Journal
MOLECULAR PHARMACEUTICS
Volume 6, Issue 3, Pages 825-835Publisher
AMER CHEMICAL SOC
DOI: 10.1021/mp800102c
Keywords
Drug-target complex networks; drug-receptor interaction; multitarget quantitative structure-activity relationships (mt-QSAR); molecular descriptor; Markov model; complex networks
Funding
- Isidro Pal a Pondal Programme of the Direccion Xeral de Investigacion e Desenvolvemento, Xunta de Galicia
- Conselleria de Educacion e Ordenacion Universitaria de la Xunta de Galicia [SUG 2007-2008]
- Direccion Xeral de I+D+I, Xunta de Galicia [INCITE08PXIB203022PR]
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There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRIPS), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRIPS, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.
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