Quantitative Structure Activity Relationship and Molecular Docking of Pim-1 Kinase Inhibitors

QSAR has established itself as an important means of virtual screening. Computational analysis of numerous drug molecules aided the facility to meaningfully screen specific ones for further use and experimentation. 

In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) associated simulations were done and stochastic models were developed considering 55 molecules of hydroxyflavanone, thaizolidene2, 4-dione, pyridone derivatives against proviral insertion site of moloney murine leukemia virus. As part of the study, molecular field analysis (MFA) was done along with receptor surface analysis (RSA). Moreover, the generalization ability of the developed model was rigorously validated using 12 test set molecules.

Bioinformatics offer new avenues for the clinical and Medical data analysis

Genomics and molecular biology has served as a source of inspiration for biology an d biotechnology researchers and these two fields together developed a massive data known as bioinformatics. Bioinformatics data cut the elaborative laboratory costs as the analysis of this offers valuable inputs related to cancer genomics and viral genomics apart from offering next generation sequencing that is capable of analyzing the massive data generated from the medical, biotechnological and clinical research globally. 

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Enhancing Visual Evoked Potentials Detection with Use of Computational Intelligence Tools

The analysis of evoked potentials (EPs) in the electroencephalogram (EEG) is usually inspected visually and demands subjective interpretation of the results. This paper aims at combining an statistical criterion based on the magnitude square multiple coherence (MSMC) estimate with computational intelligence methods in order to estimate the EPs detection rate (DR) using only portions of the frequency spectrum. 

Thus, networks were used to predict the DR in EEG signals of 15 normal subjects during stroboscopic stimulation. The algorithms were designed to receive the spectral information of two, four or six EEG derivations as the input and DR as the output. Our best result shows that the artificial neural networks can estimate DR with correlation coefficient of 0.97 compared with MSMC, even when a reduced amount of spectral information from the data is available.