Semantic similarity for automatic classification of chemical compounds.

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 st...

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Autores principales:	João D Ferreira, Francisco M Couto
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/96bb4f1feea84c7abc362d62a8be458e
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spelling	oai:doaj.org-article:96bb4f1feea84c7abc362d62a8be458e2021-11-18T05:49:17ZSemantic similarity for automatic classification of chemical compounds.1553-734X1553-735810.1371/journal.pcbi.1000937https://doaj.org/article/96bb4f1feea84c7abc362d62a8be458e2010-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20885779/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358With 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.João D FerreiraFrancisco M CoutoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 9 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 João D Ferreira Francisco M Couto Semantic similarity for automatic classification of chemical compounds.
description	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.
format	article
author	João D Ferreira Francisco M Couto
author_facet	João D Ferreira Francisco M Couto
author_sort	João D Ferreira
title	Semantic similarity for automatic classification of chemical compounds.
title_short	Semantic similarity for automatic classification of chemical compounds.
title_full	Semantic similarity for automatic classification of chemical compounds.
title_fullStr	Semantic similarity for automatic classification of chemical compounds.
title_full_unstemmed	Semantic similarity for automatic classification of chemical compounds.
title_sort	semantic similarity for automatic classification of chemical compounds.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/96bb4f1feea84c7abc362d62a8be458e
work_keys_str_mv	AT joaodferreira semanticsimilarityforautomaticclassificationofchemicalcompounds AT franciscomcouto semanticsimilarityforautomaticclassificationofchemicalcompounds
_version_	1718424801208434688

Semantic similarity for automatic classification of chemical compounds.

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