Reliable force field (FF) is a central issue in successful prediction of physical chemical properties via computer simulations. This work introduces refined FF parameters for six popular ionic liquids (ILs) of the pyridinium family (butylpyridinium tetrafluoroborate, bis(trifluoromethanesulfonyl)imide, dicyanamide, hexafluorophosphate, triflate, chloride). We elaborate a systematic procedure, which allows accounting for specific cationanion interactions in the liquid phase. Once these interactions are described accurately, all experimentally determined transport properties can be reproduced. We prove that three parameters per interaction site (atom diameter, depth of potential well, point electrostatic charge) provide a sufficient basis to predict thermodynamics (heat of vaporization, density), structure (radial distributions), and transport (diffusion, viscosity, conductivity) of ILs at room conditions and elevated temperature. The developed atomistic models provide a systematic refinement upon the well-known Canongia LopesPadua (CL&P) FF. Together with the original CL&P parameters the present models foster a computational investigation of ionic liquids.

The transfer of ions and neutral particles through water/organic interfaces has been widely studied in the last few decades by both experimental and theoretical methods. The reason for the never ceasing interest in this field is the importance of transport phenomena in electrochemistry, biochemistry and separation science. In the current paper the solvation Helmholtz free energy profile of a methane molecule is presented, with respect to the intrinsic (i.e., real, capillary wave corrugated) interface of water and 1,2-dichloroethane, as obtained from constrained molecular dynamics simulations. The results of the current calculation are analysed in comparison with the solvation free energy profile of the chloride ion across the same interface.

The charge and spin distribution in manganese-salen complexes were analyzed using different basis sets and density functionals. Five population analysis methods [Mulliken, Lowdin, Natural population analysis (NPA), atoms in molecules (AIM), and CHelpG] were used to characterize the charge distribution. Results show that NPA and AIM were the only methods capable of giving charges with the correct sign for all cases under study. According to the analysis of the natural charge and spin distributions, the salen ligand shows a complex behavior, counteracting the effect of the chloro and oxo ligands on the metal center. Furthermore, the presence of a chloride counter ion increases the oxo-radical character of Oxo-Mn(salen) complexes, which may play an important role in the rationalization of the catalytic properties of Mn(salen) complexes. (c) 2014 Wiley Periodicals, Inc.

Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.

Ionic liquids (ILs) constitute one of the hottest areas in chemistry since they have become increasingly popular as reaction and extraction media. Their almost limitless structural possibilities, as opposed to limited structural variations within molecular solvents, make ILs designer solvents. They also have been widely promoted as green solvents although their claimed relative nontoxicity has been frequently questioned. The Thinking in Structure-Activity Relationships (T-SAR) approach has proved to be an efficient method to gather relevant toxicological information of analog series of ILs. However, when data sets significantly grow in size and structural diversity, the use of computational models becomes essential. We provided such a computational solution in a previous work by introducing a reliable, predictive, simple, and chemically interpretable Classification and Regression Tree (CART) classifier enabling the prioritization of ILs with a favorable cytotoxicity profile. Even so, an efficient and exhaustive mining of SAR information goes beyond analog compound series and the applicability domain of quantitative SAR modeling. So, we decided to complement our previous findings based on the use of the CART classifier by applying the network-like similarity graph (NSG) approach to the mining of relevant structure-cytotoxicity relationship (SCR) trends. Finally, the SCR information concurrently gathered by both, quantitative (CART classifier) and qualitative (NSG) approaches was used to design a focused combinatorial library enriched with potentially safe ILs.

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanopartides to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanopartides against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.