DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank

  • Jieyao Deng
  • Qingjun Yuan
  • Hiroshi Mamitsuka
  • Shanfeng ZhuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1807)


Identifying drug-target interactions is crucial for the success of drug discovery. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. By utilizing the “Learning to rank” framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at

Key words

DrugE-Rank Learning to rank Drug discovery 



This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868, and FiDiPro by Tekes.


  1. 1.
    Keiser MJ, Setola V, Irwin JJ et al (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175–181CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Lounkine E, Keiser MJ, Whitebread S et al (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403):361–367CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Nunez S, Venhorst J, Kruse CG (2012) Target-drug interactions: first principles and their application to drug discovery. Drug Discov Today 17:10–22CrossRefPubMedGoogle Scholar
  4. 4.
    Ding H, Takigawa I, Mamitsuka H, Zhu S (2014) Similarity-based machine learning methods for predicting drug–target interactions: a brief review. Brief Bioinform 15(5):734–747CrossRefPubMedGoogle Scholar
  5. 5.
    Zheng X, Ding H, Mamitsuka H, Zhu S (2013) Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1025–1033Google Scholar
  6. 6.
    Takigawa I, Mamitsuka H (2013) Graph mining: procedure, application to drug discovery and recent advance. Drug Discov Today 18(1–2):50–57CrossRefPubMedGoogle Scholar
  7. 7.
    Liu T (2009) Learning to rank for information retrieval. Found Trends Inf Retr 3(3):225–331CrossRefGoogle Scholar
  8. 8.
    Li H (2011) A short introduction to learning to rank. IEICE Transactions 94-D(10):1854–1862CrossRefGoogle Scholar
  9. 9.
    Yuan Q, Gao J, Wu D et al (2016) DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics 32(12):i18–i27CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V et al (2014) Drugbank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(D1):D1091–D1097CrossRefGoogle Scholar
  11. 11.
    Bleakley K, Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18):2397–2403CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Van LT, Marchiori E (2013) Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS One 8(6):e66952CrossRefGoogle Scholar
  13. 13.
    Van LT, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21):3036–3043CrossRefGoogle Scholar
  14. 14.
    Xia Z, Zhou X, Sun Y, Wu L (2009) Semi-supervised drug-protein interaction prediction from heterogeneous spaces. In: The Third International Symposium on Optimization and Systems Biology, vol 11. pp 123–131Google Scholar
  15. 15.
    Rao H, Zhu F, Yang G, Li Z, Chen Y (2011) Update of profeat: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 39(Suppl 2):W385–W390CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jieyao Deng
    • 1
    • 2
  • Qingjun Yuan
    • 1
    • 2
  • Hiroshi Mamitsuka
    • 3
    • 4
  • Shanfeng Zhu
    • 1
    • 2
    • 5
    Email author
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  3. 3.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan
  4. 4.Department of Computer ScienceAalto UniversityEspooFinland
  5. 5.Center for Computational System BiologyFudan UniversityShanghaiChina

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