Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols

<p>The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to<br>marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly<br>correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (LogP). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure–activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q2test=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided<br>an excellent external predictivity (Q2test=0.93).<br>The proposed algal QSARs were further tested in their predictivity using an external set comprising<br>toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (Cortho) not only had high external predictivity (Q2ext=0.74), but it also had excellent external data set coverage (%97).</p

QSTR modelling of the acute toxicity of pharmaceuticals to fish

<p>Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R(2 )= 0.664, F = 13.588, and R(cv)(2) (LOO) = 0.542 while it achieves R(2 )= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The R(pred)(2) is 0.777, reflecting a relatively good predictive power for the external set.</p