Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR

Reliable experimental method for determination of photoacidity revealed by quantum chemical calculations

Photoacids are aromatic acids that exhibit significantly different acidities when they are electronically excited. Three experimental methods have been extensively used to determine the photoacidity, : fluorescence titration, the Förster cycle, and time-resolved experiments. However, the photoacidities determined by these experimental methods are not consistent. In this work, we used a theoretical method to evaluate the reliability of experimentally determined values. In particular, density functional theory (DFT) and time-dependent DFT calculations were used to obtain the changes in Gibbs free energy for acid dissociation reactions which are directly related to values. The Förster cycle, which is frequently used to experimentally determine the photoacidity due to its simplicity, yielded inconsistent results depending on how the transition energy was defined. We evaluated six empirical parameters extracted from the absorption and emission spectra of acidic and basic species of photoacids to adequately define the transition energy in the Förster cycle. And we found that the values obtained using the optical bandgap as the transition energy in the Förster cycle were in the best agreement with the results of quantum chemical calculations.

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Effect of ultrasonic waves on the water turbidity during the oxidation of phenol. Formation of (hydro)peroxo complexes

Analysis of the kinetics of aqueous phenol oxidation by a sono-Fenton process reveals that the via involving ortho-substituted intermediates prevails: catechol (25.0%), hydroquinone (7.7%) and resorcinol (0.6%). During the oxidation, water rapidly acquires color that reaches its maximum intensity at the maximum concentration of p-benzoquinone. Turbidity formation occurs at a slower rate. Oxidant dosage determines the nature of the intermediates, being trihydroxylated benzenes (pyrogallol, hydroxyhydroquinone) and muconic acid the main precursors causing turbidity. It is found that the concentration of iron species and ultrasonic waves affects the intensity of the turbidity. The pathway of (hydro)peroxo-iron(II) complexes formation is proposed. Operating with 20.0-27.8mgFe²⁺/kW rates leads to formation of (hydro)peroxo-iron(II) complexes, which induce high turbidity levels. These species would dissociate into ZZ-muconic acid and ferrous ions. Applying relationships around 13.9mgFe²⁺/kW, the formation of (hydro)peroxo-iron(III) complexes would occur, which could react with carboxylic acids (2,5-dioxo-3-hexenedioic acid). That reaction induces turbidity slower. This is due to the organic substrate reacting with two molecules of the (hydro)peroxo complex. Therefore, it is necessary to accelerate the iron regeneration, intensifying the ultrasonic irradiation. Afterwards, this complex would dissociate into maleic acid and ferric ions.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Phenylboronic acid-based (19)F MRI probe for the detection and imaging of hydrogen peroxide utilizing its large chemical-shift change

Herein, we report on a new (19)F MRI probe for the detection and imaging of H2O2. Our designed 2-fluorophenylboronic acid-based (19)F probe promptly reacted with H2O2 to produce 2-fluorophenol via boronic acid oxidation. The accompanying (19)F chemical-shift change reached 31 ppm under our experimental conditions. Such a large chemical-shift change allowed for the imaging of H2O2 by (19)F chemical-shift-selective MRI.

How Does the Methodology of 3D Structure Preparation Influence the Quality of pKa Prediction?

The acid dissociation constant is an important molecular property, and it can be successfully predicted by Quantitative Structure-Property Relationship (QSPR) models, even for in silico designed molecules. We analyzed how the methodology of in silico 3D structure preparation influences the quality of QSPR models. Specifically, we evaluated and compared QSPR models based on six different 3D structure sources (DTP NCI, Pubchem, Balloon, Frog2, OpenBabel, and RDKit) combined with four different types of optimization. These analyses were performed for three classes of molecules (phenols, carboxylic acids, anilines), and the QSPR model descriptors were quantum mechanical (QM) and empirical partial atomic charges. Specifically, we developed 516 QSPR models and afterward systematically analyzed the influence of the 3D structure source and other factors on their quality. Our results confirmed that QSPR models based on partial atomic charges are able to predict pKa with high accuracy. We also confirmed that ab initio and semiempirical QM charges provide very accurate QSPR models and using empirical charges based on electronegativity equalization is also acceptable, as well as advantageous, because their calculation is very fast. On the other hand, Gasteiger-Marsili empirical charges are not applicable for pKa prediction. We later found that QSPR models for some classes of molecules (carboxylic acids) are less accurate. In this context, we compared the influence of different 3D structure sources. We found that an appropriate selection of 3D structure source and optimization method is essential for the successful QSPR modeling of pKa. Specifically, the 3D structures from the DTP NCI and Pubchem databases performed the best, as they provided very accurate QSPR models for all the tested molecular classes and charge calculation approaches, and they do not require optimization. Also, Frog2 performed very well. Other 3D structure sources can also be used but are not so robust, and an unfortunate combination of molecular class and charge calculation approach can produce weak QSPR models. Additionally, these 3D structures generally need optimization in order to produce good quality QSPR models.

Influence of ligand architecture on oxidation reactions by high-valent nonheme manganese oxo complexes using water as a source of oxygen

Mononuclear nonheme Mn(IV)=O complexes with two isomers of a bispidine ligand have been synthesized and characterized by various spectroscopies and density functional theory (DFT). The Mn(IV)=O complexes show reactivity in oxidation reactions (hydrogen-atom abstraction and sulfoxidation). Interestingly, one of the isomers (L(1) ) is significantly more reactive than the other (L(2) ), while in the corresponding Fe(IV)=O based oxidation reactions the L(2) -based system was previously found to be more reactive than the L(1) -based catalyst. This inversion of reactivities is discussed on the basis of DFT and molecular mechanics (MM) model calculations, which indicate that the order of reactivities are primarily due to a switch of reaction channels (σ versus π) and concomitant steric effects.

Predicting p Ka values from EEM atomic charges

The acid dissociation constant p K_ais a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p K_aprediction. We have evaluated the p K_aprediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p K_a(63% of these models had R² > 0.9, while the best had R² = 0.924). As expected, QM QSPR models provided more accurate p K_apredictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a fast and accurate p K_aprediction approach that can be used in virtual screening.

Quantitative structure-activity relationship for prediction of the toxicity of phenols on Photobacterium phosphoreum

Quantitative structure-activity relationships (QSAR) is an alternative to experimental toxicity testing and recommended by environmental protection agencies. In this background, an accurate and reliable QSAR model of 18 phenols for their toxicity to Photobacterium phosphoreum was developed using mechanistically interpretable molecular structural descriptors. The QSAR model was developed by stepwise multiple linear regression and the reliability of the model was evaluated by internal and external validation. The cross-validated correlation coefficient (q (2)) was 0.7021, indicating good predictive ability for the toxicity of these phenols. The QSAR model suggests that the toxicity of the studied compounds mainly depends on the logarithm of octanol/water partition coefficient, dipole moment and the most negative atomic charge.

Evaluation of combined toxicity of phenols and lead to Photobacterium phosphoreum and quantitative structure-activity relationships

The combined toxicity of lead (Pb) and nine phenols were measured. The result indicated that the combined toxicity is not only dependent on the Pb concentrations but also on the positions of substituted groups of phenols. Quantitative structure-activity relationship equations were built from the combined toxicity and physico-chemical descriptors of phenols in the different Pb concentrations. The combined toxicity was related to water solubility and the third order molecular connectivity index ((3)X) in low Pb concentration, to solute excess molar refractivity (E) and ionization constant (pKa) in medium Pb concentration and to dipolarity/polarizability (S) in high Pb concentration.

Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis

ABSTRACT

Genetic algorithm (multiparameter linear regression; GA-MLR) and genetic algorithm-artificial neural network (GA-ANN) global models have been used for prediction of the toxicity of phenols to Tetrahymena pyriformis. The data set was divided into 150 molecules for training, 50 molecules for validation, and 50 molecules for prediction sets. A large number of descriptors were calculated and the genetic algorithm was used to select variables that resulted in the best-fit to models. The six molecular descriptors selected were used as inputs for the models. The MLR model was validated using leave-one-out, leave-group-out cross-validation and external test set. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the MLR model. Comparison of the results obtained using the ANN model with those from the MLR revealed the superiority of the ANN model over the MLR. The root mean square error of the training, validation, and prediction sets for the ANN model were calculated to be 0.224, 0.202, and 0.224 and correlation coefficients (r2) of 0.926, 0.943, and 0.925 were obtained. The improvements are because of non-linear correlations of the toxicity of the compounds with the descriptors selected. The prediction ability of the GA-ANN global model is much better than that of previously proposed models.

GRAPHICAL ABSTRACT

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors

AIM

To develop an artificial neural network model for predicting the resistance index (RI) of taxoids.

METHODS

A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models.

RESULTS

The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933.

CONCLUSION

The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.

Axial ligand tuning of a nonheme iron(IV)-oxo unit for hydrogen atom abstraction

The reactivities of mononuclear nonheme iron(IV)-oxo complexes bearing different axial ligands, [Fe(IV)(O)(TMC)(X)](n+) [where TMC is 1,4,8,11-tetramethyl-1,4,8,11-tetraazacyclotetradecane and X is NCCH(3) (1-NCCH(3)), CF(3)COO(-) (1-OOCCF(3)), or N(3)(-) (1-N(3))], and [Fe(IV)(O)(TMCS)](+) (1'-SR) (where TMCS is 1-mercaptoethyl-4,8,11-trimethyl-1,4,8,11-tetraazacyclotetradecane), have been investigated with respect to oxo-transfer to PPh(3) and hydrogen atom abstraction from phenol O H and alkylaromatic C H bonds. These reactivities were significantly affected by the identity of the axial ligands, but the reactivity trends differed markedly. In the oxidation of PPh(3), the reactivity order of 1-NCCH(3) > 1-OOCCF(3) > 1-N(3) > 1'-SR was observed, reflecting a decrease in the electrophilicity of iron(IV)-oxo unit upon replacement of CH(3)CN with an anionic axial ligand. Surprisingly, the reactivity order was inverted in the oxidation of alkylaromatic C H and phenol O H bonds, i.e., 1'-SR > 1-N(3) > 1-OOCCF(3) > 1-NCCH(3). Furthermore, a good correlation was observed between the reactivities of iron(IV)-oxo species in H atom abstraction reactions and their reduction potentials, E(p,c), with the most reactive 1'-SR complex exhibiting the lowest potential. In other words, the more electron-donating the axial ligand is, the more reactive the iron(IV)-oxo species becomes in H atom abstraction. Quantum mechanical calculations show that a two-state reactivity model applies to this series of complexes, in which a triplet ground state and a nearby quintet excited-state both contribute to the reactivity of the complexes. The inverted reactivity order in H atom abstraction can be rationalized by a decreased triplet-quintet gap with the more electron-donating axial ligand, which increases the contribution of the much more reactive quintet state and enhances the overall reactivity.

Application of quantum topological molecular similarity descriptors in QSPR study of the O-methylation of substituted phenols

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2--C1--O--H fragment of the parent molecule. Indeed, the C2--X and C4--X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, lambda1, lambda2, lambda3, epsilon, and K(r) were detected as highly influential parameters.