Structural data mining

Case Study 7 Crystals and Their Structure (Data Mining and Storage)... 

A most important task in the handling of molecular data is the evaluation of "hidden information in large chemical data sets. One of the differences between data mining techniques and conventional database queries is the generation of new data that are used subsequently to characterize molecular features in a more general way. Generally, it is not possible to hold all the potentially important information in a data set of chemical structures. Thus, the extraction of relevant information and the production of reliable secondary information are important topics. 

Nowadays a broad range of methods is available in the field of chemoinfor-matics. These methods will have a growing impact on drug design. In particular, the discovery of new lead structures and their optimization will profit by virtual saeening [17, 66, 100-103]. The huge amounts of data produced by HTS and combinatorial chemistry enforce the use of database and data mining techniques. 

Du, Y., Liang, Y., Yun, D. J. Chem. Inf. Comput. Sci. 42, 2002, 1283-1292. Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

D similarity and substructure searching are among the most widely used methods for mining structural data (9). The basic principle of these methods is that compounds with structural features in common will have similar properties,... 

It makes sense to use data mining of structural features and molecular properties to assist in the building of alerts for more precise structural features. Various in vitro testing results can be used to describe structures at the compound and feature levels. These profiles can be used either to build weight of evidence strategies or as predictors for prediction models. 

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