Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

A review on structural mechanisms of protein-persistent organic pollutant (POP) interactions

With the advent of the industrial revolution, the accumulation of persistent organic pollutants (POPs) in the environment has become ubiquitous. POPs are halogen-containing organic molecules that accumulate, and remain in the environment for a long time, thus causing toxic effects in living organisms. POPs exhibit a high affinity towards biological macromolecules such as nucleic acids, proteins and lipids, causing genotoxicity and impairment of homeostasis in living organisms. Proteins are essential members of the biological assembly, as they stipulate all necessary processes for the survival of an organism. Owing to their stereochemical features, POPs and their metabolites form energetically favourable complexes with proteins, as supported by biological and dose-dependent toxicological studies. Although individual studies have reported the biological aspects of protein-POP interactions, no comprehensive study summarizing the structural mechanisms, thermodynamics and kinetics of protein-POP complexes is available. The current review identifies and classifies protein-POP interaction according to the structural and functional basis of proteins into five major protein targets, including digestive and other enzymes, serum proteins, transcription factors, transporters, and G-protein coupled receptors. Further, analysis detailing the molecular interactions and structural mechanism evidenced that H-bonds, van der Waals, and hydrophobic interactions essentially mediate the formation of protein-POP complexes. Moreover, interaction of POPs alters the protein conformation through kinetic and thermodynamic processes like competitive inhibition and allostery to modulate the cellular signalling processes, resulting in various pathological conditions such as cancers and inflammations. In summary, the review provides a comprehensive insight into the critical structural/molecular aspects of protein-POP interactions.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

Quantitative structure-property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software

The soil adsorption coefficient (K_oc) is an environmental fate parameter that is essential for environmental risk assessment. However, obtaining K_oc requires a significant amount of time and enormous expenditure. Thus, it is necessary to efficiently estimate K_oc in the early stages of a chemical's development. In this study, a quantitative structure-property relationship (QSPR) model was developed using calculated physicochemical properties and molecular descriptors with the OPEn structure-activity/property Relationship App (OPERA) and Mordred software using the largest available K_oc dataset. Specifically, we compared the accuracies of the model using the light gradient boosted machine (LightGBM), a gradient boosting decision tree (GBDT) algorithm, with those of previous models. The experimental results suggested the potential to develop a QSPR model that will produce highly accurate K_oc values using molecular descriptors and physicochemical properties. Unlike previous studies, the use of a combination of LightGBM, OPERA and Mordred enables the prediction of K_oc for many chemicals with high accuracy. In this study, OPERA was used to calculate the physicochemical properties, and Mordred was used to calculate molecular descriptors. The wide range of chemicals covered by OPERA and Mordred enables the analysis of a diverse range of chemical compounds. We also report a method to tune the LightBGM program. The use of fast-processing software, such as LightGBM, enables parameter tuning of a method required to obtain best performance. Our research represents one of the few studies in the field of environmental chemistry to use LightGBM. Using physicochemical properties as well as molecular descriptors, we could develop highly accurate K_oc prediction models when compared to prior studies. In addition, our QSPR models may be useful for preliminary environmental risk assessment without incurring significant costs during the early chemical developmental stage.

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

The soil adsorption coefficient (K_OC ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to study and obtain the K_OC value of a pesticide. Furthermore, acquiring the K_OC requires a large cost. It is necessary to efficiently estimate the K_OC value in the early stages of pesticide development. In the present study, the experimental values of physicochemical properties and molecular descriptors of chemical structures were collected to develop a quantitative structure-property relationship (QSPR) model, and the prediction performance of the model was evaluated. More specifically, we compared the accuracies of models based on a gradient boosting decision tree, multiple linear regression, and support vector machine. The experimental results suggest that it is possible to develop a QSPR model with high accuracy using both the molecular descriptors calculated from the structural formula and experimental values of physicochemical properties from open literature and databases. Comparing to the previously established models, we achieved high prediction accuracy, fitness, and robustness by only using freeware. Therefore, our developed QSPR models can be useful preliminary risk assessment in the early developmental stages of pesticides. Environ Toxicol Chem 2020;39:1451-1459. © 2020 SETAC.

A Round Trip from Medicinal Chemistry to Predictive Toxicology

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.

A quantum chemical based toxicity study of estimated reduction potential and hydrophobicity in series of nitroaromatic compounds

Nitroaromatic compounds and the products of their degradation are toxic to bacteria, cells and animals. Various studies have been carried out to better understand the mechanism of toxicity of aromatic nitrocompounds and their relationship to humans and the environment. Recent data relate cytotoxicity of nitroaromatic compounds to their single- or two-electron enzymatic reduction. However, mechanisms of animal toxicity could be more complex. This work investigates the estimated reduction and oxidation potentials of 34 nitroaromatic compounds using quantum chemical approaches. All geometries were optimized with density functional theory (DFT) using the solvation model based on density (SMD) and polarizable continuum model (PCM) solvent model protocols. Quantitative structure-activity/property (QSAR/QSPR) models were developed using descriptors obtained from quantum chemical optimizations as well as the DRAGON software program. The QSAR/QSPR equations developed consist of two to four descriptors. Correlations have been identified between electron affinity (E_LUMO) and hydrophobicity (log P).

Assessing predictive uncertainty in comparative toxicity potentials of triazoles

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species.