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Monday, December 6, 2010

Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus

In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead structure. We have shown that the multilayer perceptron-based model is capable of determining the QSAR quite satisfactorily, with high R-value. Usually, the design of potent ARIs requires the use of complex computer docking and quantum mechanical (QM) steps involving excessive time and human judgement. In the second part of this article, to reduce the design cycle of potent ARIs, we propose a novel ANN technique to eliminate the computer docking and QM steps, to predict the total score. The MLP-based QSAR models obtained in the first part are used to predict the potent ARIs, using the experimental data reported by Hu et al. (J Mol Graph Mod 2006, 24, 244). The proposed ANN-based model can predict the total score with an R-value of 0.88, which indicates that there exists a close match between the predicted and experimental total scores. Using the ANN model, we obtained 71 potent ARIs out of 6.25 million new ARI compounds created by substituting different functional groups at substituting sites of main lead structure of known ARI. Finally, using high bioactivity relationship and total score values, we determined four potential ARIs out of these 71 compounds. Interestingly, these four ARIs include the two potent ARIs reported by Hu et al. (J Mol Graph Mod 2006, 24, 244) who obtained these through the complex computer docking and QM steps. This fact indicates the effectiveness of our proposed ANN-based technique. We suggest these four compounds to be the most promising candidates for ARIs to prevent the diabetic complications and further recommend for wet bench experiments to find their potential against AR in vitro and in vivo. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 http://onlinelibrary.wiley.com/doi/10.1002/jcc.21240/abstract

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Life Science and Informatics

What is this?
is this a new industry?
or a old wine in a new bottle?

Well Life Sciences and Informatics can be anything form computational biology, all omes and omics, core bioinformatics to curation and literature mining, database creation, in the area of biology, chemistry , bio-chem space.

There are number of companies in India and bangalore is the forefront as a major bio-cluster with 20 to 30 companies in this sphere.

now how good are these companies doing?
how good are they in terms of the international markets and how profitable is their business?
what do they do?
their clients?

These are some interesting things that could be discussed in this blog page...

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