In this work, we carried out a systematic and detailed study of water under severely confined conditions, based on atomistic molecular dynamics (MD) simulations. Specifically, MD simulations were performed for water confined in single-walled capped carbon nanotubes of chiral indices between (4,4) and (10,10). The structure and dynamics of confined water, and its dependence on the diameter and length of the capped carbon nanotubes (CCNTs) was examined, alongside with the influence of the latter on water immersion. Our results show that the axial water density decreases with increasing distance from the open face of the CCNTs, and that water forms well-defined shells along the radius of the CCNTs. The confined water molecules closer to the water-wall interfacial region tend to orient themselves pointing their OH bonds towards the wall. This trend becomes less evident in the inner regions of the CCNTs, so that the water molecules in the central region of the (10,10) CCNT are almost randomly oriented. On the other hand, the water molecules inside the (6,6) CCNT form a single-file chain along the CCNT's axis. Furthermore, the average number of hydrogen bonds per water molecule decreases with increasing length of the CCNT and decreasing diameter. Hydrogen bonding and water orientations in the CCNTs significantly affect the mobility of water, being the mobility of water faster in the (7,7) CCNT than in other CCNTs of the same length. (C) 2018 Published by Elsevier B.V.

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The results of experimental (conductometry, NMR-diffusometry) and computational (MD simulations) studies on the binary mixtures of room-temperature imidazolium- and pyridinium-based ionic liquids (RTILs) with acetonitrile (AN), gamma-butyrolactone (gamma-BL) and propylene carbonate (PC) over the wide composition range are presented. The conductometric analysis was carried out in the RTILS mole fraction (chi(RTIL),) range between 0.0 and 0.5 in the temperature ranges from 278.15 to 328.15 K. Notably, all binary systems exhibit conductivity maximum at, chi(RTIL), between 0.1 and 0.2. This maximum slightly shifts towards smaller chi(RTIL), as counter-ion gets larger. Self-diffusion coefficients of solvent molecules and cations were obtained by means of H-1-NMR-diffusometry in mixtures of 1-n-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide tetrafluoroborate, trifluoro methanesulfonate and hexafluorophosphate with PC, gamma-BL and AN over the whole concentration range at 300 K. The relative diffusion coefficients of solvent molecules to cations as a function of composition were established to be depended on a solvent but not on the anion of RTIL. In all cases the relative diffusion coefficients demonstrate a plateau at chi(RTIL) < 0.2 and then increase significantly for AN, moderately for gamma-BL or negligibly for PC at higher RTIL content. Such behavior was attributed to the different solvation ability of the investigated solvents. In the mixtures with [BMIM] [PF6] anion diffusion coefficients derived from P-31 NMR were found to be higher than the corresponding values for cation in RTIL-depleted systems and lower in the RTIL-enriched systems. The inversion of relative ion diffusion is observed near the equimolar composition and being insensitive to the solvent. At this point a remarkable change in the diffusion mechanism of ion of RTIL is expected. Additionally, molecular dynamics simulations on the binary mixtures of 1-ethyl-3-methylimidazolium and 1-butyl-3-methylimidazolium tetrafluoroborates with AN were performed. The conductivity correlates with a composition of ion aggregates simplifying its predictability. Large amounts of AN stabilize ion pairs, although destroy greater ion aggregates. Based on the simulation results, we show that conductivity of the studied mixtures significantly depend on the ion aggregation.

The water gas shift is a very relevant reaction for producing hydrogen for the synthesis of alcohols and ammonia, reforming of hydrocarbons and alcohols, and fuel cells. The current technologies use significant amounts of energy and the industrial catalysts suffer from several limitations that lead to the reduction of their performance. These limitations can be surpassed by the use of bimetallic-based catalysts in the form of metal alloys or of dispersed metal particles on a metallic support.

P-glycoprotein (P-gp) has been considered an important molecular target in the reversal of multidrug resistance (MDR). As such, the development of P-gp modulators able to restore drug sensitivity in resistant cells is still considered one of the most promising strategies for overcoming MDR. Since the identification of the P-gp's role in MDR, several studies have been performed in order to develop effective P-gp modulators and understand the efflux mechanism. However, no efflux modulator is still clinically available for treating multidrug-resistant cancers. Nevertheless, recent experimental studies suggest that MDR can be surpassed by targeting a specific region within the ABC transporter structure rather than the polyspecific drug-binding pocket. This article will focus on the information available about this new target region and on a brief overview of which scaffolds would be suitable for modulating P-gp at this new location. (C) 2017 John Wiley & Sons, Ltd

The last decade has been seeing an increase of public-private partnerships in drug discovery, mostly driven by factors such as the decline in productivity, the high costs, time, and resources needed, along with the requirements of regulatory agencies. In this context, traditional computer-aided drug discovery techniques have been playing an important role, enabling the identification of new molecular entities at early stages. However, recent advances in chemoinformatics and systems pharmacology, alongside with a growing body of high quality, publicly accessible medicinal chemistry data, have led to the emergence of novel in silico approaches. These novel approaches are able to integrate a vast amount of multiple chemical and biological data into a single modeling equation. The present review analyzes two main kinds of such cutting-edge in silico approaches. In the first subsection, we discuss the updates on multitasking models for quantitative structure-biological effect relationships (mtkQSBER), whose applications have been significantly increasing in the past years. In the second subsection, we provide detailed information regarding a novel approach that combines perturbation theory with quantitative structure-property relationships modeling tools (ptQSPR). Finally, and most importantly, we show that the joint use of mtk-QSBER and ptQSPR modeling tools are apt to guide drug discovery through its multiple stages: from in vitro assays to preclinical studies and clinical trials.

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.