2D fragments

Figure 8.3 Example of a 2D similarity search, showing a query molecule and five of its nearest neighbors. The similarity measure for the search is based on 2D fragment bit-strings and the Tanimoto coefficient.

Holliday, J. D., Hu, C.-Y., and Willett, P. (2002) Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Combi. Chem. High Through. Screen. 5, 155-166. 

Medicinal chemists have numerous fast in sUico tools to evaluate the log Poet of NCEs prior to synthesis. These different methods can be divided in two main classes according to the level of description of molecular structure, namely 2D fragmental methods which cut the molecule in typical atomic or multiatomic fragments possessing their own lipophilicity coefficients and 3D global methods which code explicitly the principal intermolecular interactions potential of a 3D molecule. This section presents only an outline of the principal in silica methods since this subject was recently reviewed in detail [33, 34]. 

Chen, X., Reynolds, . H. (2002) Performance of similarity measures in 2D fragment-based similarity searching comparison of... 

Many different structural descriptors have been developed for similarity searching in chemical databases [4] including 2D fragment based descriptors, 3D descriptors, and descriptors that are based on the physical properties of molecules. More recently, attention has focused on diversity studies and many of the descriptors applied in similarity searching are now being applied in diversity studies. Structural descriptors are basically numerical representations of structures that allow pairwise (dis)similarities between structures to be measured through the use of similarity coefficients. Many diversity metrics have been devised that are based on calculating structural (dis)similarities, some of these are described below. 

One of the most commonly used structural descriptors in similarity and diversity studies is that of the 2D fragment bitstring where a molecule is represented by a vector of binary values that indicate the presence or absence of structural features, or fragments, within the molecule. Many different similarity measures or coefficients have been developed to quantify the degree of similarity between such vector based descriptors [5-7]. Usually, the values that can be taken by a coefficient lie in the range 0..1, or they can be normalised to be within this range. A similarity coefficient of 1 indicates that the two molecules are identical with respect to the structural descriptors and a value of 0 indicates that the two molecules are maximally different... 

Z. Zhang, J.S. McElvam, De novo peptide sequencing by 2D fragment correlation MS, Anal. Chem., 72 (2000) 2337. 

Keseru [35] used literature data on 55 compounds to train a QSAR model based on a number of calculated descriptors. Five descriptors were used clogP, calculated molar refractivity (CMR), partial negative surface area, and the VolSurf W2 (polarizability) and D3 (hydrophobicity) descriptors. A model of acceptable quality was obtained (f = 0.94, SSE = 0.82) and tested on a 13 compound holdout set (r2 = 0.56, SSE = 0.98). An HQSAR model was then created that made use of 2D fragment fingerprints (threshold hERG IC50 = lpM). The best HQSAR model was validated on a holdout set of 13 compounds (f = 0.81, SSE = 0.67). 

MACCS substructure keys on the other hand encode the presence of a predefined set of relevant 2D fragments, originally designed for speeding up database substructure searching [48,49] by eliminating those compounds from detailed consideration that can-... 

The 3D and 2D fragments can be cut according to various orientations from a perovskite stmcture (Figure 17-4) layers perpendicular to directions [001] (most common), [110] and [111] are known [10]. 

HQSAR model created that made 173 use of 2D fragment fingerprints. 

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