Pharmacophore fingerprints

Any type of selected descriptor will provide a more or less complex characterization of each virtual library component. The use of similarity indices offers a straightforward method to evaluate similarities between virtual compounds. These indices use a bit-string representation for any descriptor (distances, fingerprints, pharmacophores, and so on) and, by simply counting the presence or the absence of specific bins and comparing the bit strings of virtual compounds, provide a numerical similarity index. The formula for the commonly used Tanimoto similarity index (71, 43), which can readily be transformed into the complementary diversity index, is reported in Fig. 5.14... 

Beno BR, Mason JS. The design of combinatorial libraries using properties and 3D pharmacophore fingerprints. Drug Discov Today 2001 6(5) 251-8. 

Xue L, Stahura FL, Godden JW, Bajorath J. Mini-fingerprints detect similar activity of receptor ligands previously recognized only by three-dimensional pharmacophore-based methods. J Chem Inf Comp Sci 2001 41 394-401. 

Hence there are multiple solutions for the final set of 10000 compounds. The final selection can be diversity driven using for example cluster analysis based on multiple fingerprints [63], hole filling strategies by using scaffold/ring analysis (LeadScope [66], SARVision [66]) or pharmacophore analysis [67, 68]. For a review of computational approaches to diversity and similarity-based selections, see the paper of Mason and Hermsmeier [69] and the references therein. 

McGregor, M. J., Muskal, S. M. Pharmacophore fingerprinting. 2. Application to primary library design. 

Another family of fingerprints available are the MOE pharmacophore fingerprints accessible through software from the Chemical Computing group [51]. In this system, the atoms are generalized into a smaller vocabulary of pharmacophore features, after which the fingerprint is constructed based on connected paths. 

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. 

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 Cheney, D.L. Library design and virtual screening using multiple 4-point pharmacophore fingerprints. Pac. Symp. Biocomput. 1999, 4, 456-467. 

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. 

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