Fingerprints molecular similarity methods

Since LEAP1 was built based on Pipeline Pilot technology, multiple molecular fingerprints and similarity methods can be applied at disposal, which currently include MDL Public Keys and different levels of FCFPs and ECFPs (18). 

A special case of cell-based methods is a diversity measure proposed for binary fingerprints. Unlike continuous descriptors, binary descriptors such as structural keys and hashed fingerprints can be compared using fast binary operations to give rapid estimates of molecular similarity, diversity, and complementarity. The most common example of a diversity measure applied to binary descriptors is the binary union (inclusive or ). This can be exploited in a number of different ways elegant examples can be found in the following references. ... 

An alternative to fingerprint based similarities are those based on BCUTs (Burden, CAS, University of Texas). This method uses a modified connectivity matrix (the Burden matrix) onto which are mapped atomic descriptors (such as atomic mass and polarizability) and connectivity information. The eigenvectors of this matrix represent a compressed summary of the information in the matrix and are used to describe a molecule. Typically 5-6 BCUT descriptors suffice to describe the chemical space of a set of molecules, and the space is usually partitioned into distinct bins , with each molecule assigned to the appropriate partition. In this format, similarity calculations become very simple molecules which are mapped into the same partition are similar. As an alternative, one could use larger numbers of molecular properties and a correlation vector approach. 

The fact that mass, infra-red, NMR and ultra-violet spectra and, to a certain extent, also chromatographic retention data can be considered as molecular fingerprints , forms the basis of most computerised library search systems. Retrieval methods for characteristic chemical data and techniques for the comparison of human fingerprints have similar elements the first step is to clean up the raw data, then in many cases a data reduction is carried out by selection of prominent features. Finally, there is the comparison of unknown and reference data patterns, which, for a useful result, requires a statistical correlation to be established. In this paper no attention will be paid to feature selection. 

How does this example apply to the use of multiple similarity methods Each of the similarity methods can be considered to be equivalent to an independent judge, since none of the values produced by the other methods have an explicit impact on the value produced by a given method. This may not always be the case, for example, if two methods use MACCS key fingerprints, but one uses the Tanimoto (Jacard) and the other a closely related similarity function (see Table 15.3). As shown by Gower [76], some molecular similarity functions are monotonically related. Thus, comparisons of these functions based on the same molecular representation will produced linear correlations of the values computed by the two functionally similarity functions. Hence, only one of the functions should be used. 

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