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Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

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Listed:
  • Dong-Sheng Cao
  • Yi-Zeng Liang
  • Zhe Deng
  • Qian-Nan Hu
  • Min He
  • Qing-Song Xu
  • Guang-Hua Zhou
  • Liu-Xia Zhang
  • Zi-xin Deng
  • Shao Liu

Abstract

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

Suggested Citation

  • Dong-Sheng Cao & Yi-Zeng Liang & Zhe Deng & Qian-Nan Hu & Min He & Qing-Song Xu & Guang-Hua Zhou & Liu-Xia Zhang & Zi-xin Deng & Shao Liu, 2013. "Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-12, April.
  • Handle: RePEc:plo:pone00:0057680
    DOI: 10.1371/journal.pone.0057680
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    References listed on IDEAS

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    1. Michael J. Keiser & Vincent Setola & John J. Irwin & Christian Laggner & Atheir I. Abbas & Sandra J. Hufeisen & Niels H. Jensen & Michael B. Kuijer & Roberto C. Matos & Thuy B. Tran & Ryan Whaley & Ri, 2009. "Predicting new molecular targets for known drugs," Nature, Nature, vol. 462(7270), pages 175-181, November.
    2. Gerard J P van Westen & Jörg K Wegner & Peggy Geluykens & Leen Kwanten & Inge Vereycken & Anik Peeters & Adriaan P IJzerman & Herman W T van Vlijmen & Andreas Bender, 2011. "Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-13, November.
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