Quantitative Structure-activity Relationship of Toxicity of Alkyl(1-phenylsulfonyl) Cycloalkane-carboxylates Using MLSER Model and Ab initio

QSPR Models for the Prediction of Some Thermodynamic Properties of Cycloalkanes Using GA-MLR Method

AIM AND OBJECTIVE

Cycloalkanes have been largely used in the field of medicine, components of food, pharmaceutical drugs, and they are mainly used to produce fuel. In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°_f), the standard enthalpies of fusion (∆H°_fus), and the standard Gibbs free energy of formation (∆G°_f)of the cycloalkanes is represented.

MATERIALS AND METHODS

The Genetic Algorithm (GA) and multiple linear regressions (MLR) were successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular descriptors were obtained with the Dragon program. The Genetic algorithm and backward method were used to reduce and select suitable descriptors.

RESULTS

QSPR models were used to delineate the important descriptors responsible for the properties of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF), Pearson Correlation Coefficient (PCC) and the Durbin-Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The statistical parameters of the training, and test sets for GA-MLR models were calculated.

CONCLUSION

The results of the present study indicate that the predictive ability of the models was satisfactory and molecular descriptors such as: the Functional group counts, Topological indices, GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising route for developing highly correlated QSPR models for prediction the studied properties.

QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method

Aims and Objectives:

QSPR models establish relationships between different types of structural information to their observed properties. In the present study the relationship between the molecular de-scriptors and quantum properties of cycloalkanes is represented.

Materials and Methods:

Genetic Algorithm (GA) and Multiple Linear Regressions (MLR) were successful-ly developed to predict quantum properties of cycloalkanes. A large number of molecular descriptors were calculated with Dragon software and a subset of calculated descriptors was selected with a genetic algorithm as a feature selection technique. The quantum properties consist of the heat capacity (Cv)/ Jmol-1K-1 entropy(S)/ Jmol-1K-1 and thermal energy(Eth)/ kJmol-1 were obtained from quantum-chemistry technique at the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets.

Results:

The Genetic Algorithm (GA) method was used to select important molecular descriptors and then they were used as inputs for SPSS software package. The predictive powers of the MLR models were dis-cussed using Leave-One-Out (LOO) cross-validation, leave-group (5-fold)-out (LGO) and external predic-tion series. The statistical parameters of the training and test sets for GA–MLR models were calculated.

Conclusion:

The resulting quantitative GA-MLR models of Cv, S, and Eth were obtained:[r2=0.950, Q2=0.989, r2ext=0.969, MAE(overall,5-flod)=0.6825 Jmol-1K-1], [r2=0.980, Q2=0.947, r2ext=0.943, MAE(overall,5-flod)=0.5891Jmol-1K-1], and [r2=0.980, Q2=0.809, r2ext=0.985, MAE(overall,5-flod)=2.0284 kJmol-1]. The results showed that the predictive ability of the models was satisfactory, and the constitutional, topological indices and ring descriptor could be used to predict the mentioned properties of 103 cycloalkanes.

QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS ON DISSOLVABILITY OF PCDD/Fs USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

The environmental fate of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) has become a major issue in recent decades. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, QSPR models were established for estimating water solubility (- log S W ) and n-octanol/water partition coefficient ( log KOW) of PCDD/Fs. Quantum chemical descriptors computed with density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for - log S W and log K OW of PCDD/Fs. Optimized models with high correlation coefficients (R2 > 0.983) were obtained for estimating - log S W and log K OW of PCDD/Fs. Both the internal cross validation test [Formula: see text] and external validation test (R2 > 0.965) results showed that the obtained models had high-precision and good prediction capability. The - log S W } and log K OW values predicted by the obtained models are very close to those observed. The PLS analysis indicated that PCDD/Fs with larger electronic spatial extent (R e ), lower molecular total energy (E T ), and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals (E LUMO -E HOMO ) tend to be less soluble in water but more lipophilic.

ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.

ESTIMATION OF DISSOLVABILITY OF CHLORIC AND ALKYL BENZENE DERIVATIVES USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports two optimal QSPR models for estimating water solubility ( log S W ) and n-octanol/water partition coefficient ( log K OW ) of chloric and alkyl benzene derivatives. Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log S W and log K OW of chloric and alkyl benzene derivatives. The correlation coefficients of the optimal models for log S W and log K OW were 0.973 and 0.990, respectively. The results of internal cross-validation test and external validation test showed that both of the optimal models had high fitting precision and good predicting ability. The log S W and log K OW values predicted by the optimal models are very close to those observed. The PLS analysis indicated that chloric and alkyl benzene derivatives with larger electronic spatial extent and lower molecular total energy tend to be more hydrophobic and lipophilic, and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals leads to larger dissolvability.

Quantitative structure-activity relationship analysis of acute toxicity of diverse chemicals to Daphnia magna with whole molecule descriptors

Quantitative structure-activity relationship analysis and estimation of toxicological effects at lower-mid trophic levels provide first aid means to understand the toxicity of chemicals. Daphnia magna serves as a good starting point for such toxicity studies and is also recognized for regulatory use in estimating the risk of chemicals. The ECOTOX database was queried and analysed for available data and a homogenous subset of 253 compounds for the endpoint LC50 48 h was established. A four-parameter quantitative structure-activity relationship was derived (coefficient of determination, r (2) = 0.740) for half of the compounds and internally validated (leave-one-out cross-validated coefficient of determination, [Formula: see text] = 0.714; leave-many-out coefficient of determination, [Formula: see text] = 0.738). External validation was carried out with the remaining half of the compounds (coefficient of determination for external validation, [Formula: see text] = 0.634). Two of the descriptors in the model (log P, average bonding information content) capture the structural characteristics describing penetration through bio-membranes. Another two descriptors (energy of highest occupied molecular orbital, weighted partial negative surface area) capture the electronic structural characteristics describing the interaction between the chemical and its hypothetic target in the cell. The applicability domain was subsequently analysed and discussed.

Mechanism-based quantitative structure-activity relationships on toxicity of selected herbicides to Chlorella vulgaris and Raphidocelis subcapitata

Four quantitative structure-activity relationships were developed for toxicity of selected photosynthesis (PHS) inhibitors and acetolactate synthase (ALS) inhibitors to Chlorella Vulgaris and Raphidocelis subcapitata using a mechanism-based approach. These models have good fitness and predictive ability. The potential of electron transfer, intermolecular interactions with weak electron-transfer, and intermolecular dispersive interactions between PHS inhibitors and the active site of action are key factors influencing the toxicity of these PHS inhibitors. Intermolecular weak electron-transfer interactions and intermolecular dispersive interactions mainly determine the toxicity of these ALS inhibitors. Sulfonyl is an important functional group governing the toxicity of ALS inhibitors investigated.

Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

Quantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

Development of a general quantum-chemical descriptor for steric effects: density functional theory based QSAR study of herbicidal sulfonylurea analogues

Quantitative structure-activity relationship (QSAR) analysis has become one of the most effective approaches for optimizing lead compounds and designing new drugs. Although large number of quantum-chemical descriptors were defined and applied successfully, it is still a big challenge to develop a general quantum-chemical descriptor describing the bulk effects more directly and effectively. In this article, we defined a general quantum-chemical descriptor by characterizing the volume of electron cloud for specific substituent using the method of density functional theory. The application of our defined steric descriptors in the QSAR analysis of sulfonylurea analogues resulted in four QSAR models with high quality (the best model: q2 = 0.881, r2 = 0.901, n = 35, s = 0.401, F = 68.44), which indicated that this descriptor may provide an effective way for solving the problem how to directly describe steric effect in quantum chemistry-based QSAR studies.

Modeling and prediction of photolysis half-lives of polycyclic aromatic hydrocarbons in aerosols by quantum chemical descriptors

Quantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental fate parameters of organic pollutants with their structure descriptors. This study reports QSPR models for photolysis half-lives of polycyclic aromatic hydrocarbons (PAHs) in aerosols. Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models. The correlation coefficient of the optimal model was 0.993, and the fitting results showed this optimal model had high fitting precision and good predictability. The predicted photolysis half-lives by the optimal model are very close to those observed. The PLS assistant analysis indicated that PAHs with large electronic spatial extent tend to be photolyzed faster, while PAHs with high molecular total energy and small Mulliken atomic charges on the most negative carbon atom tend to be photolyzed slower in aerosols.