Abstract
Since there are many commercially available compounds which are toxic to humans, animals, and environment, Chemical Substances Control Law (CSCL) is enacted in order to regulate such compounds. However it is almost impossible to examine the toxicities of all the compounds experimentally. Thus, predicting the toxicities using Quantitative Structure-Toxicity Relationships (QSTR) Analysis as an alternative method of experimental toxicity test has been being tried. In this study, we tried to build a regression and a 2-class classification models in order to predict algae growth inhibition toxicities of the compounds, which have been determined experimentally, using the support vector machine (SVM) with common chemical descriptors as well as graph descriptors. We proposed to use the graph descriptors, which characterize partial graph structures, as complemental descriptors of chemical ones for building better QSAR models. As a result, although we could construct an enough 2-class classification model, graph descriptors were not as useful as chemical descriptors. However, it was suggested that graph descriptors have important information which were different from chemical descriptors’ one. Now, we are trying to build an enough regression model.
