ISCMF: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction

  • Narjes Rohani
  • Changiz EslahchiEmail author
  • Ali Katanforoush
Original Article


Drug–drug interaction (DDI) prediction prepares substantial information for drug discovery. As the exact prediction of DDIs can reduce human health risk, the development of an accurate method to solve this problem is quite significant. Despite numerous studies in the field, a considerable number of DDIs are not yet identified. In the current study, we used Integrated Similarity-constrained matrix factorization (ISCMF) to predict DDIs. Eight similarities were calculated based on the drug substructure, targets, side effects, off-label side effects, pathways, transporters, enzymes, and indication data as well as Gaussian interaction profile for the drug pairs. Subsequently, a non-linear similarity fusion method was used to integrate multiple similarities and make them more informative. Finally, we employed ISCMF, which projects drugs in the interaction space into a low-rank space to obtain new insights into DDIs. However, all parts of ISCMF have been proposed in previous studies, but our novelty is applying them in DDI prediction context and combining them. We compared ISCMF with several state-of-the-art methods. The results show that It achieved more appropriate results in five-fold cross-validation. It improves AUPR, and F-measure to 10% and 18%, respectively. For further validation, we performed case studies on numerous interactions predicted by ISCMF with high probability, most of which were validated by reliable databases. Our results provide support for the notion that ISCMF might be used unequivocally as a powerful method for predicting the unknown DDIs. The data and implementation of ISCMF are available at


Drug–drug interaction Matrix factorization Drug similarity Similarity integration 



All authors thank Fatemeh Ahmadi Moughari for her helpful comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13721_2019_215_MOESM1_ESM.pdf (101 kb)
Supplementary material 1 (PDF 101 kb)
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Supplementary material 2 (PDF 61 kb)
13721_2019_215_MOESM3_ESM.pdf (402 kb)
Supplementary material 3 (PDF 403 kb)


  1. Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, Grimm S, Kao J, King SP, Miwa G, Ni L et al (2003) The conduct of in vitro and in vivo drug-drug interaction studies: a pharmaceutical research and manufacturers of america (phrma) perspective. Drug metabolism and disposition 31(7):815–832CrossRefGoogle Scholar
  2. CYP2C9 C, CYP2D6 C (2007) The effect of cytochrome p450 metabolism on drug response, interactions, and adverse effects. Am Fam Phys 76:391–396Google Scholar
  3. Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R (2012) Indi: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol 8(1):592CrossRefGoogle Scholar
  4. Hanton G (2007) Preclinical cardiac safety assessment of drugs. Drugs R & D 8(4):213–228CrossRefGoogle Scholar
  5. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2009) Kegg for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38(suppl–1):D355–D360Google Scholar
  6. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V et al (2010) Drugbank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39(suppl–1):D1035–D1041Google Scholar
  7. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37CrossRefGoogle Scholar
  8. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6(1):343CrossRefGoogle Scholar
  9. Kusuhara H (2014) How far should we go? perspective of drug-drug interaction studies in drug development. Drug Metab Pharmacokinet 29(3):227–228CrossRefGoogle Scholar
  10. van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21):3036–3043CrossRefGoogle Scholar
  11. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V et al (2013) Drugbank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(D1):D1091–D1097CrossRefGoogle Scholar
  12. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15):1200–1205CrossRefGoogle Scholar
  13. Li Q, Cheng T, Wang Y, Bryant SH (2010) Pubchem as a public resource for drug discovery. Drug Discov Today 15(23–24):1052–1057CrossRefGoogle Scholar
  14. Magnus D, Rodgers S, Avery A (2002) Gps’ views on computerized drug interaction alerts: questionnaire survey. J Clin Pharm Ther 27(5):377–382CrossRefGoogle Scholar
  15. Menon AK, Elkan C (2011) Link prediction via matrix factorization. Joint European conference on machine learning and knowledge discovery in databases. Springer, New York, pp 437–452CrossRefGoogle Scholar
  16. Olayan RS, Ashoor H, Bajic VB (2017) Ddr: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics 34(7):1164–1173CrossRefGoogle Scholar
  17. Percha B, Altman RB (2013) Informatics confronts drug–drug interactions. Trends Pharmacol Sci 34(3):178–184CrossRefGoogle Scholar
  18. Prueksaritanont T, Chu X, Gibson C, Cui D, Yee KL, Ballard J, Cabalu T, Hochman J (2013) Drug–drug interaction studies: regulatory guidance and an industry perspective. AAPS J 15(3):629–645CrossRefGoogle Scholar
  19. Rohani N, Eslahchi C (2019) Drug–drug interaction predicting by neural network using integrated similarity. Sci Rep 9(1):1–11CrossRefGoogle Scholar
  20. Stražar M, Žitnik M, Zupan B, Ule J, Curk T (2016) Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins. Bioinformatics 32(10):1527–1535CrossRefGoogle Scholar
  21. Tatonetti NP, Patrick PY, Daneshjou R, Altman RB (2012) Data-driven prediction of drug effects and interactions. Sci Transl Med 4(125):125ra31CrossRefGoogle Scholar
  22. Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C (2012) Drug-drug interaction through molecular structure similarity analysis. J Am Med Inform Assoc 19(6):1066–1074CrossRefGoogle Scholar
  23. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11(3):333CrossRefGoogle Scholar
  24. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH (2009) Pubchem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37(suppl–2):W623–W633CrossRefGoogle Scholar
  25. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2007) Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(suppl–1):D901–D906Google Scholar
  26. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34(suppl–1):D668–D672CrossRefGoogle Scholar
  27. Zhang P, Wang F, Hu J, Sorrentino R (2015) Label propagation prediction of drug-drug interactions based on clinical side effects. Sci Rep 5:12339CrossRefGoogle Scholar
  28. Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X (2017) Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform 18(1):18CrossRefGoogle Scholar
  29. Zhang W, Yue X, Lin W, Wu W, Liu R, Huang F, Liu F (2018) Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinform 19(1):233CrossRefGoogle Scholar
  30. Zhang Y, Chen M, Huang D, Wu D, Li Y (2017) idoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener Comput Syst 66:30–35CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Computer Sciences, Faculty of Mathematical SciencesShahid-Beheshti University, GCTehranIran
  2. 2.School of biological sciencesInstitute for research in fundamental sciences (IPM)TehranIran

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