QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors

QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC₅₀) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r² = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r² = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r² = 0.74 and 0.75 for the CORAL model).

Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin

The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-phenylboronic acids with hTTR was determined and analyzed by competitive fluorescence displacement assay integrated with computational methods. The laboratorial results indicated that the three test groups of model compounds exhibited a distinct binding affinity to hTTR. All the tested halo-phenylboronic acids, some of the tested halo-benzoic acids and halo-benzenesulfonic acids/sulfates were shown to be inactive with hTTR. Other halo-benzoic acids and halo-benzenesulfonic acids/sulfates were moderate and/or weak hTTR binders. The binding affinity of halo-benzoic acids and halo-benzenesulfonic acids/sulfates with hTTR was similar. The low distribution ability of the model compounds from water to hTTR may be the reason why they exhibited the binding potency observed with hTTR. By introducing other highly hydrophobic compounds, we observed that the binding affinity between compounds and hTTR increased with increasing molecular hydrophobicity. Those results indicated that the highly hydrophobic halo-benzoic acids and halo-benzenesulfonic acids/sulfates may be high-priority hTTR disruptors. Finally, a binary classification model was constructed employing three predictive variables. The sensitivity (S_n), specificity (S_p), predictive accuracy (Q) values of the training set and validation set were >0.83, indicating that the model had good classification performance. Thus, the binary classification model developed here could be used to distinguish whether a given ionizable compound is a potential hTTR binder or not.

Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra

Advances to the distributed, multi-core and fully cross-platform QuBiLS-MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS-MIDAS software is the only one that computes atom-pair and alignment-free geometrical MDs (3D-MDs) from several distance metrics other than the Euclidean distance, as well as alignment-free 3D-MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS-MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex-degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter-atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D-MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS-MIDAS 3D-MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS-MIDAS 3D-MDs and the QuBiLS-MIDAS 3D-MDs calculation can be embedded in other tools. A set of predefined QuBiLS-MIDAS 3D-MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D-MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D-to-3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS-MIDAS 3D-MDs codify chemical information orthogonal to the Dragon 0D-to-3D MDs. This set of predefined 3D-MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications.

Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R²) was 0.962 and coefficient determinations of cross-validation (R_cv²) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (R_ext²) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability.

In silico toxicity evaluation of dioxins using structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR)

Prediction of pEC₅₀ values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC₅₀ values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC₅₀ values and 162 compounds without pEC₅₀ values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC₅₀ value of the compound. Moreover, two-dimensional quantitative structure-activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC₅₀ values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and Neoplastic-80. In addition, the pEC₅₀ values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.

Ecotoxicological QSAR modelling of organic chemicals against Pseudokirchneriella subcapitata using consensus predictions approach

The present study provides robust consensus quantitative structure-activity relationship (QSAR) models developed from 334 organic chemicals covering a wide chemical domain for the prediction of effective concentrations of chemicals for 50% and 10% inhibition of algal growth. Only 2D descriptors with definite physicochemical meaning were employed for QSAR model building, whereas development, validation and interpretation were achieved following the strict Organization for Economic Co-operation and Development (OECD) recommended guidelines. Genetic algorithm along with stepwise approach was used in feature selection while the final QSAR models were derived using partial least squares regression technique. The applicability domain of the developed models was also checked. The obtained consensus models were then used to predict 64 organic chemicals having no definite observed responses while the confidence of predictions was checked by the 'prediction reliability indicator' tool. The developed models should be applicable for data gap filling in case of new or untested organic chemicals provided they fall within the domain of the model and can also be implemented to design safer alternatives to the environment.