Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?

Abstract

SObjectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water.Methods: A subset of calculated descriptors was selected from Dragon ® software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results:The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory.Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

2D QSAR Approach to Develop Newer Analogs as Melatonin Receptor Agonist

“…In case of Mahalanobis distance of MT1 receptor agonist, compound CBOBNEA with highest Mahalanobis distance of 3.12079 and melatonin with lowest Mahalanobis distance of 1.07321 among the training set compounds and compound SD6 with highest distance score of 2.04131 and ramelteon with 2.04121 lowest distance score among the test compounds as well as in case of MT2 receptor agonist compound S26284 and melatonin with highest and lowest mahalanobis distance respectively present inside the Training set and compound 5-HEAT and EFPPEA with highest and lowest mahalanobis distance respectively present inside the Among the two QSAR model one parameter as ALogP is common, so we have to emphasize on the partition coefficient value along with molar refractivity, relative polarizability and 2D matrix descriptors. 36,37 The statistical information was SEE: 0. 18717 which also shown that the predictability of the model is quite high and the Y Randomization test result also shown that cRp^2: 0.7665 which must be greater than 0.5 for good predictability.…”

Untitled

“…The data showed a 0.5926 value with 29 molecules in high total active/less and 55 molecules in less total active/toxic with a threshold value of model ability index 0.65. So model ability index value of the model was 0.5926, which reflected that the dataset was quite close to developing a good QSAR model [38][39] . Then the dataset was divided into training and test sets using the Kennard-Stone method.…”

Exploring Descriptor Combination by Chemometric Approach to Develop Newer Molecules Active through Corticosteroid Binding Globulin Receptor

“…The rm^2 (test) value for external validation is 0.9935 which must be greater than 0.5 as a good external predictability parameter. For a QSAR model can be considered acceptable if the values of r2m (overall) and r2p are equal to or above 0.5 (or at least near 0.5); in this developed QSAR model r2m is 0.99979 and r2p is 0.99929 [11,12]. The outcomes from Golbraikh and Tropsha acceptable model was diagrammatized at Table: 6 which represent that Q^2: 0.99928, r^2: 0.99979, |r0^2-r'0^2|:0.0, k: 0.97936, [(r^2-r0^2)/r^2]:0.00004, k': 1.02084, [(r^2-'0^2)/r^2]: 0.00004 which also shown that the predictability of the model is quite high [13].…”