Topological fingerprints

Topological fingerprints consider the connection table of a target molecule and ignore the respective atom coordinates. The rapid calculation and efficiency of such fingerprint methods implicate their wide acceptance in industry. An overview of similarity-based 2D fingerprint methods and their performance during VS is provided in ref. 176. 

One of the major strengths of pharmacophore descriptors and searches is that they are well suited for scaffold hopping, which is finding compounds with 

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A Godzik, A Kolinski, J Skolmck. Topology fingerprint approach to the inverse protein folding problem. J Mol Biol 227 227-238, 1992. 

The application of this topological fingerprint to a database of more than 700 000 structural fragments resulted in more than 2200 experimentally observed bioisos-teric R-groups, cores, and linkers from the Bioster database [22]. The descriptor was also optimized to increase the separation between experimentally observed bioisos-teric pairs and pairs selected at random. 

A. Godzik, A. Kolinski, and J. Skolnick, Topology fingerprint approach to the inverse folding problem. J. Mol Biol 111, 221-23% (1992). 

In addition to looking for data trends in physical property space using PCA and PLS, trends in chemical structure space can be delineated by viewing nonlinear maps (NLM) of two-dimensional structure descriptors such as Unity Fingerprints or topological atom pairs using tools such as Benchware DataMiner [42]. Two-dimensional NLM plots provide an overview of chemical structure space and biological activity/molecular properties are mapped in a 3rd and/or 4th dimension to look for trends in the dataset. 

In conclusion, the electrochemical data offer a fingerprint of the chemical and topological structure of these dendrimers. Furthermore, the knowledge of the electrochemical properties of the mononuclear components and the synthetic control of the supramolecular structure allow the design of dendrimers with predetermined redox patterns. 

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. 

The second step comprises similarity searches from known ligands using 2-D descriptors like fingerprints, topological descriptors like atom-pair fingerprints. 

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 ... 

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