RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

The low cost and high selectivity toward CO2 and H-2 of Ni-Cu catalysts for the methanol steam reforming (MSR) make them excellent candidates for the production of hydrogen from methanol. Moreover, bimetallic Ni-Cu alloy blocks the production of undesirable methane, CO, and coke. In this work, the full MSR mechanism on Ni-Cu surfaces was studied by density functional theory calculations, a step forward to explain their high activity and selectivity for that reaction. The MSR evolves on Ni-Cu surfaces mostly through the methanol decomposition on the catalytic surface followed by the water-gas shift (WGS) reaction, which converts the CO obtained from methanol decomposition to CO2 and additional H-2. Direct CO2 formation from methanol should be a minority route associated with the presence of combed surfaces in the catalysts. Finally and most importantly, the Ni-Cu alloy suppresses the formation of methane and coke while the high desorption barrier for CO species avoids its production. Overall, the information gathered in this work alongside the insights into the MSR reaction mechanism on these surfaces shall aid in the future design of improved Ni-Cu alloy-based catalysts for hydrogen production through methanol.

<jats:p>This chapter brings important novel insights and perspectives to the urging contemporary debate on public hygienist policies. The authors intend to explore how an episode of history of science can be used to explore the struggles of universal pandemic responses. The focus will be on the inception of science-based legislation, created to deal with public health emergencies, and their communication and social acceptance. They argue if any of the symptoms of science misinformation and a weak science foundation of legislative action identified in the 2020 coronavirus pandemic can be identified in an early 20th-century outbreak of bubonic plague in Portugal. They present a national legislative policy timeline towards the pandemic effort in the form of consolidated legislative responses to fight Porto's emerging pandemic in 1899. They also provide future studies on science-based policy with newfound material, aiding the characterization of the communication and eventual harmonization of concerted responses in preempting the spread of pandemics. </jats:p>

<jats:title>ABSTRACT</jats:title><jats:p>The modulation of drug efflux by P-glycoprotein (P-gp, ABCB1) represents one of the most promising approaches to overcome multidrug resistance (MDR) in cancer cells, however the mechanisms of drug specificity and signal-transmission are still poorly understood, hampering the development of more selective and efficient P-gp modulators. In this study, the impact of four P-gp mutations (G185V, G830V, F978A and ΔF335) on drug-binding and efflux-related signal-transmission mechanism was comprehensively evaluated in the presence of ligands within the drug-binding pocket (DBP), which are experimentally related with changes in their drug efflux profiles. The severe repacking of the transmembrane helices (TMH), induced by mutations and exacerbated by the presence of ligands, indicates that P-gp is sensitive to perturbations in the transmembrane region. Alterations on drug-binding were also observed as a consequence of the TMH repacking, but were not always correlated with alterations on ligands binding mode and/or binding affinity. Finally, and although all P-gp variants <jats:italic>holo</jats:italic> systems showed considerable changes in the intracellular coupling helices/nucleotide-binding domain (ICH-NBD) interactions, they seem to be primarily induced by the mutation itself rather than by the presence of ligands within the DBP. The data further suggest that the changes in drug efflux experimentally reported are mostly related with changes on drug specificity rather than effects on signal-transmission mechanism. We also hypothesize that an increase in the drug-binding affinity may also be related with the decreased drug efflux, while minor changes in binding affinities are possibly related with the increased drug efflux observed in transfected cells.</jats:p>

Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

In this study, the full reaction mechanism for N2O hydrogenation on silver doped Au(210) surfaces was investigated in order to clarify the experimental observations. Density functional theory (DFT) calculations were used to state the most favorable reaction paths for individual steps involved in the N2O hydrogenation. From the DFT results, the activation energy barriers, rate constants and reaction energies for the individual steps were determined, which made it possible to elucidate the most favorable reaction mechanism for the global catalytic process. It was found that the N2O dissociation occurs in surface regions where silver atoms are present, while hydrogen dissociation occurs in pure gold regions of the catalyst or in regions with a low silver content. Likewise, N2O dissociation is the rate determining step of the global process, while water formation from O adatoms double hydrogenation and N-2 and H2O desorptions are reaction steps limited by low activation energy barriers, and therefore, the latter are easily carried out. Moreover, water formation occurs in the edges between the regions where hydrogen and N2O are dissociated. Interestingly, a good dispersion of the silver atoms in the surface is necessary to avoid catalyst poison by O adatoms accumulation, which are strongly adsorbed on the surface.

Atorvastatin (ATV) is a statin member consumed in high quantities worldwide. In response to that, the occurrence of ATV in environmental waters has become a reality, highlighting the need of rapid and sensitive analytical devices for its monitoring. In this work, the first electrochemical molecularly imprinted polymer (MIP) sensor for the detection of ATV in water samples is presented. Computational studies were conducted based on quantum mechanical (QM) calculations and molecular dynamics (MD) simulations for rational selection of a suitable functional monomer and to study in detail the templatemonomer interaction, respectively. The sensor was prepared by electropolymerisation of the selected 4aminobenzoic acid (ABA) monomer with ATV, acting as template, on screen printed carbon electrode (SPCE). Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques were applied to characterise the modified electrode surfaces. The quantitative measurements were carried out with differential pulse voltammetry (DPV) in 0.1 M phosphate buffer (pH = 7). After investigation and optimisation of important experimental parameters, a linear working range down to 0.05 mmol L-1 was determined with a correlation coefficient of 0.9996 and a limit of detection (LOD) as low as 0.049 mmol L-1 (S/N = 3). High sensitivity and selectivity of the prepared sensor were demonstrated with the ability to recognise ATV molecules over its closer structural analogues. Moreover, the sensor was quickly and successfully applied in spiked water samples, proving its potential for future on-site monitoring of ATV in environmental waters.

Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including 'mixtures-out'- and 'compounds-out'-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.