Alignment-Free Prediction of a Drug−Target Complex Network Based on Parameters of Drug Connectivity and Protein Sequence of Receptors
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- 作者：Dolores Via ; Eugenio Uriarte ; Francisco Orallo ; Humberto Gonzlez-Daz
- 刊名：Molecular Pharmaceutics
- 出版年：2009
- 出版时间：June 1, 2009
- 年：2009
- 卷：6
- 期：3
- 页码：825-835
- 全文大小：472K
- 年卷期：v.6,no.3(June 1, 2009)
- ISSN：1543-8392
文摘
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 (DRPs), 1754 edges or pairs of DRPs with similar drug−target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 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.