Document Detail


Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real datasets.
MedLine Citation:
PMID:  23078167     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper reports an analysis and comparison of the use of 51 different similarity coefficients for computing the similarities between binary fingerprints for both simulated and real chemical datasets. Five pairs and a triplet of coefficients were found to yield identical similarity values, leading to the elimination of seven of the coefficients. The remaining 44 coefficients were then compared in two ways: by their theoretical characteristics using simple descriptive statistics, correlation analysis, multi-dimensional scaling, Hasse diagrams, and the recently described atemporal target diffusion model; and by their effectiveness for similarity-based virtual screening using MDDR, WOMBAT and MUV data. The comparisons demonstrate the general utility of the well-known Tanimoto method, but also suggest other coefficients that may be worthy of further attention.
Authors:
Roberto Todeschini; Viviana Consonni; Hua Xiang; John Holliday; Massimo Buscema; Peter Willett
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-19
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  -     ISSN:  1549-960X     ISO Abbreviation:  J Chem Inf Model     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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