Semantic Similarity for Automatic Classification of Chemical Compounds
Semantic Similarity for Automatic Classification of Chemical Compounds
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DOI：
10.1371/journal.pcbi.1000937
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发表时间：
2010-09-01
影响因子：
4.3
通讯作者：
Couto, Francisco M.
中科院分区：
文献类型：
--
作者：
Ferreira, Joao D.;Couto, Francisco M.
随着化学领域中提供的数据量的增加，人们非常需要以有效有效的方式比较和分类化学化合物的系统。当今现有的最佳方法基于结构活性关系前提，该前提指出，分子的生物学活性与其结构或物理化学特性密切相关。这项工作通过将语义相似性与现有的结构比较方法整合在一起，为化合物自动分类提供了一种新颖的方法。根据预测的Matthews相关系数评估了我们的方法，当用作血脑屏障渗透性的预测为0.810时，P-甘草蛋白底物的值为0.810，雌激素受体结合活性为0.694，为0.673。这些结果比当前现有的方法的最佳性能分别为0.628、0.591和0.647。证明语义相似性的整合是改善现有化学复合分类系统的可行和有效方法。除其他可能的用途外，该工具有助于研究代谢途径的演变，研究代谢网络与这些网络特性的相关性的研究或代表化学信息的本体论的改善。
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.