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Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile

Author

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  • Twan van Laarhoven
  • Elena Marchiori

Abstract

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Suggested Citation

  • Twan van Laarhoven & Elena Marchiori, 2013. "Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
  • Handle: RePEc:plo:pone00:0066952
    DOI: 10.1371/journal.pone.0066952
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    Cited by:

    1. Cheng Yan & Jianxin Wang & Wei Lan & Fang-Xiang Wu & Yi Pan, 2017. "SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    2. Hansaim Lim & Aleksandar Poleksic & Yuan Yao & Hanghang Tong & Di He & Luke Zhuang & Patrick Meng & Lei Xie, 2016. "Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-26, October.
    3. Chao Huang & Yang Yang & Xuetong Chen & Chao Wang & Yan Li & Chunli Zheng & Yonghua Wang, 2017. "Large-scale cross-species chemogenomic platform proposes a new drug discovery strategy of veterinary drug from herbal medicines," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-20, September.
    4. Krisztian Buza & Ladislav Peška & Júlia Koller, 2020. "Modified linear regression predicts drug-target interactions accurately," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.
    5. Anna Cichonska & Balaguru Ravikumar & Elina Parri & Sanna Timonen & Tapio Pahikkala & Antti Airola & Krister Wennerberg & Juho Rousu & Tero Aittokallio, 2017. "Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-28, August.
    6. Yong Liu & Min Wu & Chunyan Miao & Peilin Zhao & Xiao-Li Li, 2016. "Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-26, February.
    7. Benoit Playe & Chloé-Agathe Azencott & Véronique Stoven, 2018. "Efficient multi-task chemogenomics for drug specificity prediction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-34, October.

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