Chloroquine resistance is nowadays a great problem. Aurone derivatives are effective against chloroquine resistant parasite. Ligand based validated comparative chemometric modeling through 2D-QSAR and kNN-MFA 3D-QSAR studies as well as common feature 3D pharmacophore mapping were done on thirtyfive aurone derivatives having antimalarial activity. Statistically significant 2D-QSAR models were generated on unsplitted as well as splitted dataset by MLR and PLS technique. The MLR model of the unsplitted method was validated by two-deep cross validation and 10 fold cross validation for determining the predictive ability. The PLS technique of the unsplitted method was done to compare the significance of these methods. In the splitted method, model was developed on the training set by Y-based ranking method by using the same descriptors and was validated on fifty pairs of the test and the training sets by k-MCA technique. These models generated by using the same descriptors were well validated irrespective of MLR as well as PLS analysis of unsplitted as well as splitted methods and are showing similar results. Therefore, these descriptors and model generated were reliable and robust. The kNN-MFA 3D-QSAR models were generated by three variable selection methods: genetic algorithm, simulated annealing and stepwise regression. The kNN-MFA 3D-QSAR results support the 2D QSAR data and in turn validate the earlier observed SAR results. Common feature 3D-pharmacophore generation was performed on these compounds to validate both 2D and 3D-QSAR studies as well as the earlier observed SAR data. The work highlights the required structural features for the higher antimalarial activity. © 2013 Bentham Science Publishers.

Experimental studies show that copper complexes can be effectively anchored onto the pores of mesoporous solids, having a good catalytic performance in several reactions, among them the aziridination of olefins and in particular, styrene. In this work, the mechanism of the aziridination of styrene catalyzed by a bis(oxazoline) copper(I) complex was studied in detail by means of density functional theory (DFT) calculations. For such reactions in the homogeneous phase, our calculations revealed a wide diversity of reaction-pathways, which have not been considered in previous studies, and should be taken into account due to the small energy differences between them. What is more, our results show that there is a strong dependence on the chosen DFT functional. This has profound implications on the way the heterogeneous reaction is studied. (c) 2013 Wiley Periodicals, Inc.

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values > 80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy = 90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.

Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD treatments is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD, constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitative-structure-activity relationships (QSAR). The principal purpose was the in silico design of multi-target (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt-QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins.

Tuberculosis (TB) constitutes one of the most dangerous and serious health problems around the world. It is a very lethal disease caused by microorganisms of the genus mycobacterium, principally Mycobacterium tuberculosis (MTB) which affects humans. A very active field for the search of more efficient anti-TB chemotherapies is the use in silico methodologies for the discovery of potent anti-TB agents. The battle against MTB by using antimicrobial chemotherapies will depend on the design of new chemicals with high anti-TB activity and low toxicity as possible. Multi-target methodologies focused on quantitative-structure activity relationships (mt-QSAR) have played a very important role for the rationalization of drug design, providing a better understanding about the molecular patterns related with diverse pharmacological profiles including antimicrobial activity. Nowadays, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for the simultaneous prediction of anti-TB activity and toxicity against Mus musculus and Rattus norvegicus. The mtk-QSBER model was created by using linear discriminant analysis (LDA) for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under many experimental conditions. Our mtk-QSBER model, correctly classified more than 90% of the case in the whole database (more than 12,000 cases), serving as a powerful tool for the computer-assisted screening of potent and safe anti-TB drugs.

Environmental contaminants are frequently encountered as mixtures, and research on mixture toxicity is a hot topic until now. In the present study, the mixture toxicity of non-polar narcotic chemical was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNNs) from molecular descriptors that are calculated and be defined as composite descriptors according to the fractional concentrations of the mixture components. The statistical parameters provided by the MLR model were R-2 = 0.9512, RMS = 0.3792, F = 1402.214 and LOOq(2) = 0.9462 for the training set, and R-2 = 0.9453, RMS = 03458, F = 276.671 and q(ext)(2) = 0.9450 for the external test set. The RBFNN model gave the following statistical results, namely: R-2 = 0.9779, RMS = 0.2498, F = 3188.202 and LOOq(2) = 0.9746 for the training set, and R-2 = 0.9763, RMS = 0.2358, F = 660.631 and q(ext)(2) = 0.9745, for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction mixture toxicity of non-polar narcotic chemicals. The RBFNN model gave even improved results. In addition, epsilon(LUMO+1) (the energy of the second lowest unoccupied molecular orbital) and PPSA (total charge weighted partial positively surface area) were found to have high correlation with the mixture toxicity.

Malaria has been known as one of the major causes of morbidity and mortality on a large scale in tropical countries until now. In the past decades, many scientific groups have focused their attention on looking for ideal drugs to this disease. So far, this research area is still a hot topic. In the present study, the antimalarial activity of 1, 4-naphthoquinonyl derivatives was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNN). The derived QSAR models have been statistically validated both internally - by means of the Leave One Out (LOO) and Leave Many Out (LMO) cross-validation, and Y-scrambling techniques, as well as externally (by means of an external test set). The statistical parameters provided by the MLR model were R-2 =0.7876, LOOq(2) =0.7068, RMS =0.3377, R-0(2) =0.7876, k = 1.0000 for the training set, and R-2 =0.7648, q(ext)(2) =0.7597, RMS=0.2556, R-0(2)=0.7598, k=1.0417 for the external test set. The RBFNN model gave the following statistical results, namely: R-2=0.8338, LOOq(2)=0.5869, RMS=0.2781, R-0(2) = 0.8335, k=1.0000 for the training set, and R-2 =0.7586, q(ext)(2) =0.7189, RMS=0.2788, R-0(2)=0.7129, k=1.0284 for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction and virtual screening of 1, 4-naphoquinone derivatives with high antimalarial activity. In addition, the energies of the highest occupied molecular orbital were found to have high correlation with the activity.

Adenosine receptors (ARs) are signaling molecules ubiquitously expressed in a wide variety of tissues in the human body. ARs mediate physiological functions by interacting with four subtypes of G-protein-coupled receptors, namely A(1), A(2A), A(2B) and A(3). The A(3) AR, probably the most studied subtype, is also ubiquitously expressed, with high levels in peripheral organs and low levels in the brain. This type of AR is involved in a variety of important pathophysiological processes, ranging from modulation of cerebral and cardiac ischemic damage to regulation of immunosuppression and inflammation. Consequently, the development of potent and selective A(3) AR ligands as promising therapeutic options for a variety of diseases has been a prime subject of medicinal chemistry research for more than two decades. Among the plethora of approaches applied quantitative structure activity relationships (QSAR) stands out for being largely employed due to their potential to increase the efficiency at initial stages of the drug discovery process. So, we provide a review of the main QSAR studies devoted to the design, discovery and development of agonist and antagonist A(3) adenosine receptor ligands. Common pitfalls of these QSAR applications and the current trends in this area are also analyzed.