Dyes have been applied and are playing an increasingly important role in many industries, including the textile, printing, medical and energy industries. Their wide applications imply that specific dyes possessing given properties need to be effectively designed. The present review aims to survey information related to activity/property research of dyes that has been published in the past two decades. Emphasis is laid particularly on studies based on quantitative structureactivity/property relationships that have contributed to the theoretical design and application of dyes. Finally, the perspectives of quantitative structure-activity/property relationship studies are set out in order to show how this method may be used to design new dyes and to evaluate their different properties. The challenges facing these studies are also outlined.

In this paper we review and at the same time investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability around the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function and the second order moments. We have studied and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. These stochastic models are studied from stability point of view and they were graphically represented using the second order Euler scheme and Maple 12 software.

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

In recent years, numerous changes have been made in the field of cancer research with the progresses of molecular biology, chemoinformatics and chemogenomics. Several new biomolecular targets have been identified, and investigated for new drug discovery. In the current article, we discuss the role of pharmacophore mapping and pharmacophore-based virtual screening (PBVS) approaches for identification of novel anticancer hits. It showed that pharmacophore-based studies were performed for almost every type of anticancer agents. However, such applications are clustered on finding novel hits for a few targets like cancer-related hormones, kinase enzymes and other less investigated targets. Some reports were found with virtual hits experimentally validated against respective targets. These were thoroughly described and the novel hits were pointed out. Others with PBVS of anticancer targets were also discussed and the identified features were highlighted. Present review showed that PBVS may serve as a true lead generator if it is performed in a unified fashion that combines in silico techniques with experimental validation. With enormous progresses in computational methods as well as molecular biology, it is expected that pharmacophore-based drug discovery strategy will aid in significant upsurge in the field of cancer chemotherapy in near future. © 2013 Bentham Science Publishers.

In this work we present a fundamental study of the electrodeposition of tin from Deep Eutectic Solvents (DES) formed by a mixture of choline chloride and different hydrogen bond donors (HBD). Results shows that choline chloride based solvents can be successfully used for the electrodeposition of tin. Furthermore we demonstrate that the choice of hydrogen bond donor does not affect, significantly, the chemistry of tin in solution and we characterize the first stages of tin deposits at glassy-carbon (GC) electrode. The electrochemical characterization of tin deposits is carried out using cyclic voltammetry and chronoamperometry. The comparison of the theoretically and experimentally obtained current transients via dimensionless plots based on Bewick-Fleischman-Thirsk (BFT) theory, Scharifker and Hills (SH) and Scharifker and Mostany (SM) models and a non-linear fitting method showed that tin nucleation on GC surface occurs though a 3D instantaneous process with growth controlled by diffusion.

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure-Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H2O2. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection = 23.4% in assay (1) and protection = 15.2% in assay (2); and for compound 7a with protection = 46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays,. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.

Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative-Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.