Molecular dynamics simulations are used to study the structural properties of mixed self-assembled monolayers (SAMs) of 11-carbon alkanethiolate chains, comprising methyl-terminated and carboxylic acid-terminated tail groups, coating a planar gold surface and interfaced with water. Three different arrangements of the SAM-coated surfaces are compared, namely: random, ordered and Janus particles-type shells. Our simulation study reveals the different structural morphology of the SAM surfaces and shows how it influences their overall hydrophilicity, the hydrogen-bonding structure and the water molecules orientation.

Cancers can be considered as a group of diseases, where cells undergo an uncontrolled growth, and malignant tumors are formed. Between them, bladder cancer (BLC) is an aggressive type of cancer, which can propagate from the urinary bladder to other organs in the human body. BLC is the seventh most common cancer around the world, contributing with more than 4% of all deaths caused by cancers. A very good alternative in the battle against this serious disease is the use of chemotherapy. However, for the design of efficient anti-BLC agents, it is necessary to cover a huge space in terms of molecular diversity and complexity. Consequently, chemoinformatics could be a great ally, helping to rationalize the discovery of new and versatile anti-BLC drugs in terms of diminution of financial resources and time. Current computer-aided models are able to predict anti-BLC activity against only one biological target (protein, cancer cell line) by using very limited and homogenous datasets. On the other hand, there is no information related with the possible safety of anti-BLC agents which have been tested. In this chapter, we introduce a multi-tasking (mtk) chemoinformatic model for simultaneous prediction of anti-BLC activity and safety profiles such as ADMET (absorption, distribution, metabolism, elimination, toxicity) properties. At the same time, we provide two useful insights regarding the molecular patterns that can be responsible for the anti-BLC activity and/or ADMET properties. In this sense, we give a physicochemical and/or structural explanation about the molecular descriptors which entered in our mtk-chemoinformatic model, and how a defined biological effect can be enhanced. On the other hand, we show a procedure for the calculation and analysis of quantitative contributions of molecular fragments to the biological effects, which can be a useful guide for experts working in medicinal chemistry and pharmaceutical sciences. © 2014 Nova Science Publishers, Inc.

A QSAR study is reported to predict the binding affinity of a set of 81 modulators for both of human estrogen receptor alpha and beta (ER alpha and ER beta). In this study, the derived QSAR models were built by forward stepwise multilinear regression (MLR) and nonlinear radial basis function neural networks (RBFNN), respectively. The statistical characteristics of the external test set provided by multiple linear model (R-2=0.814, F=61.277, RMS=0.5461 for ER alpha; R-2=0.600, F=21.039, RMS=0.6707 for ER beta) indicated satisfactory stability and predictive ability of the model built. The predictive ability for ER beta of RBFNN model is somewhat superior: R2=0.7691, F=32.012, RMS=0.5764, and the similar result was obtained for ERa of the test set: R2=0.7950, F=54.131 RMS=0.3120. Overall, the appropriate results proved the models to be meaningful and useful to predict and virtual screen of the derivatives with high binding activity.

The theoretical study of Mn(salen) catalysts has been traditionally performed under the assumption that Mn(acacen') (acacen' = 3,3'-(ethane-1,2-diylbis(azanylylidene)) bis(prop-1-en-olate)) is an appropriate surrogate for the larger Mn(salen) complexes. In this work, the geometry and the electronic structure of several Mn(salen) and Mn(acacen') model complexes were studied using Density Functional Theory (DFT) at diverse levels of approximation, with the aim of understanding the effects of truncation, metal oxidation, axial coordination, substitution on the aromatic rings of the salen ligand and chirality of the diimine bridge, as well as the choice of the density functional and basis set. To achieve this goal, geometric and structural data, obtained from these calculations, were subjected to Principal Component Analysis (PCA) and PCA with orthogonal rotation of the components (rPCA). The results show the choice of basis set to be of paramount importance, accounting for up to 30% of the variance in the data, while the differences between salen and acacen' complexes account for about 9% of the variance in the data, and are mostly related to the conformation of the salen/acacen' ligand around the metal centre. Variations in the spin state and oxidation state of the metal centre also account for large fractions of the total variance (up to 10% and 9%, respectively). Other effects, such as the nature of the diimine bridge or the presence of an alkyl substituent in the 3,3 and 5,5 positions of the aldehyde moiety, were found to be less important in terms of explaining the variance within the data set. A matrix of discriminants was compiled using the loadings of the principal and rotated components that best performed in the classification of the entries in the data. The scores obtained from its application to the data set were used as independent variables for devising linear models of different properties, with satisfactory prediction capabilities.

The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure-Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic CD8+) population.