ABSTRACT
In the present study, a generalized approach for molecular structure characterization is introduced, based on the relation frequency matrix (F) representation of the molecular graph and the subsequent calculation of the corresponding discrete derivative (finite difference) over a pair of elements (atoms). In earlier publications (22- 24), an unique event, named connected subgraphs, (based on the Kier-Hall's subgraphs) was systematically employed for the computation of the matrix F. The present report is a generalization of this notion, in which eleven additional events are introduced, classified in three categories, namely, topological (terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs), fingerprints (MACCs, E-state and substructure fingerprints) and atomic contributions (Ghose and Crippen atom-types for hydrophobicity and refractivity) for F generation. The events are intended to capture diverse information by the generation or search of different kinds of substructures from the graph representation of a molecule. The discrete derivative over duplex atom relations are calculated for each event, and the resulting derivatives, local vertex invariants (LOVIs) are finally obtained. These LOVIs are subsequently employed as the basis for the calculation of global and local indices over groups of atoms (heteroatoms, halogens, methyl carbons, etc.), by using norms, means, statistics and classical algorithms as aggregator (fusion) operators. These indices were implemented in our house software DIVATI (Derivative Type Indices, a new module of TOMOCOMDCARDD system). DIVATI provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http: //www.tomocomd.com. Factor analysis shows that the presented events are rather orthogonal and collect diverse information about the chemical structure. Finally, QSPR models were built to describe the logP and logK of 34 furylethylenes derivatives using the eleven events. Generally, the equations obtained according to these events showed high correlations, with the Sach's sub-graphs and Multiplicity events showing the best behavior in the description of logK (Q2 LOO value of 99.06%) and logP (Q2 LOO value of 98.1 %), respectively. These results show that these new eventbased indices constitute a powerful approach for chemoinformatics studies.
Subject(s)
Algorithms , Furans/chemistry , Models, Chemical , SoftwareABSTRACT
In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.
Subject(s)
Blood-Brain Barrier/physiology , Computer Simulation , Models, Cardiovascular , Models, Neurological , Animals , HumansABSTRACT
Para los estudiantes de ciencias de la salud, el conocimiento de la biología constituye un pilar fundamental para afrontar con éxito los problemas que se les presenten relacionados con este campo del conocimiento y para interpretar los fenómenos concretos de las ciencias médicas y de la salud. Este artículo muestra los resultados del uso de los mapas conceptuales para el aprendizaje significativo de conceptos de biología celular. Objetivo: Determinar la efectividad de los mapas conceptuales como estrategia didáctica en el aprendizaje de conceptos de biología celular en estudiantes de ciencias de la salud. Materiales y métodos. Se trabajó un diseño cuasi experimental pretest postest con dos grupos intactos: un grupo experimental (usó los mapas conceptuales como estrategia de aprendizaje) y un grupo control (no usó los mapas conceptuales como estrategia de aprendizaje). Los estudiantes fueron evaluados con preguntas de selección múltiple con única respuesta, en los niveles de conocimiento, comprensión y aplicación del dominio cognitivo de la Taxonomía de Bloom. Resultados: En el postest no se hallaron diferencias significativas en el total de preguntas. Sin embargo, se encontraron diferencias significativas entre los grupos en el nivel de aplicación, y se observó mejor resultado en el grupo experimental. Conclusiones: Los mapas conceptuales proporcionaron a los estudiantes una herramienta para aprender significativamente conceptos de biología celular. Sin embargo, su uso efectivo depende de factores como: motivación por aprender, entrenamiento continuo en la elaboración de mapas, contenidos significativos de aprendizaje y tiempo de implementación...
For students of health sciences, knowledge of biology is a cornerstone to successfully face the challenges presented to them related to this field of knowledge and to interpret specific phenomena of medical science and health. This paper shows the results of the use of the concept maps for meaningful learning of cellular biology concepts. Objective: to determine the effectiveness of the concept maps as a didactic strategy in the learning of concepts of cellular Biology in students of Health Sciences. Material and methods: It was worked with a cuasi-experimental design of pre-test and post-test with two intact groups: an experimental group (it used the concept maps as learning strategy) and a control group (it did not use the concept maps as learning strategy). The students were evaluated using a pre-test and post-test, built with multiple-choice questions with one answer in the levels of knowledge, comprehension and analysis of the cognitive domain of Blooms Taxonomy. Results: In the post-test found no significant differences in the total number of questions. However significant differences were found between groups in the application level, observing better results in the experimental group. Conclusions: The concept maps gave students a tool to learn concepts of cell biology significantly. However, its effective use depends on factors such as motivation for learning, continuous training in mapping, significant contents of learning and implementation time...
