Designing Novel Antitrypanosomal Agents from a Mixed Graph-Theoretical Substructural Approach

Chagas disease is nowadays the most serious parasitic health problem. This disease is caused by Trypanosoma cruzi. The great number of deaths and the insufficient effectiveness of drugs against this parasite have alarmed the scientific community worldwide. In an attempt to overcome this problem, a model for the design and prediction of new antitrypanosomal agents was obtained. This used a mixed approach, containing simple descriptors based on fragments and topological substructural molecular design descriptors. A data set was made up of 188 compounds, 99 of them characterized an antitrypanosomal activity and 88 compounds that belong to other pharmaceutical categories. The model showed sensitivity, specificity and accuracy values above 85%. Quantitative fragmental contributions were also calculated. Then, and to confirm the quality of the model, 15 structures of molecules tested as antitrypanosomal compounds (that we did not include in this study) were predicted, taking into account the information on the abovementioned calculated fragmental contributions. The model showed an accuracy of 100% which means that the ""in silico"" methodology developed by our team is promising for the rational design of new antitrypanosomal drugs. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 31: 882-894. 2010

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

Quantitative structure-hydrophobicity relationships of molecular fragments and beyond

Quantitative structure-property relationship (QSPR) models were firstly established for the hydrophobic substituent constant (πX) using the theoretical descriptors derived solely from electrostatic potentials (EPSs) at the substituent atoms. The descriptors introduced are found to be related to hydrogen-bond basicity, hydrogen-bond acidity, cavity, or dipolarity/polarizability terms in linear solvation energy relationship, which endows the models good interpretability. The predictive capabilities of the models constructed were also verified by rigorous Monte Carlo cross-validation. Then, eight groups of meta- or para- disubstituted benzenes and one group of substituted pyridines were investigated. QSPR models for individual systems were achieved with the ESP-derived descriptors. Additionally, two QSPR models were also established for Rekker's fragment constants (foct), which is a secondary-treatment quantity and reflects average contribution of the fragment to logP. It has been demonstrated that the descriptors derived from ESPs at the fragments, can be well used to quantitatively express the relationship between fragment structures and their hydrophobic properties, regardless of the attached parent structure or the valence state. Finally, the relations of Hammett σ constant and ESP quantities were explored. It implies that σ and π, which are essential in classic QSAR and represent different type of contributions to biological activities, are also complementary in interaction site.

Artificial neural network modeling of phytoestrogen binding to estrogen receptors

Differential pathophysiological roles of estrogen receptors alpha (ERα) and beta (ERβ) are of particular interest for phytochemical screening. A QSAR incorporating theoretical descriptors was developed in the present study utilizing sequential multiple-output artificial neural networks. Significant steric, constitutional, topological and electronic descriptors were identified enabling ER affinity differentiation.

Theoretical Approximations Between Brazilian and Spanish Authors' Production in the Field of Knowledge Organization in the Production of Journals on Information Science in Brazil

This work identifies and analyzes literature about knowledge organization (KO), expressed in scientific journals communication of information science (IS). It performs an exploratory study on the Base de Dados Referencial de Artigos de Periodicos em Ciência da Informacio (BRAPCI, Reference Database of Journal Articles on Information Science) between the years 2000 and 2010. The descriptors relating to "knowledge organization" are used in order to recover and analyze the corresponding articles and to identify descriptors and concepts which integrate the semantic universe related to KO. Through the analysis of content, based on metrical studies, this article gathers and interprets data relating to documents and authors. Through this, it demonstrates the development of this field and its research fronts according to the observed characteristics, as well as noting the transformation indicative in the production of knowledge. The work describes the influences of the Spanish researchers on Brazilian literature in the fields of knowledge and information organization. As a result, it presents the most cited and productive authors, the theoretical currents which support them, and the most significant relationships of the Spanish-Brazilian authors network. Based on the constant key-words analysis in the cited articles, the co-existence of the French conception current and the incipient Spanish influence in Brazil is observed. Through this, it contributes to the comprehension of the thematic range relating to KO, stimulating both criticism and self-criticism, debate and knowledge creation, based on studies that have been developed and institutionalized in academic contexts in Spain and Brazil.

Synthesis, X-ray Crystal Structure and Theoretical Calculations of Antileishmanial Neolignan Analogues

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Synthesis, X-ray crystal structure and theoretical calculations of antileishmanial neolignan analogues

A síntese e a estrutura cristalina por difração de raios-X de dois análogos de neolignanas, 2-(4-clorofenil)-1-feniletanona (20) e 2-[tio(4-clorofenil)]-1-(3,4-dimetoxifenil)propan-1-ona (12) são descritas. O composto 12 apresenta atividade intracelular contra Leishmania donovani e Leishmania amazonensis de amastigotas que causam a leishmaniose tegumentar e visceral. Além disso, a teoria do funcional de densidade (DFT) com o funcional híbrido B3LYP foi empregado para calcular um conjunto de descritores moleculares para dezenove análogos sintéticos de neolignanas com atividades antileishmaniose. Posteriormente, a análise discriminante stepwise foi realizada para investigar possíveis relações entre a estrutura molecular e atividades biológicas. Por meio dessa análise os compostos foram classificados em dois grupos ativos e inativos de acordo com seu grau de atividade biológica, e as propriedades mais importantes foram as cargas de alguns átomos, a afinidade eletrônica e o ClogP.

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

Thiosemicarbazones are cruzain inhibitors which have been identified as potential antitrypanosomal agents. In this work, several molecular properties were calculated at the density functional theory (DFT)/B3LYP/6-311G* level for a set of 44 thiosemicarbazones. Unsupervised and supervised pattern recognition techniques (hierarchical cluster analysis, principal component analysis, kth-nearest neighbors, and soft independent modeling by class analogy) were used to obtain structureactivity relationship models, which are able to classify unknown compounds according to their activities. The chemometric analyses performed here revealed that 12 descriptors can be considered responsible for the discrimination between high and low activity compounds. Classification models were validated with an external test set, showing that predictive classifications were achieved with the selected variable set. The results obtained here are in good agreement with previous findings from the literature, suggesting that our models can be useful on further investigations on the molecular determinants for the antichagasic activity. (C) 2012 Wiley Periodicals, Inc.