Query compounds

Current chemical information systems offer three principal types of search facility. Structure search involves the search of a file of compounds for the presence or absence of a specified query compound, for example, to retrieve physicochemical data associated with a particular substance. Substructure search involves the search of a file of compounds for all molecules containing some specified query substructure of interest. Finally, similarity search involves the search of a file of compounds for those molecules that are most similar to an input query molecule, using some quantitative definition of structural similarity. 

XLOGP, version 2.0, is written in C-h-. The program reads the query compound (represented in SYBYL/MOL2 format), performs atom classification, detects correction factors, and then calculates the log P value. Due to its simple methodology the program is quite fast. It can process about 100 medium-sized compounds per second on an SGI 02/R10000 workstahon. 

The importance of an appropriate transformation of mass spectra has also been shown for relationships between the similarity of spectra and the corresponding chemical structures. If a spectra similarity search in a spectral library is performed with spectral features (instead of the original peak intensities), the first hits (the reference spectra that are most similar to the spectrum of a query compound) have chemical structures that are highly similar to the query structure (Demuth et al. 2004). Thus, spectral library search for query compounds—not present in the database—can produce useful structure information if compounds with similar structures are present. 

Similarity Distance In the case of a nonlinear method such as the k Nearest Neighbor (kNN) QSAR [41], since the models are based on chemical similarity calculations, a large similarity distance could signal query compounds that are too dissimilar to the... 

Figure 1.1.1 Examples of property estimation techniques (Sw = water solubility Kow = octanol-water partition coefficient). Chlorobenzene is the query compound. F are fragment or atom constants / is a property-property or a structure-property relationship.

Application of a specific QPPR consistent with eq. 1.4.1 to estimate Y for a query compound requires the following ... 

The query compound belongs to the same compound class(es) defined by the training and evaluation sets. 

In addition, one has to qualify the estimation result by identifying further possible limitations of the used model. For example, if a model applies to liquids only, one has to assure that the query compound is a liquid. 

The discussion above indicates that QPPR models must be selected carefully, considering the structure of query compound and its relationship to the structures represented in the training set. It is often useful to employ different models and to compare the results. 

These characteristics distinguish QPPRs from QSPRs in terms of their statistical evaluation and in terms of their applicability. Note that to estimate the property of interest with a QPPR model, certain other properties of a query compound must be available. 

The GI approach relates the property, Yg, of a query compound to the known property, To, of a database compound, by the equation [36]... 

Figure 1.7.4 Database compound A and query compound X related through RE -C I C.S IC.

The nearest-neighbor (NN) approach relates the property of a query compound, Yq, to the properties of k nearest-neighbor (kNN) compounds selected from a database. The general model is... 

The kNN compounds used to estimate the query property are those k database compounds that exhibit the greatest similarity to the query compound. Basak and Grunwald [42] and Basak et al. [47] use the mean of the kNN property values... 

Clearly, this example demonstrates how important it is to recognize the structural difference between similar compounds and base property estimation on AStructure-ATm relationships instead of simply setting their Tm values equal to each other. Figures 10.4.2 to 10.4.6 illustrate similarity-based estimation of Tm using the method of Joback and Reid (Section 9.3). For comparison, the observed Tm values [4] for the query compounds are given below ... 

Virtual Screening Based on Multiple Query Compounds... 

Figure 2.35. Web-based chemistry search tool that combines multiple search criteria to query compound structures and related data. Uses Marvin for structure input and viewing and JChem Base for structural search, designed by Zhenbin Li of Neurogen Corporation...

Once the desired structure is generated the user should be able to use its representation (the connection table) in many different ways to store it, to combine it with other structures, supplement it with textual information, to decompose it to fragments, add it to a collection, use it as a target or query compound in different searches or procedures, use it in different applications such as simulation of spectra, determination of properties, etc. calculate molecular formula, draw it on a plotter, etc. 

This task was done by comparing the new compound first against the KEGG COMPOUND database to retrieve a list of candidate compounds that are most similar to the query. The matched compounds are then queried against the RDM pattern library to retrieve a list of putative RDM patterns. In the third step, the query compound is transformed into new possible compounds based on the retrieved transformation patterns. These newly generated compounds are then used iteratively as a new query to repeat the prediction cycle until no new transformations can be found. This approach retrieved successfully the degradation pathway for 1,2,3,4-tetrachlorobenzene (34). 

The intersection of different types of spectral information appears to be an extremely promising approach. However, it does not necessarily lead to a full determination of the query compound structure, because frequently the different methods provide overlapping information about certain structural units and no information about others. 

TOPKA T Toxicity Prediction by Komputer Assisted Technology (TOPKAT) (http //www.accelrys.com/products/topkat/index.html) uses QSAR models for prediction of various toxicological properties such as mutagenicity, developmental toxicity potential, carcinogenicity, and skin/eye irritancy (see also Chapter 18). It employs the Optimum Prediction Space (OPS) technology to assess whether the query compound is well represented in its QSAR models and provides a confidence level on its prediction [85],... 

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