2D fingerprints

Clustering is the process of dividing a collection of objects into groups (or clusters) so that the objects within a cluster are highly similar whereas objects in different clusters are dissimilar [41]. When applied to databases of compounds, clustering methods require the calculation of all the pairwise similarities of the compounds with similarity measures such as those described previously, for example, 2D fingerprints and the Tanimoto coefficient. 

Wild DJ, Blankley J. Comparison of 2D fingerprint types and hierarchy level selection methods for structural grouping using Ward s clustering. J Chem Inf Comput Sci 2000 40 155-62. 

Schuffenhauer A, Gillet VJ, Willett P. Similarity searching in files of 3D chemical structures analysis of the BIOSTER database using 2D fingerprints and molecular field descriptors. J Chem Inf Comput Sci 2000 40 295-307. 

Similarity Comparison of molecules using molecular descriptors and a measure of similarity, for example a 2D fingerprint and the Tanimoto coefficient... 

We have evaluated the various approaches described above by means of simulated virtual screening searches on the MDL Drug Data Report (MDDR) database. After removal of duplicates and molecules that could not be processed using local software, a total of 102 535 molecules were available for searching. These molecules were represented by 988-bit Tripos Unity 2D fingerprints, and searched using the eleven sets of active compounds detailed in Table 1. 

Table 1 MDDR Activity Classes used in this Study. MPS is the mean pair-wise similarity, computed using the Tanimoto coefficient and Unity 2D fingerprints, averaged over all of the molecules in an activity class.

Matter H, Potter T. (1999) Comparing 3D Pharmacophore Triplets and 2D Fingerprints for Selecting Diverse Compound Subset. J. Chem. Inf. Comp. Set. 39 1211-1225. 

When molecules are represented by high-dimensional descriptors such as 2D fingerprints or several hundred topological indices, then the diversity of a library of compounds is usually calculated using a function based on the pairwise (dis)similarities of the molecules. Pairwise similarity can be quantified using a similarity or distance coefficient. The Tanimoto coefficient is most often used with binary fingerprints and is given by the formula below ... 

Because of the numerous choices for molecular descriptors, weighting factors, and similarity coefficients, there are many ways in which the similarities between pairs of molecules can be calculated. The most used molecular descriptors for defining similarity are probably the 2D fingerprints (22). The bit strings of the molecular fingerprints are used to calculate similarity coefficients. Table 2.3 lists several selected similarity coefficients that can be used with various 2D fingerprints (23). The Tanimoto coefficient is the most popular one (22). 

Selected similarity coefficients to be used with 2D fingerprints for molecule pair (A, B)... 

Willett, P. (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11, 1046-1053. 

Decornez et al. used a generalized kinase model and a combination of 2D (fingerprint based similarity) and 3D methods (docking) to develop a kinase family focused library (15). The authors used 2800 kinase inhibitors compounds as a reference for a 2D search of their in-house database of 260 compounds... 

Unity 2D fingerprints [42] are also based on paths and additionally denote the presence of specific functional groups, rings or atoms. Fingerprints have been used in a number of diversity studies, for example [13, 15,39, 43-46]. 

Matter [45] has also validated a range of 2D and 3D structural descriptors for their ability to predict biological activity and for their ability to be able to sample structurally and biologically diverse datasets effectively. The descriptors examined included Unity 2D fingerprints [42], atom-pairs [47],... 

---