Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0 (c) software.

Mixed self-assembled monolayers (SAMs) of alkanethiols on gold nanoparticles emerged as key elements to set up many systems that have various potential applications in the field of nanotechnology. However, since investigations on such nanoparticle molecular systems are scarce, these have to be studied in depth in order to understand and control their physical and chemical properties at the molecular level. This chapter focuses on some of these topics, including the synthesis’s strategies of various mixed arrangements of SAMs coating gold nanoparticles such as completely mixed arrangements (random and ordered) and demixed arrangements (Janus), whose molecular structure still remains elusive, as well as on their properties and applications. © 2018 Elsevier Inc.

Detection of Patient Zero is an increasing concern in a world where fast international transports makes pandemia a Public Health issue and a social fear, in cases such as Ebola or H5N1. The development of a medical social network and data visualization information system, which would work as an interface between the patient medical data and geographical and/or social connections, could be an interesting solution, as it would allow to quickly evaluate not only individuals at risk but also the prospective geographical areas for imminent contagion. In this work we propose an ideal model, and contrast it with the status quo of present medical social networks, within the context of medical data visualization. From recent publications, it is clear that our model converges with the identified aspects of prospective medical networks, though data protection is a key concern and implementation would have to seriously consider it.

Computational modeling is more and more often used in studies of novel ionic liquids. The inevitable side-effect is the growing number of similar computations that require automation. This article introduces NaRIBaS (Nanomaterials and Room Temperature Ionic Liquids in Bulk and Slab)-a scripting framework that combines bash scripts with computational codes to ease modeling of nanomaterials and ionic liquids in bulk and slab. NaRIBaS helps to organize and document all input and output data, thus, improving the reproducibility of computations. Three examples are given to illustrate the NaRIBaS workflows for density functional theory (DFT) calculations of ionic pairs, molecular dynamics (MD) simulations of bulk ionic liquids (ILs), and MD simulations of ILs at an interface.

In this study, we examined the thickness of the electrical double layer (EDL) in ionic liquids using density functional theory (DFT) calculations and molecular dynamics (MD) simulations. We focused on BF4- anion adsorption from the 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF(4)) ionic liquid on the Au(111) surface. At both DFT and MD levels, we evaluated the capacitance-potential dependence for the Helmholtz model of the interface. Using MD simulations, we also explored a more realistic, multilayer EDL model accounting for the ion layering. Concurrent analysis of the DFT and MD results provides a ground for thinking whether the electrical double layer in ionic liquids is one- or multi-ionic-layer thick.

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure-activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R-2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq(2) (leave-one-out correlation coefficient) = 0.864, F (Fisher's test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and N-ext (number of compounds in external test set) = 20, R-2 = 0.853, (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R-2 = 0.925, LOOq(2) = 0.924, F = 950.686, RMS = 0.447 for the training set, and N-ext = 20, R-2 = 0.896, = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

The Q2DTor program (Quantum 2-Dimensional Torsions) is designed to calculate accurate rotational-vibrational partition functions (also called rovibrational partition functions) and thermodynamic functions for molecular systems having two [1] or more torsions. Systems with more than two torsions can also be studied by treating the torsions by pairs. The program searches for all the torsional conformers and evaluates the rovibrational partition function using the multi-structural harmonic oscillator (MS-HO) approximation and the extended two-dimensional torsion (E2DT) approximation. The latter incorporates full coupling of the two torsions by means of the two-dimensional non-separable (2D-NS) approximation [2], and it also includes their influence on the remaining degrees of freedom. The program also calculates the ideal gas-phase standard-state thermodynamic functions at the requested temperatures. Twenty molecules have been used to test Q2DTor. Program summary Program Title: Q2DTor Program Files doi: http:/dx.doi.org/10.17632/wbechgc2kp.1 Licensing provisions: GNU GPL v3 Programming language: Python 2.7 Nature of problem: Calculation of accurate partition functions and thermodynamic functions in molecular systems involving two torsional modes. Torsional anharmonicity is treated quantically and includes full coupling in the kinetic and potential energies between the torsions and between the torsions and the rest of the degrees of freedom. Solution method: The program uses the variational method to solve the Schrodinger equation of a two-dimensional torsional potential using Fourier series. All of the remaining degrees of freedom (non-torsional) are incorporated through a projected (the torsional modes are removed) rigid-rotator harmonic-oscillator partition function which is calculated at every torsional stationary point and that is allowed to vary with the torsional motion. The integration of the rovibrational partition function over the torsional space leads to a mixed quantum-classical vibrational partition function, which is transformed into a full quantum partition function by including the quantum contribution due to the torsions. For the evaluation of the integral, the rovibrational partition function at nonstationary points is carried out through a Delaunay triangulation procedure using the calculated rovibrational partition functions at the stationary points as nodes. Additional comments including Restrictions and Unusual features: The program is limited to two coupled torsional modes. References: [1] L. Simon-Carballido et al., J. Chem. Theory Comput. 13 (2017) 3478. [2] A. Fernandez-Ramos, J. Chem. Phys. 138 (2013) 134112.

Introduction: Quantitative Structure-Activity Relationship (QSAR) models are becoming one of the most interesting fields for developing therapeutics and therapeutics related patents. At present, QSAR methodologies comprise a series of possibilities, including joining forces with machine learning methods and increasing even more the swiftness they might bring to the prospective development of therapeutics in the Health Sciences scope. Areas covered: After evaluating the period from 2010 to early 2018, the areas covered by the reviewed QSAR based therapeutics patents comprise three main fields (drug development, risk assessment and novel QSAR methodologies), and several areas, from cancer and cancer related symptomatology to neurodegenerative diseases, such as Parkinson's disease, or even monitoring several chemical particles carrier-mediums or interface frontiers. Expert opinion: Among the several conclusions drawn from this reviewing, some pertain to the near future of investigative research on QSAR based inventions for therapeutic purposes, while others include the prospective of an even more grown interest on cytotoxicity assessment with in silico models and protocols. Further, the type of compounds described in these types of patents is likely to see an increase in neurodegenerative diseases therapeutics, as the novel methodologies meet the challenging global health needs as human life expectancy increases.