarity search,... 

The E-state indices may define chemical spaces that are relevant in similarity/ diversity search in chemical databases. This similarity search is based on atom-type E-state indices computed for the query molecule [55]. Each E-state index is converted to a z score, Z =(% -p )/0 , where is the ith E-state atomic index, p is its mean and O is its standard deviation in the entire database. The similarity was computed with the EucHdean distance and with the cosine index and the database used was the Pomona MedChem database, which contains 21000 chemicals. Tests performed for the antiinflamatory drug prednisone and the antimalarial dmg mefloquine as query molecules demonstrated that the chemicals space defined by E-state indices is efficient in identifying similar compounds from drug and drug-tike databases. 

One element of database generation that is a key consideration is whether to expand the representative compounds to include alternative tautomers, protonated and deprotonated forms of the molecule, and also to enumerate stereochemistry fully if not specified in the input. Depending on the molecules in question and the options considered, these can lead to a 10-fold increase in the size of the database to be explored. However, such an expansion is necessary if methods are used that are sensitive to such chemical precision (e.g., docking). For 3D similarity searching, it is sometimes more efficient to consider various modifications to the query, leading to multiple searches against a smaller database. 

Abrahamian, E., Fox, P.C., Naerum, L., Christensen, I.T., Thogersen, H., and Clark, R.D. Efficient generation, storage, and manipulation of fully flexible pharmacophore multiplets and their use in 3D similarity searching./. Chem. 

Yu, N., Bakken, G. A. (2009) Efficient exploration of large combinatorial chemistry spaces by monomer-based similarity searching. J Chem Inf Model 49, 745-755. 

Descriptors based on pattern functions are helpful tools for a quick recognition of substructures. A pattern-search algorithm based on binary pattern descriptors can then be used for substructure search. However, patterns and other characteristics of descriptors that seem to indicate unique features should be investigated carefully. With these descriptors 3D similarity searches for complete structures or substructures in large databases are possible and computationally very efficient. In addition, descriptors can serve as the basis for a measure for the diversity of compounds in large data sets, a topic that is of high interest in combinatorial chemistry. 

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