Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 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 (LD50) 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 R2 values for the training set (R2Train) and R2 values for the test set (R2Test), around 0.90. METHODS: The molecular structure of all organothiophosphates was optimized via the range-separated hybrid functional ωB97XD with the 6-311 + + G** basis set. Seven hundred and eighty-seven descriptors have been processed using a variety of machine learning algorithms: RF LASSO, Ridge, EN and SVM to generate a predictive model. The properties were obtained with Multiwfn, AIMALL and VMD programs. Docking simulations were performed by using AutoDock 4.2 and LigPlot + programs. All the calculations in this work are carried out in Gaussian 16 program package.

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 (R2) and a leave-one-out coefficient (Q2LOO) of 0.6584 and 0.6289, respectively.

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 (ESPmin), and volume (V) descriptors. Furthermore, the validity of the proposed model is corroborated by an adsorption study on an iron surface Fe(110).

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 (λmax) of these systems, the two QSPR models were validated using the coefficient of determination (R2) of 0.62 for both models with the leave-one-out cross-validation correlation coefficient (Q2LOO) of 0.55 and 0.57, respectively. Furthermore, applicability domain analysis was conducted in order to identify the related compounds via the extrapolation of the model.

A new computational model for the prediction of toxicity of phosphonate derivatives using QSPR.

Structural and electronic properties of a series of 25 phosphonate derivatives were analyzed applying density functional theory, with the exchange-correlation functional PBEPBE in combination with the 6-311++G** basis set for all atoms. The chemical reactivity of these derivatives has been interpreted using quantum descriptors such as frontier molecular orbitals (HOMO, LUMO), Hirshfeld charges, molecular electrostatic potential, and the dual descriptor [[Formula: see text]]. These descriptors are directly related to experimental median lethal dose ([Formula: see text], expressed as its decimal logarithm [[Formula: see text]([Formula: see text]] through a multiple linear regression equation. The proposed model predicts the toxicity of phosphonates in function of the volume (V), the load of the most electronegative atom of the molecule (q), and the eigenvalue of the molecular orbital HOMO ([Formula: see text]. The obtained values in the internal validation of the model are: [Formula: see text]%, [Formula: see text]%, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]%. The toxicity of nine phosphonate derivatives used as test molecules was adequately predicted by the model. The theoretical results indicate that the oxygen atom of the O=P group plays an important role in the interaction mechanism between the phosphonate and the acetylcholinesterase enzyme, inhibiting the removal of the proton of the ser-200 residue by the his-440 residue.

Density Functional Theory and Electrochemical Studies: Structure-Efficiency Relationship on Corrosion Inhibition.

The relationship between structure and corrosion inhibition of a series of 30 imidazol, benzimidazol, and pyridine derivatives has been established through the investigation of quantum descriptors calculated with PBE/6-311++G**. A quantitative structure-property relationship model was obtained by examination of these descriptors using a genetic functional approximation method based on a multiple linear regression analysis. Our results indicate that the efficiency of corrosion inhibitors is strongly associated with aromaticity, electron donor ability, and molecular volume descriptors. In order to calibrate and validate the proposed model, we performed electrochemical impedance spectroscopy (EIS) studies on imidazole, 2-methylimidazole, benzimidazole, 2-chloromethylbenzimidazole, pyridine, and 2-aminopyridine compounds. The experimental values for efficiency of corrosion inhibition are in good agreement with the estimated values obtained by our model, thus confirming that our approach represents a promising and suitable tool to predict the inhibition of corrosion attributes of nitrogen containing heterocyclic compounds. The adsorption behavior of imidazole or benzimidazole heterocyclic molecules on the Fe(110) surface was also studied to elucidate the inhibition mechanism; the aromaticity played an important role in the adsorbate-surface complex.