QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure-toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R-2) and a leave-one-out coefficient (Q(LOO)(2)) of 0.6584 and 0.6289, respectively.

Automated summary

In this study, a QSTR model was developed to predict the toxicity of carbamate derivatives using a large dataset. The model was rigorously validated and successfully predicted the toxicity by combining structure-based evaluation and physicochemical, structural, and quantum molecular descriptors.