Use of structure descriptors to discriminate between modes of toxic action of phenols

Two classification models were developed based on a data set of 220 phenols with four associated Modes of Toxic Action (MOA). Counter-propagation neural networks (CPG NN) and multinomial logistic regression (multinom) were used as classification methods. The combination of topological autocorrelation of empirical pi-charge and sigma-electronegativity and of surface autocorrelation of hydrogen-bonding potential resulted in a 21-dimensional model that successfully discriminated between the four MOAs. Its overall predictive power was estimated to 92% using 5-fold cross-validation. Subsequently, a simple score for the distance to the training data was used to determine the prediction space of the model and used in an exploratory study on the phenols contained in the open NCI database. The use of a prediction space metric proved indispensable for the screening of such a diverse database. The prediction space covered by the proposed model is still of rather local nature which is either caused by the limited diversity and size of the training set or by the high dimensionality of the descriptors.