Searching Similarity

Searching for compounds in databases that are similar to query molecules is one of the most widely applied molecular similarity-based approaches. Commonly used similarity search tools have different levels of complexity, as discussed in the following. 

elegans, and D. melanogaster have been completely sequenced, as have many bacterial and viral genomes. More recently, an intermediate form of the human genome is now available in the form of a working draft. Given this explosion of data, the new challenge has become how to focus searches in such a way as to reduce search times and limit the number of results that must be examined. 

In database searching, the basic operation is to sequentially align a query sequence to each subject sequence in the database. The results are reported as a ranked hit list followed by a series of individual sequence alignments, plus various scores and statistics (e.g.. Fig. 8.9). As will be discussed in more detail below, that choice of search program, sequence database, and various optional parameters can have an 

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Location I http //www.ncbi.nlm.nih.gov/cgi-bin/BLAST/nph-blast Jform=1 

The query sequence is filtered for low complexity regions by default. 

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

The protein sequence database is also a text-numeric database with bibliographic links. It is the largest public domain protein sequence database. The current PIR-PSD release 75.04 (March, 2003) contains more than 280 000 entries of partial or complete protein sequences with information on functionalities of the protein, taxonomy (description of the biological source of the protein), sequence properties, experimental analyses, and bibliographic references. Queries can be started as a text-based search or a sequence similarity search. PIR-PSD contains annotated protein sequences with a superfamily/family classification. 

To become familiar with the basics of chemical structure similarity, similarity measures, and different approaches exploited within the similarity search process. 

Similarity search appears as an extremely useful tool for computer-aided structure elucidation as well as for molecular design. Here the similarity property principle is involved. This may be stated as ... 

Similarity searching is the database implementation of the similarity concept. Some of the steps involved in similarity searching are overviewed next, in the context of chemoinformatics. 

The atom pair, ap, and topological torsion, tt, descriptors are selected for illustrative purposes in the similarity searching context. 

Increasing the fuzziness of object description reduces the number of descriptors used and broadens the scope of a similarity search. At the same time, increasing fuzziness may reduce the discriminatory power of desaiptors to unacceptable levels. Therefore it is desirable to be able to control the degree of fuzziness of desaiptors. 

D similarity search methods are quite well developed. Thus, methods which attempt to find overlapping parts (atoms and functional groups) of the molecular moieties studied were reported first [31]. As discussed above for the case of 2D searching, these methods are of combinatorial complexity. To reduce this complexity some field-based methods have been introduced. In this case, the overlap of the fields of two structures is considered as a similarity measure. 

Similarity search appears as an extremely useful tool for computer-aided structure elucidation as well as molecular design. 

CACTVS is a chemical information system which provides 2D and 3D complete structure, substructure, and similarity search on plain files of structures. The... 

Following the similar structure - similar property principle", high-ranked structures in a similarity search are likely to have similar physicochemical and biological properties to those of the target structure. Accordingly, similarity searches play a pivotal role in database searches related to drug design. Some frequently used distance and similarity measures are illustrated in Section 8.2.1. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

To seat ch for available starting materials, similarity searches, substructure searches, and some classical retrieval methods such as full structure searches, name searches, empirical formula searches, etc., have been integrated into the system. All searches can be applied to a number of catalogs of available fine chemicals (c.g, Fluka 154]. In addition, compound libraries such as in-housc catalogs can easily be integrated. 

Figure 10.3-16. The principle of similarity searches. The query (target, precursor) as well as the catalog compound are transformed by the criterion maximum oxidation state". Since the transformation for both compounds results in the samie transformed structure, the catalog compound is presented to the user as a suitable starting material. The comparison of the structure is performed by a hashcode algorithm.

Figure lO.J-48. Nine of 41 hits for the similarity search Largest Ring Systetr... 

Figure lO.J-49. One hit for the similarity search Ring + Substitution Position"... 

Downs G M, P Willett and W Fisanick 1994. Similarity Searching and Qustering of Chemical Structure Databases using Molecular Property Data, journal of Chemical Information and Computer Sciences 34 1094-1102. 

Lipman, D ] and W R Pearson 1985. Rapid and Sensitive Protein Similarity Searches. Science 227 1435-1441. 

Downs G M and Peter Willett 1995. Similarity Searching in Databases of Chemical Structures. In Lipkowitz K B and D B Boyd (Editors) Reviews in Computational Chemistry Volume 7. New York, VCH Publishers, pp. 1-66. 

P, J M Barnard and G M Downs 1998. Chemical Similarity Searching. Journal of Chemical irmation and Computer Science 38 983-996. 

WR Pearson. Empirical statistical estimates for sequence similarity searches. J Mol Biol 276 71-84, 1998. 

Molecular similarity searching provides the possibility of finding unrelated but functionally analogous molecules. This is a very nice feature because many distinct structures in contact with a CSP often share the same active sites. The compounds which have a structure similar to the structure of the sample query can be displayed automatically in order of their similarity. The degree of similarity is measured by a numerical value on a scale of 0 to 100 that may be included in the output form. An example of a similarity search is shown in Fig. 4-3. In this example, a search is being performed for the AZT with a similarity value >65 %. 

Fig. 4-3. Molecular similarity searching of AZT in CHIRBASE. (Compounds reported in Refs. [7-12].)...

Auto Search This button initiates from a structure query two or three automated series of search exact and substructure searches in local desktop versions exact, substructure and similarity searches in network version (under ISIS/Host). All the result lists are saved in CHIRBASE using exact-auto , SSS-auto and SIMXX %-auto names. XX is the highest similarity search value (from 80 % to 40 %) allowing to retrieve hits in CHIRBASE. The records in all the lists are unique. The SSS-auto list does not include records that are in the exact-auto list. The SIMXX %-auto list does not include records that are in exact and SSS-auto lists. 

Utilization of intelligent systems in chiral chromatography starts with an original project called CHIRULE developed by Stauffer and Dessy [36], who combined similarity searching and an expert system application for CSP prediction. This issue has recently been reconsidered by Bryant and co-workers with the first development of an expert system for the choice of Pirkle-type CSPs [37]. 

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