Semantic similarity for automatic classification of chemical compounds.

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 substrat

SUBMITTER: Ferreira JD 

PROVIDER: S-EPMC2944781 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature