ON QUANTITATIVE STRUCTURE–TOXICITY RELATIONSHIPS (QSTR) USING HIGH CHEMICAL DIVERSITY MOLECULES GROUP
Laszlo Tarko, Mihai V. Putz · Journal of Theoretical and Computational Chemistry · 2012
This paper presents result of QSTR (quantitative structure–toxicity relationship) study obtained using the PRECLAV software. The dependent property is toxicity against rat (Rattus norvegicus), measured by TD50 values. The calibration/training/learning set includes 49 molecules having a very high chemical diversity. There are five outliers in calibration set. In the presence of outliers the predictive power of QSTR is very low (r2 = 0.5425, F = 10.4, [Formula: see text]). After elimination of outliers the predictive power of QSTR is much higher (r2 = 0.9078, F = 44.3, [Formula: see text]). All eight predictors are nonlinear functions (parabolic and products) of descriptors. The LogP is not predictor. Presence of C = CH2 and N–NO molecular fragments increases toxicity. Presence of C6H4 molecular fragment decreases toxicity.