Neglected diseases are infections that thrive mainly among underdeveloped countries, particularly those belonging to regions found in Asia, Africa, and America. One of the most complex diseases is noma, a dangerous health condition characterized by a polymicrobial and opportunistic nature. The search for potent and safer antibacterial agents against this disease is therefore a goal of particular interest. Chemoinformatics can be used to rationalize the discovery of drug candidates, diminishing time and financial resources. However, in the case of noma, there is no in silico model available for its use in the discovery of efficacious antibacterial agents. This work is devoted to report the first mtk-QSBER model, which integrates dissimilar kinds of chemical and biological data. The model was generated with the aim of simultaneously predicting activity against bacteria present in noma, and ADMET (absorption, distribution, metabolism, elimination, toxicity) parameters. The mtk-QSBER model was constructed by employing a large and heterogeneous dataset of chemicals and displayed accuracies higher than 90% in both training and prediction sets. We confirmed the practical applicability of the model by predicting multiple profiles of the investigational antibacterial drug delafloxacin, and the predictions converged with the experimental reports. To date, this is the first model focused on the virtual search for desirable anti-noma agents.

In this concise review paper, we will address recent studies based on the generalized-gradient approximation (GGA) of the density functional theory (DFT) and on the periodic slab approach devoted to the understanding of the Fischer-Tropsch synthesis process on transition metal catalysts. As it will be seen, this computational combination arises as a very adequate strategy for the study of the reaction mechanisms on transition metal surfaces under well-controlled conditions and allows separating the influence of different parameters, e.g., catalyst surface morphology and coverage, influence of co-adsorbates, among others, in the global catalytic processes. In fact, the computational studies can now compete with research employing modern experimental techniques since very efficient parallel computer codes and powerful computers enable the investigation of more realistic molecular systems in terms of size and composition and to explore the complexity of the potential energy surfaces connecting reactants, to intermediates, to products of reaction. In the case of the Fischer-Tropsch process, the calculations were used to complement experimental work and to clarify the reaction mechanisms on different catalyst models, as well as the influence of additional components and co-adsorbate species in catalyst activity and selectivity.

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 degrees C at 5 mu M, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Carbon monoxide (CO) methanation has been studied through periodic density functional theory calculations on flat and corrugated nickel surfaces. The effect of doping the catalyst was taken into account by impregnating the nickel surfaces with Rh or Ru atoms. It was found that the methanation of CO as well as the synthesis of methanol from CO and hydrogen (H-2) evolve through the formyl (HCO) intermediate on all the surfaces considered. The formation of this intermediate is the most energy-consuming step on all surface models with the exception of the Rh- and Ru-doped Ni(110) surfaces. In the methanation reaction, the CO dissociation is assisted by hydrogen atoms and it is the rate-determining step. Also, surfaces displaying low-coordinated atoms are more reactive than flat surfaces for the dissociative reaction steps. The reaction route proposed for the formation of methanol from CO and H-2 presents activation energy barrier maxima similar to those of CO methanation on pure nickel and Rh- or Ru-doped flat nickel surfaces. However, the CO methanation reaction is more likely than the methanol formation on the doped stepped nickel surfaces, which is in agreement with experimental results available in the literature. Thus, the different behavior found for these two reactions on the corrugated doped surfaces can then be used in the optimization of Ni-based catalysts favoring the formation of methane over methanol.

Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies. We used different experimental protocols to determine the mitoprotective activity (%P) of different carbon nanotubes (CNT) against mitochondrial swelling in multiple boundary conditions (bj). The experimental boundary conditions explored included different sub-sets of combinations of the following factors b0 = three different mitochondrial swelling assays using the MPT-inductor (Ca2+, Fe3+, H2O2) combined or not with a second MPT-inductor and swelling control assays using MPT-inhibitor (CsA, RR, EGTA), b1 = exposure time (0-600 s), and b2 = CNT concentrations (0-5 μg ml-1). Other boundary conditions (bk) changed were the CNT structural parameters b3 = CNT type (SW, SW + DW, MW), b4 = CNT functionalization type (H, OH, COOH). We also changed different of CNT like b5 = molecular weight/functionalization ratio (minW/maxW) or b6 = maximal and minimal diameter (Dmin/Dmax) as physic-chemical properties (Vk). Next, we employed chemoinformatics ideas to develop a new Perturbation Theory (PT) model able to predict the %P of CNT in multiple experimental conditions. We investigated different output functions of the absorbance ′f(εij) used in PL4/PL5 methods like (εij, 1/εij, 1/εij2, or -log εij) as alternative outputs of the model. The inputs are in the form an additive functions with linear/non-linear terms. The first term is a function 0f(〈εij〉) of the average absorbance 〈εij〉 (expected value) in different assays (bj). The concentration dependent terms are linear functions of concentration, or hill-shaped curves similar to PL4/PL5 functions (used in dose-response analysis). The CNT structure perturbation terms are linear/non-linear functions of Box-Jenkins operators (ΔVkj). The ΔVkj are moving averages (deviations) of the Vk of the CNT with respect to their expected values 〈Vkj〉. The best model found predicted the values of absorbance (measure of mitoprotective activity vs. mitochondrial swelling) with regression coefficient R2 = 0.997 for >6000 experimental data points (q2 = 0.994). Last, we used the model to carry out a simulation of the changes on mitoprotective activity for CNT family after one increase of 1-10% of the minWi and maxDi of CNT. © The Royal Society of Chemistry.