ArticleMolecules (Basel, Switzerland)2022
QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 9 citations in OpenAlex.
- Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.ChemistryOpen · 2024Article
- Conceptual DFT, machine learning and molecular docking as tools for predicting LDJournal of molecular modeling · 2023Article
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9 authors at 3 institutions in 1 country.
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Abstract
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 (
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