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Triangle descriptors

PPP-triangle descriptors substructure descriptors ( pharmacophore-based descriptors)... [Pg.596]

Used BCI, MACCS, MolconnZ 196 and typed graph triangle descriptors. Best models with BCI descriptors and SVM. [Pg.325]

Fig. 5. A plot of the two largest principal components of the training set developed from the 312 compounds and 15 molecular descriptors identified by the pattern-recognition GA. The plane defined by the two largest principal components accounts for 35% of the total cumulative variance. Circles are the musks inverted triangles are the nonmusks M = musks from the prediction set projected onto the principal component plot N = nonmusks from the prediction set projected onto the principal component plot. Fig. 5. A plot of the two largest principal components of the training set developed from the 312 compounds and 15 molecular descriptors identified by the pattern-recognition GA. The plane defined by the two largest principal components accounts for 35% of the total cumulative variance. Circles are the musks inverted triangles are the nonmusks M = musks from the prediction set projected onto the principal component plot N = nonmusks from the prediction set projected onto the principal component plot.
Enantiotopie groups A bound to a center Xi.AABC) or a center X(ABCD) are classified by the descriptor. Re or Si, of the corresponding chirotopic half-space defined by the triangle ABC, in which the group to be specified resides. A relevant question here would be to ask what property the descriptors RejSi describe. Logic demands that it describes the sense of local chirality. [Pg.18]

Figure 12.2 The compound sets from which the hERG (filled triangles) and the BBB (open circles) in-silico filters were derived are compared by principal components analysis, and one structure of each set is depicted. Ghose and Crippen descriptors were calculated for all the molecules, and after autoscaling, the compounds were projected onto the scores plot of the first two components [74],... Figure 12.2 The compound sets from which the hERG (filled triangles) and the BBB (open circles) in-silico filters were derived are compared by principal components analysis, and one structure of each set is depicted. Ghose and Crippen descriptors were calculated for all the molecules, and after autoscaling, the compounds were projected onto the scores plot of the first two components [74],...
PPP eigenvalues eigenvalue-based descriptors PPP pairs - substructure descriptors PPP triangles substructure descriptors predictor variables independent variables -> data set prime ID number ID numbers... [Pg.350]

For each 3-point pharmacophore, all the combinations of three interatomic distances are determined, considering only those combinations valid that satisfy the triangle rule, that is, the length of each side of a triangle caimot exceed the sum of the lengths of the other two sides, otherwise this would produce a geometrically impossible object. Moreover, redundant pharmacophores related to symmetry are usually eliminated. The resulting number of descriptors is 10549. [Pg.777]

Unique is the number of unique 3-point pharmacophores displayed by the whole library, Conscore is a term that can force a design to fill specific 3-point pharmacophores and avoid others i.e. to complement an existing compound collection). Partscore is used to force a distribution of shape related descriptors heavy atoms (ha), largest triangle perimeter for a pharmacophore in the library (pp) and largest triangle are for a pharmacophore in the library (pa). S is the total number of molecules that pass certain filters, such as a limit of molecular flexibility. Totpharm is the total of pharmacophores displayed by the entire library and Flex is the normalised number of conformations accessed by the library of n molecules. The terms a, b, Y and Z are user defined weights. [Pg.382]


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See also in sourсe #XX -- [ Pg.103 ]




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