Fingerprints, binary

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 ... 

For similarity searching, all molecules are described by an appropriate binary descriptor (consisting of only zeros and ones). Such a binary fingerprint contains all structural information for a particular molecule and was applied at Aventis to identify new Kvl.5 inhibitors in the compound collection. 

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. ... 

Molecules are typically represented by a vector of real-valued properties (molecular weight, logP, etc.) or binary values (e.g., 0 for absence, 1 for presence of a substructure feature) in a bit-string or binary fingerprint. The term fingerprint or key or signature thus refers to an encoding of features/characteristics a molecule exhibits (e.g., substructures present, all possible combinations of 2-4 pharmaco-... 

Godden, J.W., Xue, L. and Bajorath, J. (2000). Combinatorial Preferences Affect Molecular Similarity/Diversity Calculations Using Binary Fingerprints and Tanimoto Coefficients. J.Chem. lnf.Comput.Sci., 40,126-134. 

The fingerprint methods can be divided into dictionary-based and hashed-based methods. In the dictionary-based methods, such as the MDL MACCS keys [12] and BCI fingerprints [13], a binary fingerprint is defined in which each bit represents a particular substructural fragment contained in a fragment dictionary. The fingerprint... 

Figure 14.3 An illustrative diagram of how a neural net is trained to distinguish drugs from non-drugs. Descriptors are calculated from the molecules (in this case, presence or absence of a structural fragment to create a binary fingerprint). The descriptors are used as input to the neural net, and the hidden layer learns how to weight the chemical inputs to optimally separate the drugs and non-drugs. Once trained, the network can predict the drug likeness of a novel molecule presented to it.

Godden JW, Xue L, Bajorath J. Combinatorial preferences affect molecular similarity/ diversity calculations using binary fingerprints and Tanimoto coefficients. J Chem Inf Comput Sci 2000 40 163-166. 

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