Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A(2A) receptor (A(2A)AR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/(A2)AAR antagonists (caffeine derivatives and benzothiazinones). Methods: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/(A2)AAR dual binding scaffold. Results: The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAOB/ (A2)AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/(A2)AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches. Conclusion: The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAOB inhibitors and A(2A)AR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson 's disease.

Background: Preeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis. Methods: We firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways. Results: The consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism. Conclusion: Our results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.

In this work, we introduce the first multitasking model for quantitative structure-biological effect relationships focused on the simultaneous exploration of antibacterial activity against Gram-negative pathogens and in vitro safety profiles related to absorption, distribution, metabolism, elimination, and toxicity (ADMET). The multitasking model for quantitative structure-biological effect relationships was created from a data set containing 46,229 cases, and it exhibited accuracy higher than 97% in both training and prediction (test) sets. Several molecular fragments present in the compounds of the data set were selected, and their contributions to multiple biological effects were calculated, providing useful insights toward the detection of 2D pharmacophores, toxicophores, etc. Here, we used a fragment-based philosophy known as puzzle approach, where different fragments with positive contributions against all the biological effects (antibacterial activity and ADMET properties) were assembled as pieces of a puzzle, leading to the creation of six new molecules. Such assembly was dictated by the physicochemical interpretations of the different molecular descriptors of the model. The new molecules were predicted to exhibit potent activity against Gram-negative bacteria, and desirable ADMET properties. The druglikeness of these new molecules was in agreement with the Lipinski's rule of five, making them promising candidates for future biological testing in the framework of collaborative drug discovery.

The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73-98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2. © 2017 The Author(s).

In order to better understand the epoxide-formation step of the Sharpless epoxidation process, a set of 263 oxygen-transfer reactions reflecting the complexity of the Sharpless epoxidation process were studied using density functional theory (DFT) and Bader's quantum theory of atoms in molecules (QTAIM). The diversity within these reactions reflects the different ligands in the coordination sphere of vanadium and also different substrates (alkene and an allylic alcohol both free and in the form of an alcoxo ligand). The transition states for 76 of these reactions were also characterized using DFT and QTAIM, allowing for an estimation of the impact of the different ligands and substrates on the activation barriers. A smaller subset of the latter was further subjected to an ab initio molecular dynamics (AIMD) simulation coupled to QTAIM analysis. The results show that the type of active catalyst plays an important role in the thermodinamic outcome of these reactions, with vanadium(V) tert-butylhydroperoxide adducts being responsible for the most exoenergetic reactions. On the other hand, the different ligands tested play only a limited role in modulating the thermodynamics and kinetics of these reactions. Moreover, no evidence was found to support a thermodynamic or kinetic preference for the epoxidation of an allylic alcohol over that of an unfunctionalized alkene. However, the results suggest that the reaction path is strongly influenced by the orientation of the substrate upon approximation to the active catalyst, confirming the well-known regioselectivity of the Sharpless epoxidation process.

The molecular and supramolecular structure and energetics of tetracene and rubrene were investigated by a combined experimental and theoretical study. Accurate equilibrium vapour pressures at various temperatures were measured for both compounds. For rubrene the energetic analysis evidences lower crystal packing efficiency, strong molecular destabilization and confirms the non-planar twisted equilibrium structure in the gas phase. The results also indicate that phenyl internal rotation in rubrene is highly hindered. The intra- and intermolecular interactions in crystal tetracene and rubrene were evaluated by computational methods. The representative stacked dimer of the tetracene...tetracene interaction in rubrene has lower ionization energy than the one modelling the same interaction in tetracene, due to stronger cation...pi interactions in the cation-radical of rubrene. Charge distribution in the cation dimer is symmetrical in rubrene, whereas in tetracene it is largely localized on the C-H center dot center dot center dot pi donor partner of the herringbone dimer. These findings highlight the impact of cation...pi interactions on the semiconducting properties of OSCs.