QSAR Modeling for Predicting AquaticToxicity of Chemicals

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3.1.2 Regression QSAR modeling
QSAR models were developed to predict the toxicity (-log EC50 mmol/L) of chemicals using the EL based modeling methods (DTB, DTF). Distribution of the selected descriptors for regression modeling is shown in the radar chart (Fig. 1). A 10-fold CV was adopted to determine the optimal architecture and the model parameters, using the criterion of minimum MSE in training and validation set. The average values (10 runs) of MSEs and in internal validation and training data for the proposed QSAR models were 0.56, 0.11 and 0.709 0.941 (DTB) and 0.70, 0.14, and 0.661, 0.940 (DTF). These values are comparable to the results obtained when establishing the models in the training (DTB 0.11, 0.946; DTF 0.16, 0.940) and test (DTB 0.31, 0.793; DTF 0.41, 0.753) phase. A model is considered acceptable90 when the value of exceeds 0.5. The results indicate that both the models herein investigated are robust. Further, the results of the external validation of these models (Table 3) suggest that in all the cases, values were above its threshold (0.6).65 Consonni et al.66 demonstrated that results obtained by are independent of the prediction set distribution and sample size, hence independent of the samples chemical space. Moreover, according to the criteria proposed by Eriksson et al.91 the difference between R2 (training) and R2 (validation) should not exceed 0.3. As our models fulfill these criteria and also positively pass internal and external validation, these were applied to predict the toxicity of new, untested chemicals.
Table 3
The regression QSAR models were based on topological (chi simple path descriptor of order 1, SP-1; molecular distance edge between all secondary and tertiary carbons, MDEC-23...

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