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    This was brought to you by:

    block this user João Ferreira

    Student, Ph.D. Level

    Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa

    Semantic Similarity for Automatic Classification of Chemical Compounds

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    With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.

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    Description

    New Full text for this article was not available? Send a request to the author(s).
    Title : Semantic Similarity for Automatic Classification of Chemical Compounds
    Author(s) : João D. Ferreira, Francisco M. Couto
    Abstract : With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2010

    Affiliations :  Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa
    Editors : John B O Mitchell
    Journal : PLoS Computational Biology
    Volume : 6
    Issue : 9
    Publisher : Public Library of Science
    Pages : e1000937 -
    Url : http://dx.plos.org/10.1371/journal.pcbi.1000937
    Doi : 10.1371/journal.pcbi.1000937

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