Descriptors BCUTs

Weighting schemes are used to generate several —> weighted matrices, from which topochemical indices are derived, and to directly calculate WHIM descriptors, autocorrelation descriptors, —> GETAWAY descriptors, RDF descriptors, —> 3D-MoRSE descriptors, BCUT... 

Finally, there is one particular topological descriptor that should be mentioned because it has found considerable utility in a number of studies. This property, originally called a chemically intuitive molecular index by its inventor, Frank Burden, is based on a modified molecular adjacency matrix. The approach was extended by Pearlman " to take account of atomic charge, polarisability and hydrogen bonding ability, properties which are termed BCUT descriptors. BCUTs are available in a number of software packages and in addition to their utility in QSAR modelling, they have been shown to be useful measures of chemical similarity. 

Z eb index, Wiener index. Balaban J index, connectivity indices chi (x), kappa (k) shape indices, molecular walk counts, BCUT descriptors, 2D autocorrelation vector... 

Before the comparative molecular field analysis (CoMFA), BCUT descriptors, 4D-QSAR, and HYBOT descriptors arc discussed in more detail, some further descriptors are listed briefly. 

The BCUT descriptors (Buiden-CAS-University of Texas eigenvalues) [52], are commonly used, are eigenvalue-based, and include 3D information also. 

Dissimilarity and clustering methods only describe the compounds that are in the input set voids in diversity space are not obvious, and if compounds are added then the set must be re-analyzed. Cell-based partitioning methods address these problems by dividing descriptor space into cells, and then populating those cells with compounds [67, 68]. The library is chosen to contain representatives from each cell. The use of a partition-based method with BCUT descriptors [69] to design an NMR screening library has recently been described [70]. 

Linear representations are by far the most frequently used descriptor type. Apart from the already mentioned structural keys and hashed fingerprints, other types of information are stored. For example, the topological distance between pharmacophoric points can be stored [179, 180], auto- and cross-correlation vectors over 2-D or 3-D information can be created [185, 186], or so-called BCUT [187] values can be extracted from an eigenvalue analysis of the molecular adjacency matrix. 

Mason, J.S. and Beno, B.R. Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints simultaneous optimization and structure-based diversity. /. Mol. 

A significant percentage of any compound library will inevitably fall into small clusters unsuitable to rigorous statistical evaluation. These must be considered separately - in our case, using diversity analysis with BCUT descriptors [39] to supplement the list derived from clustering. Throughout this process, we use visualization to assess data quality, identify potential problems such as edge effects, and check trends and patterns. 

In the technique of post hoc design, a set of descriptors are built up by examination of a set of compounds active at a particular receptor family or sub-class. Normally, the set of drugs would be from a commercial database such as MDDR or the Merck Index, etc. and the descriptors would usually be substructural fragment or key based. One example would be the GPCR-PA+ sub-class referred to above, where BCUT descriptors have been used to aid the design of a focused library of aroimd 2000 compoimds based on 8 scaffolds. Libraries have also been constructed based on peptidomimetic principles as well as on the concepts of privileged structures. ... 

Vectors whose components have continuous values correspond to the more traditional types of vectors found in the physical sciences. They are of identical form to the discrete-valued vectors (see Eq. 2.16) except that the components, vA(xk), are continuous valued. In chemoinformatics, however, the nature of the components is considerably different from those typically found in physics. For example, physiochemical properties, such as logP, solubility, melting point, molecular volume, Hammett ap parameters, and surface charge, as well as other descriptors derived explicitly for the purpose, such as BCUTs... 

Fig. 2. Example of rough activity landscape. This figure shows the activity landscape for a series of related antibacterial compounds plotted in using the 2D BCUT descriptors to arrange the compounds. (A) Shows how the compounds are arrayed in a 2D representation of the chemistry space with the height of the marker being proportional to the minimum inhibitor concentration of the compounds [the smaller the minimum inhibitory concentration (MIC) the more potent the compound]. (B) This second panel presents the upper figure as a 2D figure to enhance the sharp cutoff between active and inactive compounds, emphasizing the point that activity landscapes are rarely smooth continuous functions.

Similar compounds were selected for further testing using BCUT descriptors and Euclidean distance to identify the untested compounds closest to the initial hit (24). 

Pirard, B. and Pickett, S. D. (2000) Classification of kinase inhibitors using BCUT descriptors. J. Chem. Inf. Comput. Sci. 40, 1431-1440. 

In our study we compare two diversity-driven design methods (uniform cell coverage and clustering), two analysis methods motivated by similarity (cell-based analysis and cluster-classification), and two descriptor sets (BCUT and constitutional). Thus, our study addresses some of the many questions arising in a sequential screen how to choose the initial screen, how to analyze the structure-activity data, and what molecular descriptor set to use. The study is limited to one assay and thus cannot be definitive, but it at least provides preliminary insights and reveals some trends. 

We use assay data from a National Cancer Institute HIV/AIDS database in our study (http //dtp,nci,nih.gov/docs/aids/aids data.html). As descriptors, we apply a set of six BCUT descriptors and a set of 46 constitutional descriptors computed by the Dragon software. These descriptors could be computed for 29,374 of the compounds in the database. The assay classifies each compound as confirmed inactive (Cl), moderately active (CM), or confirmed active (CA). We treat the data as a binary classification problem with two classes inactive (Cl) and active (CM or CA). According to this classification, 542 (about 1.8%) of the compounds are active. 

Design method Descriptor set used for design BCUT Constitutional - 6 PCs Constitutional - 20 PCs... 

The UCC and clustering methods require a descriptor set—BCUT or constitutional descriptors. As our implementation of UCC requires continuous descriptors, the 46 constitutional descriptors, which include discrete counts, were also reduced to either the first 6 or the first 20 principal components (PCs). Thus, the UCC algorithm was applied to the BCUT descriptors and either 6 or 20 PCs from the constitutional descriptors. In addition to these three sets, clustering was also applied to the 46 raw constitutional descriptors. The random design requires no descriptors. 

Thus, there are eight design-method/descriptor-set combinations to compare, as shown in the first two columns of Table 1. We use UCC to measure diversity, as it provides a comprehensive assessment of coverage in all low-dimensional subsets of variables. Recall that a small value of UCC is better. Furthermore, no matter how the design is generated, UCC can be measured according to the BCUT or constitutional descriptors (6 or 20 PCs). The results are very similar for the two replicates, hence only the first replicate is reported. 

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