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The objective of the subsequent study was to examine the probability of PFAS (per- and polyfluorinated alkyl substances) binding to various NHRs (nuclear hormone receptors) and to identify their structural features that contribute most to the binding score (BS). We evaluated the BS for PFAS in relation to 7 selected NHRs - 4 with additional antagonist forms (Retinoid X receptor alpha - RXRα, Liver X receptor alpha - LXRα, Liver X receptor beta - LXRβ, Estrogen receptor alpha - ERα, Estrogen receptor alpha antagonist - anti-ERα, Estrogen receptor beta - ERβ, Estrogen receptor beta antagonist - anti-ERβ, Glucocorticoid receptor - GR, Glucocorticoid receptor antagonist - anti-GR, Androgen receptor - AR, Androgen receptor antagonist - anti-AR). We based our study on the results of molecular docking, which we used to develop MLR-QSAR (Multiple Linear Regression - Quantitative Structure-Activity Relationship) models. The models we developed allowed us to predict the BS for an extensive set of PFAS compounds from the NORMAN database (more than 4000) - virtual screening. The probability of PFAS binding to selected receptors was determined by structural features such as particle size, branching, and fluorine content. These variables were also identified in the literature reports of experimental studies as the most important for this group of compounds. The research focused on receptors from the terpenoid group. The RXRα, LXRα and β, GR, and anti-GR receptors were shown to be the group less likely to be affected by PFAS. Sex hormones such as AR, anti-AR, ERα and ERβ with their antagonist forms are the most affected., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R 2  = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF 2 groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This work aimed to verify whether it is possible to extend the applicability domain (AD) of existing QSPR (Quantitative Structure-Property Relationship) models by employing a strategy involving additional quantum-chemical calculations. We selected two published QSPR models: for water solubility, logS W , and vapor pressure, logVP of PFAS as case studies. We aimed to enlarge set of compounds used to build the model by applying factorial planning to plan the augmentation of the set of these compounds based on their structural features (descriptors). Next, we used the COSMO-RS model to calculate the logS W and logVP for selected chemicals. This allowed filling gaps in the experimental data for further training QSPR models. We improved the published models by significantly extending number of compounds for which theoretical predictions are reliable (i.e., extending the AD). Additionally, we performed external validation that had not been carried out in original models. To test effectiveness of the AD extension, we screened 4519 PFAS from NORMAN Database. The number of compounds outside the domain was reduced comparing the original model for both properties. Our work shows that combining physics-based methods with data-driven models can significantly improve the performance of predictions of phys-chem properties relevant for the chemical risk assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

In the present work, we have proposed a statistical model predicting the toxicity of ionic liquids (ILs) to Vibrio fischeri bacteria using the Quantitative Structure-Activity Relationships (QSAR) method. The model was developed with Multiple Linear Regression (MLR) technique, using the Gutman molecular topological index (GMTI), the lopping centric information index (LOC) and the number of oxygen atoms. Presented model is characterized by the good fit to the experimental data (R(2) = 0.78), high robustness (Q(2)CV = 0.72) and good predictive ability (Q(2)EXT = 0.75). This approach, with using very simple descriptors, helps to initially evaluate the toxicity of newly designed ionic liquids. The studied toxicity of ionic liquids depends mainly on their cations' structure: larger, more branched cations with long alkyl chains are more toxic than the smaller, linear ones. The presence of polar functional groups in the cation's structure reduces the toxic properties of ionic liquids. The structure of the anion has little effect on the toxicity of the studied ionic liquids. Obtained results will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of ionic liquids., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the data into the training and test set were examined. It has been shown that the CORAL provides a reliable tool that could be used to build up a QSAR of the pEC50., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Polychlorinated dibenzothiophenes (PCDTs) could be classified as persistent organic pollutants (POPs) in the environment and are particularly interesting due to their structural resemblance to highly toxic dioxins. We show here some basic environmental properties such as n-octanol water (K(OW)), n-octanol/air (K(OA)) and air/water (K(AW)) partition coefficients as well as Henry's law constants (K(H)) for all 135 congeners of chlorodibenzothiophene. Predictions were made by regression of principal components (PCR) and with aid of a set of standard chemicals, for which physical-chemical properties are well featured. Computed K(OW), K(OA), K(AW) and K(H) values for mono-CDTs ranged, respectively, between 4.66 and 4.71, 7.48 and 7.55, -2.84 and -2.82, 3.56 and 3.74; for di-CDTs between 5.02 and 5.28, 8.03 and 8.29, -3.01 and -2.95, 2.42 and 2.75; for tri-CDTs between 5.53 and 5.70, 8.65 and 8.87, -3.2 and -3.11, 1.58 and 1.92; for tetra-CDTs between 5.95 and 6.13, 9.27 and 9.50, -3.39 and -3.27, 1.02 and 1.33; for penta-CDTs between 6.38 and 6.51, 9.88 and 10.05, -3.54 and -3.45, 0.72 and 0.88; for hexa-CDTs between 6.83 and 6.97, 10.54 and 10.66, -3.72 and -3.64, 0.47 and 0.56; for hepta-CDTs between 7.28 and 7.35, 11.12 and 11.20, -3.81 and -3.87, 0.33 and 0.38; for octa-CDT 7.74, 11.78, -4.04 and 0.23. An estimated value of the three types of partition coefficient and Henry's law constants suggest that polychlorinated dibenzothiophenes are lipophilic and semi-volatile persistent organic pollutants. Their mobility in the environment seems to be very similar to that of some well-known POPs such as polychlorinated dibenzofurans, -dibenzo-p-dioxins, and -biphenyls or organochlorine pesticides.