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DR2DI: a powerful computational tool for predicting novel drug-disease associations

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Abstract

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

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Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, NanTong Institute of Technology, Nantong, China

    Lu Lu

  2. State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Hua Yu

  3. University of Chinese Academy of Sciences, Beijing, China

    Hua Yu

  4. Institute of Reproductive Medicine, Nantong Medical College and School of Pharmacy, Nantong University, Nantong, China

    Hua Yu

Authors
  1. Lu Lu
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  2. Hua Yu
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Contributions

LL and HY conceived the original research plans, analyzed the data and wrote the article; HY and LL supervised the experiments and revised the paper.

Corresponding authors

Correspondence to Lu Lu or Hua Yu.

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The authors declare no competing financial interests.

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Lu, L., Yu, H. DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 32, 633–642 (2018). https://doi.org/10.1007/s10822-018-0117-y

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  • DOI: https://doi.org/10.1007/s10822-018-0117-y

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