Query vector

The hypothetical enantiophore queries are constructed from the CSP receptor interaction sites as listed above. They are defined in terms of geometric objects (points, lines, planes, centroids, normal vectors) and constraints (distances, angles, dihedral angles, exclusion sphere) which are directly inferred from projected CSP receptor-site points. For instance, the enantiophore in Fig. 4-7 contains three point attachments obtained by ... 

Figure 6.10. Sketch of the binary-tree-data structure used in ISAT. The initial tree is empty, and thus the tree is grown by adding leaves and nodes. Traversing the binary tree begins at the first node and proceeds using the cutting-plane vectors until a leaf is reached. The final structure depends on the actual sequence of query points.

Analysis of molecular similarity is based on the quantitative determination of the overlap between fingerprints of the query structure and all database members. As descriptors of a given molecule can be considered as a vector of real or binary attributes, most of the similarity measures are derived as vectorial distances. Tanimoto and Cosine coefficients are the most popular measures of similarity.Definitions of similarity metrics are collected in Table 3. 

Once we have determined F, we may query a particular state of deformation as to the disposition of the vector dX as a result of the deformation. For example, the simplest question one might ask about the vector between two neighboring material particles is how its length changes under deformation. To compute the length change, we compute the difference... 

If a database structure contains a fragment isomorphic to one of the screen structures, the corresponding bit is set to 1. Consequently, every bit set in the query molecule must appear in the database molecule bit vector to ensure the presence of the corresponding screen fragment. Comparing the screen vectors of query and database molecules can reduce the search effort dramatically if the screen fragments have been selected adequately. 

The molecules and infrared spectra selected for training have a profound influence on the radial distribution function derived from the CPG network and on the quality of 3D structure derivation. Training data are typically selected dynamically that is, each query spectrum selects its own set of training data by searching the most similar infrared spectra, or most similar input vector. Two similarity measures for infrared spectra are useful ... 

By using the 3D arrangement of atoms in a molecule and the calculated physicochemical properties of these atoms, it is possible to calculate molecular descriptors. Since the descriptor is typically a mathematical vector of a fixed length, we can use it for a fast search in a database, provided that the database contains the equivalent descriptor for each data set and that the descriptor is calculated for the query. We have seen before that particularly similarity and diversity can be excellently expressed with molecular descriptors. 

Chasing with SQL. As an alternative, the naive chase of a set of tgds on a given source instance I can be naturally implemented using SQL. Given a tgd first-order query with free variables x over S. We may execute Q (I) using SQL to find all vectors of constants that satisfy the premise. 

FIGURE 10.3 The LOCkey algorithm. A sequence unique data set of localization annotated SWISS-PROT proteins was first compiled. Keywords were extracted for these proteins and merged with any keywords found in homologues. The keywords were represented as binary vectors in the Trusted Vector Set. An unknown query was first annotated with keywords through identification of SWISS-PROT homologues. Keywords for the query were represented as binary vectors. All possible keyword combinations were constructed (the SUB vectors). The best matching vector was found based on entropy criteria (see Methods). This vector was used to infer localization for the query. 

A normalized scalar product of two spectral vectors was used during matching as the similarity measure, and sequential searches through the entire library were always performed. Thus, each spectrum in turn was treated as a query. To simulate small variances in data acquisition, and/or spectral differences for very similar compounds, 1% and 5% of random white noise, has been added. Appropriate decomposition (wavelet or PCA) was then performed, and the resulting vector was compared to each of the spectral vectors in the compressed library. 

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