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Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

Author

Listed:
  • Anna Cichonska
  • Balaguru Ravikumar
  • Elina Parri
  • Sanna Timonen
  • Tapio Pahikkala
  • Antti Airola
  • Krister Wennerberg
  • Juho Rousu
  • Tero Aittokallio

Abstract

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005678
    DOI: 10.1371/journal.pcbi.1005678
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    References listed on IDEAS

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    1. 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.
    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. 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.
    4. Guha, Rajarshi, 2007. "Chemical Informatics Functionality in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i05).
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    Cited by:

    1. Ryan Theisen & Tianduanyi Wang & Balaguru Ravikumar & Rayees Rahman & Anna Cichońska, 2024. "Leveraging multiple data types for improved compound-kinase bioactivity prediction," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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