Structural fragment descriptors

The similarity matrices are constructed by one in-house program developed inside CHIRBASE using the application development kit of ISIS. They contain the similarity coefficients as expressed by the Tanimoto method. In ISIS, the Tanimoto coefficients are calculated from a set of binary descriptors or molecular keys coding the structural fragments of the molecules. 

Further analysis yielded new models for each of the chemical classes with improved statistical significance. The final model for nonaromatics contained six descriptors and had an Rs of 0.932 (leave-one-out 0.878), the final model for the aromatics contained 21 descriptors and had an Rs of 0.942 (leave-one-out 0.823), and the final model for the heteroaromatics contained 13 descriptors and had an Rs value of 0.863 (leave-one-out 0.758). These statistical results were considered reliable enough for the models to be regarded as predictive. The analysis did yield some interesting insights into the impact of various structural fragments on human oral bioavailability. However, these observations were based on the sign of the coefficient and so must be treated with some caution. 

As illustrated in the next section, the use of biological fingerprints, such as from a BioPrint profile, provides a way to characterize, differentiate and cluster compounds that is more relevant in terms ofthe biological activity of the compounds. The data also show that different in silico descriptors based on the chemical structure can produce quite different results. Thus, the selection of the in silico descriptor to be used, which can range from structural fragments (e.g. MACCS keys), through structural motifs (Daylight keys) to pharmacophore/shape keys (based on both the 2D structure via connectivity and from actual 3D conformations), is very important and some form of validation for the problem at hand should be performed. 

Aromatic model 31 descriptors (22 Structural fragments + 5 MCI or Charge calculations)... 

Aliphatic model 22 descriptors (21 Structural fragments and 1 MCI) Training (aliphatic) model n = 134 Overall 91% correct... 

In addition to cell-based partitioning, statistical partitioning methods are widely used for compound classification. One of the most popular approaches is recursive partitioning (Rusinko et al. 1999), a decision tree method, as illustrated in Figure 1.8. Recursive partitioning divides data sets along decision trees formed by sequences of molecular descriptors. At each node of the tree, a descriptor-based decision is made and the molecular data set is subdivided. For example, a chosen descriptor could simply detect the presence or absence of a structural fragment in a molecule. Alternatively, the... 

This chapter focuses on step 3. For step 1, descriptors may include property values, biological properties, topological indexes, and structural fragments. The performance of these descriptors and forms of representation have been analyzed by Brown and Brown and Martin. Similarity searching for step 2 has been discussed by Downs and Willett characteristics of various similarity measures have been discussed by Barnard, Downs, and Willett. " For step 4, little has been published specifically about visualization and analysis of results for chemical data sets. Flowever, most publications that focus on implementing systems that utilize clustering do provide details of how the results were displayed or analyzed. 

Two-dimensional descriptors are normally represented by linear bit strings indicating the presence or the absence of some properties in the molecule Examples include structural fragments (structural keys, 40, 41, 36), specific atom paths with predefined... 

The epoch of QSAR (Quantitative Structure-Activity Relationships) studies began in 1963-1964 with two seminal approaches the a-p-7i analysis of Hansch and Fujita " and the Free-Wilson method. The former approach involves three types of descriptors related to electronic, steric and hydrophobic characteristics of substituents, whereas the latter considers the substituents themselves as descriptors. Both approaches are confined to strictly congeneric series of compounds. The Free Wilson method additionally requires all types of substituents to be suflficiently present in the training set. A combination of these two approaches has led to QSAR models involving indicator variables, which indicate the presence of some structural fragments in molecules. 

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