In this paper, we apply novel intrinsic analysis methods, coupled with bivariate orientation analysis, to obtain a detailed picture of the molecular-level structure of ionic liquid surfaces. We observe pronounced layering at the interface, alternating non-polar with ionic regions. The outermost regions of the surface are populated by alkyl chains, which are followed by a dense and tightly packed layer formed of oppositely charged ionic moieties. We then systematically change the cation chain length, the anion size, the temperature and the molecular model, to examine the effect of each of these parameters on the interfacial structure. Increasing the cation chain length promotes orientations in which the chain is pointing into the vapor, thus increasing the coverage of the surface with alkyl groups. Larger anions promote a disruption of the dense ionic layer, increasing the orientational freedom of cations and increasing the amount of free space. The temperature had a relatively small effect on the surface structure, while the effect of the choice of molecular model was clearly significant, particularly on the orientational preferences at the interface. Our study demonstrates the usefulness of molecular simulation methods in the design of ionic liquids to suit particular applications.

The discovery of anti-cancer agents is an area which continues in accelerated expansion. Leukemias (Lkms) are among the most investigated cancers due to its high and dominant prevalence in children. Computer-aided drug design (CADD) methodologies have been extremely important for the discovery of potent anti-Lkms agents, providing essential insights about the molecular patterns which could be involved in the appearance and development of anti-Lkms activity. The present review is focused on the role of the current CADD methodologies for the discovery of anti-Lkms agents with strong emphasis on the in silico prediction of inhibitors against the primary protein associated with the appearance of Lkms: Abelson tyrosine-protein kinase 1 (TPK-ABL1). In order to make a contribution to the field, we also developed a unified ligand-based approach by exploring Quantitative-Structure Activity Relationships (QSAR) studies. Here, we focused on the construction of two multi-targets (mt) QSAR models by employing a large and heterogeneous database of compounds. These models exhibited excellent statistical quality and predictive power to classifying more than 92% of inhibitors/no inhibitors against seven proteins associated with Lkms, in both training and prediction sets. By using our unified ligand-based approach we identified several fragments as responsible for the anti-Lkms activity through inhibition of proteins, and new molecules were suggested as versatile inhibitors of the seven proteins under study.

We apply a new methodology in the force field generation (Phys. Chem. Chem. Phys. 2011, 13, 7910) to study binary mixtures of five imidazolium-based room-temperature ionic liquids (RTILs) with acetonitrile (ACN). Each RTIL is composed of tetrafluoroborate (BF4) anion and dialkylimidazolium (MMIM) cations. The first alkyl group of MIM is methyl, and the other group is ethyl (EMIM), butyl (BMIM), hexyl (HMIM), octyl (OMIM), and decyl (DMIM). Upon addition of ACN, the ionic conductivity of RTILs increases by more than 50 times. It significantly exceeds an impact of most known solvents. Unexpectedly, long-tailed imidazolium cations demonstrate the sharpest conductivity boost. This finding motivates us to revisit an application of RTIL/ACN binary systems as advanced electrolyte solutions. The conductivity correlates with a composition of ion aggregates simplifying its predictability. Addition of ACN exponentially increases diffusion and decreases viscosity of the RTIL/ACN mixtures. Large amounts of ACN stabilize ion pairs, although they ruin greater ion aggregates.

Quantitative Structure-Activity Relationships (QSAR) modeling tools play acritical role today in both drug design and environmental sciences. QSAR modeling seeksto discover and use mathematical relationships between molecular properties of thecompounds (descriptors) and the often complex activity of interest. An extensive numberof molecular descriptors exist which can and have been used to model a wide range oftarget activities. This complicates the task of selecting those that will be more suitable,especially when one tries to define an accurate, robust, predictive and (most importantly)interpretable model. Lately, recognition of the importance of the three-dimensionally (3D)structure and stereochemistry of molecules to their biological activity, and awareness of thelimitations of classical approaches, led to many attempts to generate 3D descriptors eitheras a complement for 2D-QSAR models or for standalone 3D-QSAR models. This reviewdescribes the 3D descriptors available in the DRAGON software along with theirsuccessful applications primarily in Medicinal Chemistry, updating a previously publishedpaper in Current Topics in Medicinal Chemistry (Helguera, A.M.; Combes, R.D.;González, M.P.; Cordeiro, M.N.D.S., 2008, 8, 1628-1655).

The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents.

A brain tumor (BT) constitutes a neoplasm located in the brain or the central spinal canal. The number of new diagnosed cases with BT increases with the pass of the time. Understanding the biology of BT is essential for the development of novel therapeutic strategies, in order to prevent or deal with this disease. An active area for the search of new anti-BT therapies is the use of Chemoinformatics and/or Bioinformatics toward the design of new and potent anti-BT agents. The principal limitation of all these approaches is that they consider small series of structurally related compounds and/or the studies are realized for only one target like protein. The present work is an effort to overcome this problem. We introduce here the first Chemoinformatics multi-target approach for the in silico design and prediction of anti-BT agents against several cell lines. Here, a fragment-based QSAR model was developed. The model correctly classified 89.63% and 90.93% of active and inactive compounds respectively, in training series. The validation of the model was carried out by using prediction series which showed 88.00% of correct classification for active and 88.59% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BT activity were calculated. Several fragments were identified as potential substructural features responsible of anti-BT activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-BT agents.

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which fortyfour 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity. © 2012 Bentham Science Publishers.