Document Detail

Target fishing for chemical compounds using target-ligand activity data and ranking based methods.
MedLine Citation:
PMID:  19764745     Owner:  NLM     Status:  MEDLINE    
In recent years, the development of computational techniques that identify all the likely targets for a given chemical compound, also termed as the problem of Target Fishing, has been an active area of research. Identification of likely targets of a chemical compound in the early stages of drug discovery helps to understand issues such as selectivity, off-target pharmacology, and toxicity. In this paper, we present a set of techniques whose goal is to rank or prioritize targets in the context of a given chemical compound so that most targets against which this compound may show activity appear higher in the ranked list. These methods are based on our extensions to the SVM and ranking perceptron algorithms for this problem. Our extensive experimental study shows that the methods developed in this work outperform previous approaches 2% to 60% under different evaluation criterions.
Nikil Wale; George Karypis
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  49     ISSN:  1549-960X     ISO Abbreviation:  J Chem Inf Model     Publication Date:  2009 Oct 
Date Detail:
Created Date:  2009-10-26     Completed Date:  2009-12-24     Revised Date:  2013-05-31    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2190-201     Citation Subset:  IM    
Department of Computer Science, University of Minnesota, Twin Cities, Minnesota 55455, USA.
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MeSH Terms
Artificial Intelligence
Bayes Theorem
Drug Discovery / methods*
Models, Theoretical
Grant Support
R01 LM008713-01A1/LM/NLM NIH HHS; RLM008713A//PHS HHS
Reg. No./Substance:

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