Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβ_ML) and potentiometric sensitivity (PS_ML) of 200 ligands in complexes with the heavy metal ions Cu²⁺, Cd²⁺, and Pb²⁺. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R²) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R² of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Prediction of reaction rate constants of hydroxyl radical with chemicals in water

The rate constants (k_OH ) of the reactions between organic micropollutants with hydroxyl radical (•OH) in aqueous systems are an important parameter to evaluate the persistence of organic compounds in the environment. In this paper, a support vector machine (SVM) model based on five descriptors was built to predict the reaction rate constants (log K = (log k_OH )/M_W ). The quantitative structure-activity relationship (QSAR) model of log K was obtained from a training set (600 compounds) and validated with a test set (395 compounds). The coefficients of determination R² of the training and test sets are 0.923 and 0.925, respectively. The results suggest that the SVM model developed in this work possesses satisfactory prediction ability. PRACTITIONER POINTS: The rate constants of the reactions of organic micropollutants with •OH in aqueous systems were investigated. SVM model was established for the reaction rate constants (log K = (log k_OH )/M_W ). Only five molecular descriptors were used to predict 995 log K. A large data set was used for the test set (395 compounds).

Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR

Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.

Two new predictors combined with quantum chemical parameters for the selection of oxidants and degradation of organic contaminants: A QSAR modeling study

Oxidation is an attractive treatment method to effectively remove organic contaminants in water. In this study, degradation of 30 organic compounds in different oxidation systems was evaluated, including oxygen (O₂), hydrogen peroxide (H₂O₂), ozone (O₃) and hydroxyl radical (HO). First, a quantitative structure-activity relationship (QSAR) model for oxidation-reduction potentials (ORPs) of organics was developed and exhibited a good performance to predict ORP values of organics with evaluation indices of squared correlation coefficient (R²) = 0.866, internal validation (q²) = 0.811 and external validation (Q_ext²) = 0.669. Four quantum parameters, including f(+)_n, f(-)_n, E_HOMO and E_B3LYP dominate the ORP values. Subsequently, a relationship between reaction rates (k) and the difference of ORP for oxidants and organics (ΔE_oxi-org) was established, however, which was limited (R²= 0.697). Therefore, two new predictors (slopes and intercepts) are proposed based on the linear relationships between k values and ORPs of oxidants. These new predictors can be applied to estimate the reaction rates and minimum oxidation potential for organic compounds. Afterwards, to express the two predictors, QSAR models were established. The two optimal QSAR models fitted very well with experimental values and were demonstrated to be stable and accurate based on R² (0.982 and 0.965), q² (0.950 and 0.950) and Q_ext² (0.985 and 0.989). BO_x, q(H)⁺ and q(C)_x were main factors influencing the slopes and intercepts. This study developed methods to predict ORPs of organics and established two new predictors to estimate the reaction rates undergoing different oxidation processes, offering new insights into the oxidant selection.

Development of novel therapeutics for glaucoma filtration surgery based on transforming growth factor-β receptor 1 inhibition

QSAR modeling with computer-aided drug design was used for the in silico development of novel therapeutics for glaucoma filtration surgery.

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (k _OH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: [Formula: see text], RMS _train = 0.21, while for the test set is [Formula: see text], RMS _test = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.

Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach

Designing of advanced oxidation process (AOP) requires knowledge of the aqueous phase hydroxyl radical (^●OH) reactions rate constants (k _OH), which are strictly dependent upon the pH and temperature of the medium. In this study, pH- and temperature-dependent quantitative structure-property relationship (QSPR) models based on the decision tree boost (DTB) approach were developed for the prediction of k _OH of diverse organic contaminants following the OECD guidelines. Experimental datasets (n = 958) pertaining to the k _OH values of aqueous phase reactions at different pH (n = 470; 1.4 × 10⁶ to 3.8 × 10¹⁰ M^-1 s^-1) and temperature (n = 171; 1.0 × 10⁷ to 2.6 × 10¹⁰ M^-1 s^-1) were considered and molecular descriptors of the compounds were derived. The Sanderson scale electronegativity, topological polar surface area, number of double bonds, and halogen atoms in the molecule, in addition to the pH and temperature, were found to be the relevant predictors. The models were validated and their external predictivity was evaluated in terms of most stringent criteria parameters derived on the test data. High values of the coefficient of determination (R ²) and small root mean squared error (RMSE) in respective training (> 0.972, ≤ 0.12) and test (≥ 0.936, ≤ 0.16) sets indicated high generalization and predictivity of the developed QSPR model. Other statistical parameters derived from the training and test data also supported the robustness of the models and their suitability for screening new chemicals within the defined chemical space. The developed QSPR models provide a valuable tool for predicting the ^●OH reaction rate constants of emerging new water contaminants for their susceptibility to AOPs.

Modeling the aqueous phase reactivity of hydroxyl radical towards diverse organic micropollutants: An aid to water decontamination processes

The rate constants of the hydroxyl radical reactions (k_OH) with organic micropollutants (OMPs) in aqueous medium are important in designing the advanced oxidation processes (AOPs) for their removal. In this study, a quantitative structure-property relationship (QSPR) model for the prediction of k_OH of diverse and emerging OMPs was developed in accordance with the OECD guidelines. A large experimental data set (n = 995) comprised of compounds with k_OH values ranging from 7.9 × 10⁵ to 6.8 × 10¹⁰ M^-1 s^-1 was considered and several molecular descriptors were calculated. As a result, five descriptors were found to be important in predicting the k_OH values which related to the electronegativity, topological polar surface area, double bonds, average molecular weight, and halogen atoms in the molecule. The optimal model was validated internally and externally and several statistical stringent parameters were derived. High values of the coefficient of determination (R²) and small root mean squared error (RMSE) in the training (0.954; 0.17) and validation (0.925; 0.14) sets indicated high generalization and predictivity of the developed model. Other statistical parameters derived from the training and validation data also supported the robustness of the model. The proposed model outperformed the earlier QSARs reported for k_OH prediction. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of organic micropollutants to AOPs.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Quasi-SMILES for Nano-QSAR Prediction of Toxic Effect of Al2O3 Nanoparticles

The level of malondialdehyde (MDA) in wet tissue of different organs is utilized as a measure of toxic effect. The numerical data on the concentration of MDA in wet tissue of liver, kidneys, brain, and heart of rat is examined as the endpoint which are impacted by different dose (mg/kg), exposure time (3 and 14 days) and single oral treatment of aluminium nano-oxide (Al2O3) with 30 nm or 40 nm. An attempt to develop predictive model for this endpoint has been carried out in this work. SMILES is a traditional tool to represent molecular structure for QSPRs/QSARs. In contrast to traditional SMILES, so-called quasi-SMILES can be a tool to build up quantitative features – property / activity relationships (QFPRs/QFARs) for endpoints which are not defined by solely molecular structure, but by a group of physicochemical and/or biochemical conditions. The quasi-SMILES is the representation of the above eclectic conditions whereas the QFPR/QFAR are models of endpoints which are modified under impacts of these eclectic conditions.

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling

Quantitative structure-property relationship (QSPR) models which predict hydroxyl radical rate constants (kOH) for a wide range of emerging micropollutants are a cost effective approach to assess the susceptibility of these contaminants to advanced oxidation processes (AOPs). A QSPR model for the prediction of kOH of emerging micropollutants from their physico-chemical properties was developed with special attention to model validation, applicability domain and mechanistic interpretation. In this study, 118 emerging micropollutants including those experimentally determined by the author and data collected from the literature, were randomly divided into the training set (n=89) and validation set (n=29). 951 DRAGON molecular descriptors were calculated for model development. The QSPR model was calibrated by applying forward multiple linear regression to the training set. As a result, 7 DRAGON descriptors were found to be important in predicting the kOH values which related to the electronegativity, polarizability, and double bonds, etc. of the compounds. With outliers identified and removed, the final model fits the training set very well and shows good robustness and internal predictivity. The model was then externally validated with the validation set showing good predictive power. The applicability domain of the model was also assessed using the Williams plot approach. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of micropollutants to AOPs.

Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches

A quantitative structure–activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC₅₀ of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.