In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding.

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logK_OW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R² = 0.95, RMSE_C = 0.75) and strong predictive ability (Q²_LOO = 0.93, RMSE_CV = 0.83). Leveraging the extensive NORMAN, we predicted logK_OW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logK_OW, water solubility logS_W, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (k_H), air-water partition coefficient (K_AW), octanol-air coefficient (K_OA), and soil adsorption coefficient (K_OC) exhibited favorable correlations with literature data (R² > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.

Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment. Properly developed QSAR/QSPR models can be used to assess the impact of these chemicals on humans and the environment. This work assesses 38 in silico models developed for this group of compounds, which mainly show physicochemical (22), and also toxic (8) and ecotoxic (8) properties. The evaluation of the models was carried out based on the (Q)SAR Model Reporting Format (QMRF), which was found in the QSAR Database (5) or was prepared manually, according to the information contained in scientific publications based on the QMRFEditor-v3.0.0 format (33). We based our evaluations on an individual assessment of each of the OECD principles described in the document and then summing up everything together. During the analysis, we identified 22 models as scientifically valid and could be used in the prediction of new compounds. Twelve of them contained all the information necessary to reproduce the model, and another 10, despite the lack of some information, are still reproducible. The other 16 models do not contain enough information to reproduce them and therefore they are scientifically invalid. The present work allows identifying the remaining gaps, needs, and recommendations that should be considered in further development of predictive models in the PFAS area.

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 2,209 PFAS. Consequently, 1,045 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 (R²=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 CF₂ 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.

Due to the excessive use of antibiotics in human and livestock treatments, growing antibiotic resistance has been observed. The high velocity of bacterial mutation impedes the healing process, especially for hospital patients with weak immune protection, often resulting in death. The World Health Organization (WHO) stressed the gravity of this issue and in 2017 published the first global priority pathogen list, which includes 20 bacteria, including Staphylococcus aureus. In the face of growing resistance to available antibiotics, new types of substances are under consideration as biocides, and moreover, increasing attention is given to ionic liquids (ILs). To this end, we performed computational high-throughput screening of 60 270 ionic liquids for agents active towards S. aureus. Moreover, in addition to the target properties, we analyzed the biodegradation process of prospective ILs to preliminarily estimate the environmental safety of the final biocide product. According to the U.S. Environmental Protection Agency (U.S. EPA) and the Organization for Economic Co-operation and Development (OECD) recommendations, we applied virtual screening via a classification QSAR model and a multimedia mass-balance model as a supporting tool in the design of safer ionic liquids. As a result, we explored the biocidal activity and prescreened the environmental safety of a large dataset of ionic liquids in a much shorter period of time and with less workload than required by the experimental research that could otherwise be performed. Finally, we listed 73 ILs with biocidal potential towards S. aureus that do not potentially exhibit environmentally adverse features.