Conceptual DFT, machine learning and molecular docking as tools for predicting LD toxicity of organothiophosphates.

Context: Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R values for the training set (R) and R values for the test set (R), around 0.90.

A DFT Study of the Reaction of Acrylamide with L-Cysteine and L-Glutathione.

Thermal processing of certain foods implies the formation of acrylamide, which has been proven to provoke adverse effects on human health. Thus, several strategies to mitigate it have been developed. One of them could be the application of organosulfur compounds obtained from natural sources to react with the acrylamide, forming non-toxic adducts.

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.

New QSPR model for prediction of corrosion inhibition using conceptual density functional theory.

The relationship between structure and corrosion inhibition of a series of twenty-eight quinoline and pyridine derivatives has been established through the investigation of quantum descriptors calculated with PBE/6-311 +  + G** method. A quantitative structure-property relationship (QSPR) model was obtained by examining these descriptors using a genetic algorithm approximation method based on a multiple linear regression analysis. The results indicate that the efficiency of corrosion inhibitors is strongly associated with hardness (η), minimal electrostatic potential (ESP), and volume (V) descriptors.

The chemical reactivity and QSPR of organic compounds applied to dye-sensitized solar cells using DFT.

The structural and electronic properties were calculated for seventy organic compounds used as dye sensitizers in solar cells, applying the B3LYP exchange-correlation energy functional with the 6-311G∗∗ basis set. Moreover, the present study proposes two new quantitative structure-property relationship (QSPR) models that enable the prediction of the power conversion efficiency (PCE) and maximum absorption wavelength (λ) of these systems, the two QSPR models were validated using the coefficient of determination (R) of 0.62 for both models with the leave-one-out cross-validation correlation coefficient (Q) of 0.