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Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC 90 ), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power ( Q 2 EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

This work aimed to investigate the interaction of bovine serum albumin with newly synthesized potent new pyrene derivatives (PS1 and PS2), which might prove useful to have a better antibacterial character as found for similar compounds in the previous report [Low et al. Bioconjugate Chemistry 2014, 12, 2269-2284]. However, to date, binding studies with plasma protein are still unknown. Steady-state fluorescence spectroscopy and lifetime fluorescence studies show that the static interaction binding mode and binding constants of PS1 and PS2 are 7.39 and 7.81 [ K b × 10 5 (M -1 )], respectively. The experimental results suggest that hydrophobic forces play a crucial role in interacting pyrene derivatives with BSA protein. To verify this, molecular docking and molecular dynamics simulations were performed to predict the nature of the interaction and the dynamic behavior of the two compounds in the BSA complex, PS1 and PS2, under physiological conditions of pH = 7.1. In addition, the free energies of binding for the BSA-PS1 and BSA-PS2 complexes were estimated at 300 K based on the molecular mechanics of the Poisson-Boltzmann surface (MMPBSA) with the Gromacs package. PS2 was found to have a higher binding affinity than PS1. To determine the behavior of the orbital transitions in the ground state geometry, we found that both compounds have similar orbital transitions from HOMO-LUMO via π → π* and HOMO-1-LUMO+1 via n → π*, which was included in the FMO analysis. A cytotoxicity study was performed to determine the toxicity of the compounds. Based on the MD study, the stability of the compounds with BSA and the dynamic binding modes were further revealed, as well as the nature of the binding force components involved and the important residues involved in the binding process. From the binding energy analysis, it can be assumed that PS2 may be more active than PS1.

Quantitative toxicity-toxicity relationship (QTTR) models have a great potential for improving the meaning of toxicological tests conducted on simple organisms. These models allow predicting the toxicological effect of a chemical based on its known toxicological effect in different toxicity tests, even against a different organism. This fact poses a great potential for predicting the toxicity of chemicals against higher organisms based on the results against lower ones. However, the possibility of developing such models is often restricted due to the low availability of data. We present a case study of developing the QTTR model for ionic liquids in different toxicological tests against the same species, in the face of insufficient experimental data (an additional confirmation for a different species is provided in the Supporting Information). In the presented case, we use a series of quantitative structure-activity relationship (QSAR) models developed to deliver the data concerning the toxicity of ionic liquids against human HeLa and MCF-7 cancer cell lines. We use these data to develop a QTTR model with an R 2 as high as 0.8. The benefit of applying the multi-objective genetic algorithm (MOGA-a genetic algorithm allowing for selection of the best set of explanatory features for several different dependent variables at the same time) as a QSAR model feature selecting strategy is presented and discussed.

We propose a new metric for long-range transport potential (LRTP), GIF, based on source-receptor analyses and evaluate the LRTP and persistence of a wide variety of chlorinated and brominated organic compounds using GIF and overall persistence (POV), respectively. We calculated GIF and POV using our global 3D dynamic multimedia model (FATE). Physicochemical properties were obtained from quantitative structure-property relationship (QSPR) models. The FATE-QSPR combined model enabled us to systematically investigate the LRTP and persistence of a wide variety of chemical substances. On average, the estimated GIF and POV for chlorinated compounds were larger than those for their brominated counterparts, with the largest and smallest values found for polychlorinated biphenyls and polybrominated dibenzodioxins, respectively. We also compared GIF with four differently defined LRTP metrics and two LRTP metrics obtained from a simple model. The results of our analyses indicate that the LRTP ranks can differ considerably among LRTP metrics, the differences being dependent on the governing environmental processes, relevant physicochemical properties, and multimedia model.

Congeners are molecules based on the same carbon skeleton but are different by the number of substituents and/or a substitution pattern. Examples are 1-chloronaphthalene, 1,4-dichloronaphthalene, and 1,3,8-trichloronaphthalene. Various persistent organic pollutants (POPs) exist in the environment as families of congeners. Very large numbers of possible congeners make their experimental characterization and risk assessment unfeasible. Computational high-throughput and quantitative structure-property relationship (QSPR) modeling has been limited by the lack of tools and approaches facilitating analysis of such POP families. We present a comprehensive approach that enables modeling of extremely large congeneric libraries. The approach involves three steps: (1) combinatorial generation of a library of congeners, (2) quantum chemical characterization of each structure at the PM6 semiempirical level to obtain molecular descriptors, and (3) analysis of the information generated in step 2. In steps 1-3, we employ combinatorial, computational, and cheminformatics techniques, respectively. Therefore, this hybrid approach is named "Combinatorial × Computational × Cheminformatics", or just abbreviated as C(3) (or C-cubed) approach. We demonstrate the usefulness of this approach by generating and characterizing Br- and Cl-substituted congeneric families of 23 typical POPs. The analysis of the resulting set of 1 840 951 congeners that includes Cl-, Br-, and mixed Br/Cl-substituted species, proves that, based on structural similarities defined by the molecular descriptors' values, the existing QSPR models developed originally for Cl- and Br-substituted congeners can be applied also to mixed Br/Cl-substituted ones. Thus, the C(3) approach may serve as a tool for exploring structural applicability domains of the existing QSPR models for congeneric sets.

Information about the mobility and the partitioning properties of brominated persistent organic pollutants, the environmental levels of which are sometimes higher than those of the chlorinated analogues, is limited. We estimated n-octanol/ water (log K(OW)), n-octanol/air (log K(OA)), and air/water (log K(AW)) partition coefficients for 1436 chloro- and bromo-analogues of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenyl ethers, and benzenes by employing quantitative structure-property relationship (QSPR) techniques. The searches for similar partitioning patterns were performed by means of two-dimensional cluster analysis. Five classes of compounds were identified. Each of the class is characterized by similar partition coefficients and, in consequence, similar environmental properties. Finally the data was fitted into a simple multimedia model involving the partitioning map. In addition, we found thatthe changes in the partition coefficients upon the replacement of chlorine with bromine were constant: 0.11, 0.31, and -0.21 per bromine atom for log K(OW), log K(OA), and log K(AW), respectively. On the basis of this observation, a method for rapid estimation of changes in the partition coefficient upon chlorine-bromine substitution was proposed.

Most of the recently published quantitative structure-property relationship (QSPR) models, which can be used to predict environmentally relevant physicochemical data for persistent organic pollutants (e.g., polychlorinated dibenzo- p-dioxins, dibenzofurans, and biphenyls), employ molecular descriptors obtained by means of relatively costly calculations at the density functional theory (DFT) level. However, new semiempirical methods, PM6 and RM1, have recently been developed by J. J. P. Stewart's group. In this study, we compared various QSPR models based on DFT (B3LYP functional) descriptors with the same models based on semiempirical (PM6 and RM1) descriptors. We recalibrated 10 previously published models (for different properties and groups of congeneric compounds) employing PM6 and RM1 descriptors instead of B3LYP ones. We demonstrated that by applying RM1 and PM6 descriptors, we could obtain QSPR models with quality similar to that of models based on B3LYP descriptors. This level of accuracy was out of reach for the models employing AM1- and PM3-based descriptors.

Total concentrations of mercury were determined using cold-vapor atomic absorption spectroscopy (CV-AAS) in the fruiting bodies of 16 species of wild mushrooms and underlying soil (0-10 cm) substrates collected in the areas of the Communes of Morag and Łukta in the county of Ostróda in northeastern Poland in 1997-1998. A total of 174 composite samples of caps, 174 stalks, 80 whole fruiting bodies (collectively 1254 specimens), and 252 soils were examined. Among several species of mushrooms analyzed, the greatest concentrations were between 1300 and 71000 ng.g(-1) of dry matter. These levels were found in the caps of Sweating mushroom (Clitocybe rivulosa), King Bolete (Boletus edulis), and Common Puffball (Lycoperdon perlatum) and also were characterized by the highest bioconcentration factors (BCF) for Hg, which ranged between 160 +/- 82 and 110 +/- 34. The cap to stalk quotient for mercury concentrations was approximately 2 for most of the species except Poison Pax (Paxilus involutus), which had a greater concentration in caps than in stalks and a quotient of 4.4 +/- 7.2. Hg concentrations in the underlying soil substrates (0-10 cm layer) ranged between 21 +/- 21 and 390 +/- 130 ng.g(-1) of dry matter. The results showed that the consumption of mushrooms, considered to be the sole dietary source of mercury at the highest or mean element concentrations found, is not hazardous at daily ingestion rates of less than 70 and 210 g of fresh product, which would result in a hazard index value of less than unity.

Chlordane components (CHLs) and their metabolites (heptachlor, cis-heptachlor epoxide, U82, MC4, trans-chlordane, MC5, cis-chlordane, MC7, oxychlordane, MC6- and trans- and cis-nonachlor) and aldrin, dieldrin, endrin, isodrin, endosulfan 1, endosulfan 2, and mirex were quantified in the soft tissues of blue mussel, a whole crab, and whole fishes collected from the spatially different sites in the Gulf of Gdańsk. Six to twelve chlordane compounds and metabolites and dieldrin were detected in all organisms examined while aldrin, endrin, isodrin, endosulfans 1 and 2, and mirex were not found above the detection limit of the method. The lipid weight based concentrations in Baltic biota were relatively small and ranged from 12 to 150 and 7.6-77 ng/g, while between 0.16 and 6.8 and 0.10-6.6 ng/g in fresh tissue, respectively. The profile (%) of chlordane compounds was very similar between various fish species with trans-nonachlor (28 +/- 17), cis-chlordane (23 +/- 18), oxychlordane (13 +/- 7), and heptachlor epoxide (11 +/- 5) as major constituents and was totally different in crab with oxychlordane as the most dominating (>65%) compound. Blue mussel, lamprey, and three-spined stickleback exhibited a smallest ability to metabolize CHLs, and such fishes as cod, lesser sand-eel, sand-eel, pikeperch, perch, round goby, flounder, and herring showed a slightly better ability, while crab was able to effectively metabolize most of CHL compounds except trans-nonachlor. A value of the quotient of the trans-nonachlor to cis-chlordane concentrations (N/C quotient) was 1.0 in blue mussel, 3.1 in crab, and between 0.9 and 1.8 in fish. Both the small concentrations of CHLs in all organisms and the values of N/C quotients close to 1 imply on a long-range aerial transport through movement of the air masses from the remote regions of the northern hemisphere as a main source of this pesticide in the Gulf of Gdańsk. The interdependences between the CHL profiles for various fish species and between different sampling sites were examined using the principal component analysis (PCA) method. Applying the PCA model the first four significant components explained 90% (43% + 23% + 15% + 8%) of the total variance in the data matrix.