Fingerprint molecular similarity

Liu, J., Yang, L., Li, Y, Pan, D. and Hopfinger, A.J. (2006) Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses. Bioorg. Med. Chem., 14, 611-621. 

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). 

For the library design, we have also used the Tanimoto coefficient (11) computed based on the molecular fingerprints from SciTegic Pipeline Pilot (14) as the measure of molecular similarity. 

Our approach to selecting a diverse subset is based on utilizing a minimum similarity between each molecule and all other molecules in the virtual library. For the 2-D fingerprints, the similarity is measured by a Tanimoto coefficient20 which measures similarity on a pair-wise basis. A Tanimoto coefficient for any pair of molecular structures lies in the range of zero (dissimilar) to one (similar). It is defined as the ratio of the number of common bits (in this case molecular fragments) set in two molecules divided by the number of bits set in either. 

If descriptor combinations are expressed as bit strings (often called fingerprints, as described in more detail later on), each test molecule is assigned a characteristic bit pattern, and pair-wise molecular similarity can be assessed by quantifying the overlap of bit strings using various similarity metrics (coefficients). Examples are shown in Table 1.4. 

Briem H, Kuntz ID, Molecular similarity based on dock-generated fingerprints, J. Med. Chem., 39 3401-3408, 1996. 

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. ... 

Analysis of molecular similarity is based on the quantitative determination of the overlap between fingerprints of the query structure and all database members. As descriptors of a given molecule can be considered as a vector of real or binary attributes, most of the similarity measures are derived as vectorial distances. Tanimoto and Cosine coefficients are the most popular measures of similarity.Definitions of similarity metrics are collected in Table 3. 

Briem, H. and Kuntz, I.D. (1996). Molecular Similarity Based on DOCK-Generated Fingerprints. J.Med.Chem., 39,3401-3408. 

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. 

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