Ionic liquids (ILs) possess a unique physicochemical profile providing a wide range of applications. Their almost limitless structural possibilities allow the design of task-specific ILs. However, their greenness, specifically their claimed relative nontoxicity has been frequently questioned, hindering their REACH registration processes and, so, their final application. Because the vast majority of ILs is yet to be synthesized, the development of chemoinformatics tools efficiently profiling their hazardous potential becomes essential. In this work, we introduce a reliable, predictive, simple, and chemically interpretable Classification and Regression Trees (CART) classifier, enabling the prioritization of ILs with a favorable cytotoxicity profile. Besides a good predictive capability (81% or 75% or 83% of accuracy or sensitivity or specificity in an external evaluation set), the other salient feature of the proposed cytotoxicity CART classifier is their simplicity and transparent chemical interpretation based on structural molecular fragments. The essentials of the current structure-cytotoxicity relationships of ILs are faithfully reproduced by this model, supporting its biophysical relevance and the reliability of the resultant predictions. By inspecting the structure of the CART, several moieties that can be regarded as cytotoxicophores were identified and used to establish a set of SAR trends specifically aimed to prioritize low-cytotoxicity ILs. Finally, we demonstrated the suitability of the joint use of the CART classifier and a group fusion similarity search as a virtual screening strategy for the automatic prioritization of safe ILs disperse in a data set of ILs of moderate to very high cytotoxicity.

Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q2 and R2pred respectively. The best 2D-QSAR model was obtained with q2 and R2pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q2 and R2pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q2 and R2pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q2 and R2pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors. © 2013 Elsevier Ltd.

Due to their role in the metabolism of monoamine neurotransmitters, MAO-A and MAO-B present a significant pharmacological interest. For instance the inhibitors of human MAO-B are considered useful tools for the treatment of Parkinson Disease. Therefore, the rational design and synthesis of new MAOs inhibitors is considered of great importance for the development of new and more effective treatments of Parkinson Disease. In this work, Quantitative Structure Activity Relationships (QSAR) has been developed to predict the human MAO inhibitory activity and selectivity. The first step was the selection of a suitable dataset of heterocyclic compounds that include chromones, coumarins, chalcones, thiazolylhydrazones, etc. These compounds were previously synthesized in one of our laboratories, or elsewhere, and their activities measured by the same assays and for the same laboratory staff. Applying linear discriminant analysis to data derived from a variety of molecular representations and feature selection algorithms, reliable QSAR models were built which could be used to predict for test compounds the inhibitory activity and selectivity toward human MAO. This work also showed how several QSAR models can be combined to make better predictions. The final models exhibit significant statistics, interpretability, as well as displaying predictive power on an external validation set made up of chromone derivatives with unknown activity (that are being reported here for first time) synthesized by our group, and coumarins recently reported in the literature.

Macromolecules, such as polyethylene glycol (PEG), have been frequently used in the preparation of xerogels, mainly with the purpose of tuning the meso- or macroporosity. However, PEG has never been applied in the context of the preparation of molecularly imprinted xerogels for small molecules. Thus, we decided to conduct a computational and experimental study of the incorporation of PEG into formerly studied sol-gel mixtures for the preparation of damascenone-imprinted xerogels. Computationally, two types of pregelification models were studied, one representing the initial mixture (SI3/SIPA:S:3 models) and the other representing the same mixtures after considerable solvent loss (SI3/SIPA:40:1 models). The latter ones were particularly prolific in providing clear effects of the PEG. In the SI3:40:1 model (containing SI3 units of Si3O3(OH)(6) mimicking the final xerogels backbone), a prohibitive instead of a promoting effect of PEG on the template-SI3 association was observed. PEG was found to interpose the SI3 aggregates, turning them smaller and more disperse. In agreement with that, a much higher porosity and surface area were found for the corresponding xerogel prepared with PEG, while no appreciable improvement of the imprinting efficiency could be observed. In the SIPA:40:1 model (containing both SI3 and SIPA units; SIPA, Si3O3(OH)(5)C3H6NHC6H5, representing the introduction of the organic functional group into the xerogel network), the interactions related to the network structuring were not significantly affected. This was due to the fact that the SIPA units themselves had a dispersive effect on the silica network; the PEG molecules were "pushed" into the aqueous/methanolic continuum, and their presence was somewhat redundant. Accordingly, both prepared SIPA-xerogels (with PEG or not) exhibited higher porosity compared to SI3-xerogels. Although the simulation results were not conclusive about the effect of PEG on the template-functional group association, experimentally it was clear that the imprinting effect was not improved with PEG.

Although a few groups have recently published transport properties for extensive sets of imidazoliumand pyridinium-based room-temperature ionic liquids (RTILs) and their solutions, there are still no prediction techniques for the conductivity maximum in these systems. We contribute to the discussion by reporting own conductometric data and establishing implicit empirical correlations between ionic structure, concentration and temperature. Our analysis is based on binary systems containing ionic (RTIL) and molecular (acetonitrile) co-solvent. The molar fraction of RTIL in each system ranges from 0 to 50% whereas temperature ranges from 278.15 to 328.15 K. Imidazolium-based RTILs are sampled by 1-ethyl-3-methylimidazolium, 1-butyl-3-methylimidazolium and 1-hexyl-3- methylimidazolium tetrafluoroborates, 1-n-butyl-3- methylimidazolium trifluoromethanesulfonate, and 1-butyl-3-methylimidazolium bromide. 1-butyl-4-methylpyridinium tetrafluoroborate is employed to distinguish a role of aromatic ring. Ionic association in all RTIL-AN systems poorly correlates with the cation structure, although strongly depends on the anion size and structure. Cation and anion of RTILs form the 'contact ion pairs' (CIPs) where anion is coordinated by imidazole and pyridine rings. Notably, all binary systems exhibit conductivity maximum between χ(RTIL) = 10 and 20%. This maximum slightly shifts towards smaller χ(RTIL), as counterion gets larger. Smaller cations and anions lead to substantial conductivity growth. Conductivity maximum can be boosted and observed at larger χ(RTIL) even at insignificant temperature increase. Our observations provide novel insights into a complicated functional dependence of ionic conductivity versus ionic concentration and temperature. The results may be of extensive practical application, particularly for construction of high-performance electrolyte systems. © 2013 Elsevier Ltd. All rights reserved.

This chapter reviews recent applications of density functional theory (DFT) based methods inthe study of the interaction of small gaseous molecules with metal nanoparticles, metalsurfaces, and porous or biological materials and applications in the study of chemicalreactions at catalytic sites of transition metals or enzymes. Focus is given to the interaction ofsmall molecules, e.g. H2O, O2, CO, CO2, etc., with the scaffold atoms of metal organicframeworks (MOF) or with zeolites, in the field of gas adsorption, or with the exposed atomson transition metal surfaces or nanoparticles, in the field of heterogeneous catalysis, and to theinteraction of small organic molecules with the capacity to inhibit a catalytic cysteine of themalaria's parasite, in the field of drug design. The roles of under-coordinated atoms on thestrength of the interaction and of the type of the exchange-correlation functional consideredfor the calculations are analyzed. Finally, recent successes of the consideration of DFT basedapproaches to study, with atomic detail, the reactions of such molecules on these materials arealso reviewed.

Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides own unique action mechanisms making peptide antibiotics an attractive therapeutic option against resistant bacteria. However, their high haemolytic activity lacks the selectivity required for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess greater selectivity for bacterial cells over erythrocytes. In this article, we introduce a chemoinformatics approach to simultaneously deal with these two conflicting properties consisting on a multi-criteria virtual screening strategy based on the use of a desirability-based multi-criteria classifier combined with similarity and chemometrics concepts. Here we propose a new quantitative feature encoding information related to the desirability, the degree of credibility ascribed to this desirability and the similarity of a candidate to a highly desirable query, which can be used as ranking criterion in a virtual screening campaign, the Desirability-Credibility-Similarity (DCS) Score. The enrichment ability of a multi-criteria virtual screening strategy based on the use of the DCS Score it is also assessed and compared to other virtual screening options. The results obtained evidenced that the use of the DCS score seems to be an efficient virtual screening strategy rendering promising overall and initial enrichment performance. Specifically, by using the DCS score it was possible to rank a selective antibacterial peptidomimetic earlier than a biologically inactive or non selective antibacterial peptidomimetic with a probability of ca. 0.9.