6 research outputs found

    Thermal Decomposition of Diphenyl Tetroxane in Chlorobenzene Solution

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    The thermal decomposition of Cyclic Diperoxide of Benzaldehyde 3,6-diphenyl-1,2,4,5-tetroxane, (DFT) in chlorobenzene solution in the studied temperature range (130°C - 166°C) satisfactorily satisfies a first order law up to 60% conversions of diperoxide. DFT would decompose through a mechanism in stages and initiated by the homolytic breakdown of one of the peroxidic bonds of the molecule, with the formation of the corresponding intermediate biradical. The concentration studied was very low, so that the effects of secondary reactions of decomposition induced by free radicals originated in the reaction medium can be considered minimal or negligible. The activation parameters for the unimolecular thermal decomposition reaction of the DFT are ΔH# = 30.52 ± 0.3 kcal·mol-1 and ΔS# = -6.38 ± 0.6 cal·mol-1 K-1. The support for a step-by-step mechanism instead of a process concerted is made by comparison with the theoretically calculated activation energy for the thermal decomposition of 1,2,4,5-tetroxane.Fil: Bordón, Alexander Germán. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaFil: Pila, Andrea Natalia. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: Profeta, Mariela Inés. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaFil: Jorge, María J.. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaFil: Jorge, Lilian Cristina. Universidad Nacional del Nordeste. Facultad de Ciencias Veterinarias; ArgentinaFil: Romero, Jorge Marcelo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaFil: Jorge, Nelly Lidia. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentin

    Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm

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    Multivariate image analysis quantitative structure-activity relationship (MIA-QSAR) study aims to obtain information from a descriptor set, which are image pixels of two-dimensional molecule structures. In the QSAR study of protein P38 mitogen-activated protein (MAP) kinase compounds, the genetic algorithm application for pixel selection and image processing is investigated. There is a quantitative relationship between the structure and the pIC50 based on the information obtained. (The pIC50 is the negative logarithm of the half-maximal inhibitory concentration ( IC50 ), so pIC50 = −log IC50 .) Protein P38 MAP kinase inhibitors are used in the treatment of malignant tumors. The development of a model to predict the pIC50 of these compounds was performed in this study. To accomplish this, the molecules were first plotted and fixed in the same coordinates in ChemSketch. Then, the images were processed in the MATLAB program. Partial least squares (PLS) model, orthogonal signal correction partial least squares (OSC-PLS) model, and genetic algorithm partial least squares (GA-PLS) model methods are used to generate quantitative models, and pIC50 prediction is performed. The GA-PLS model has the highest predictive power for a series of statistical parameters such as root mean square error of prediction (RMSEP) and relative standard errors of prediction (RSEP). Finally, the molecular junction (docking) was done for predicted molecules in quantitative structure activity relationship (QSAR) with an appropriate receptor and acceptable results were obtained. These results are good and proper for the prediction of compounds with better properties

    Renewable Energy Consumption, Environmental Sustainability, and Economic Growth in Developing Countries

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    This study investigated the nexus between renewable energy consumption and economic growth in developing countries and the role of environmental sustainability in the nexus between renewable energy consumption and economic growth. To achieve the objective, the researcher employed General Method of Moment (GMM) to solve for the possible problem of endogeneity common in previous studies using data sourced from World Bank. It was discovered that renewable energy consumption has positive but weak impact on economic growth in developing countries but when environmental sustainability is accounted, the impact of renewable energy consumption improves. Similarly, interacting CO2 with renewable energy changes the sign of CO2 from positive to negative. The implication of this study is that renewable energy consumption impacts on the economy might be weak but is justified giving its environmental sustainability potential

    Evaluation of Similarity Measures for Ligand-Based Virtual Screening

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    Enhancing Reaction-based de novo Design using Machine Learning

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    De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources
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