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A Computational Bipartite Graph-Based Drug Repurposing Method

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Computational Methods for Drug Repurposing

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1903))

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Abstract

We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs. In addition, we summarized some alternative strategies and variations follow-up to each section in the overall analysis.

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References

  1. Walters WP, Green J, Weiss JR, Murcko MA (2011) What do medicinal chemists actually make? A 50-year retrospective. J Med Chem 54(19):6405–6416

    Article  CAS  PubMed  Google Scholar 

  2. Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P (2013) Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 93(4):335–341

    Article  CAS  PubMed  Google Scholar 

  3. Dickson M, Gagnon JP (2004) The cost of new drug discovery and development. Discov Med 4(22):172–179

    PubMed  Google Scholar 

  4. Bolgár B, Arany Á, Temesi G, Balogh B, Antal P, Mátyus P (2013) Drug repositioning for treatment of movement disorders: from serendipity to rational discovery strategies. Curr Top Med Chem 13(18):2337–2363

    Article  PubMed  Google Scholar 

  5. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175–181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Von EJ, Murgueitio MS, Dunkel M, Koerner S, Bourne PE, Preissner R (2011) PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res 39(Database issue):D1060

    Google Scholar 

  7. Chong CR, Sullivan DJ (2007) New uses for old drugs. Nature 448(7154):645–646

    Article  CAS  PubMed  Google Scholar 

  8. Liu Z, Fang H, Reagan K, Xu X, Mendrick DL, Jr WS, Tong W (2013) In silico drug repositioning–what we need to know. Drug Discov Today 18(3–4):110–115

    Article  CAS  PubMed  Google Scholar 

  9. Oprea TI, Mestres J (2012) Drug repurposing: far beyond new targets for old drugs. AAPS J 14(4):759–763

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jin G, Wong STC (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19(5):637–644

    Article  PubMed  Google Scholar 

  11. Cheng FX, Zhao ZM (2014) Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc 21(E2):E278–E286. https://doi.org/10.1136/amiajnl-2013-002512

    Article  PubMed  PubMed Central  Google Scholar 

  12. Brown AS, Patel CJ (2017) MeSHDD: literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc 24(3):614–618. https://doi.org/10.1093/jamia/ocw142

    Article  PubMed  Google Scholar 

  13. Udrescu L, Sbarcea L, Topirceanu A, Iovanovici A, Kurunczi L, Bogdan P, Udrescu M (2016) Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing. Sci Rep 6:32745. https://doi.org/10.1038/srep32745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vilar S, Hripcsak G (2017) The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions. Brief Bioinform 18(4):670–681. https://doi.org/10.1093/bib/bbw048

    Article  PubMed  Google Scholar 

  15. Ye H, Liu Q, Wei J (2014) Construction of drug network based on side effects and its application for drug repositioning. PLoS One 9(2):e87864. https://doi.org/10.1371/journal.pone.0087864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Huang H, Nguyen T, Ibrahim S, Shantharam S, Yue Z, Chen JY (2015) DMAP: a connectivity map database to enable identification of novel drug repositioning candidates. BMC Bioinformatics 16(Suppl 13):S4. https://doi.org/10.1186/1471-2105-16-S13-S4

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wen M, Zhang ZM, Niu SY, Sha HZ, Yang RH, Yun YH, Lu HM (2017) Deep-learning-based drug-target interaction prediction. J Proteome Res 16(4):1401–1409. https://doi.org/10.1021/acs.jproteome.6b00618

    Article  CAS  PubMed  Google Scholar 

  18. Huang LC, Soysal E, Zheng W, Zhao Z, Xu H, Sun J (2015) A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case. BMC Syst Biol 9(Suppl 4):S2. https://doi.org/10.1186/1752-0509-9-S4-S2

    Article  PubMed  PubMed Central  Google Scholar 

  19. Le DH, Nguyen-Ngoc D (2018) Drug repositioning by integrating known disease-gene and drug-target associations in a semi-supervised learning model. Acta Biotheor. https://doi.org/10.1007/s10441-018-9325-z

    Article  PubMed  Google Scholar 

  20. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J (2017) A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 8(1):573. https://doi.org/10.1038/s41467-017-00680-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li L, He X, Borgwardt K (2018) Multi-target drug repositioning by bipartite block-wise sparse multi-task learning. BMC Syst Biol 12(Suppl 4):55. https://doi.org/10.1186/s12918-018-0569-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lu L, Yu H (2018) DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 32(5):633–642. https://doi.org/10.1007/s10822-018-0117-y

    Article  CAS  PubMed  Google Scholar 

  23. Wu G, Liu J, Wang C (2017) Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration. BMC Med Genet 10(Suppl 5):79. https://doi.org/10.1186/s12920-017-0311-0

    Article  CAS  Google Scholar 

  24. Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z (2017) LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning. Bioinformatics 33(8):1187–1196. https://doi.org/10.1093/bioinformatics/btw770

    Article  CAS  PubMed  Google Scholar 

  25. Zhang P, Wang F, Hu J (2014) Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA Annu Symp Proc 2014:1258–1267

    PubMed  PubMed Central  Google Scholar 

  26. Chen H, Zhang Z (2015) A miRNA-driven inference model to construct potential drug-disease associations for drug repositioning. Biomed Res Int 2015:406463. https://doi.org/10.1155/2015/406463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082. https://doi.org/10.1093/nar/gkx1037

    Article  CAS  PubMed  Google Scholar 

  28. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–D361. https://doi.org/10.1093/nar/gkw1092

    Article  CAS  PubMed  Google Scholar 

  29. Hecker N, Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, Gilson MK, Bourne PE, Preissner R (2012) SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res 40(Database issue):D1113–D1117. https://doi.org/10.1093/nar/gkr912

    Article  CAS  PubMed  Google Scholar 

  30. Davis AP, King BL, Mockus S, Murphy CG, Saraceni-Richards C, Rosenstein M, Wiegers T, Mattingly CJ (2011) The comparative toxicogenomics database: update 2011. Nucleic Acids Res 39(Database):D1067–D1072. https://doi.org/10.1093/nar/gkq813

    Article  CAS  PubMed  Google Scholar 

  31. Barbarino JM, Whirl-Carrillo M, Altman RB, Klein TE (2018) PharmGKB: a worldwide resource for pharmacogenomic information. Wiley Interdiscip Rev Syst Biol Med 10(4):e1417. https://doi.org/10.1002/wsbm.1417

    Article  PubMed  PubMed Central  Google Scholar 

  32. Burley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S (2017) Protein data bank (PDB): the single global macromolecular structure archive. Methods Mol Biol 1607:627–641. https://doi.org/10.1007/978-1-4939-7000-1_26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Leaman R, Wei CH, Lu Z (2015) tmChem: a high performance approach for chemical named entity recognition and normalization. J Cheminform 7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S3. https://doi.org/10.1186/1758-2946-7-S1-S3

    Article  PubMed  PubMed Central  Google Scholar 

  34. Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, Zhang Y, Li S, Yang F, Sun Q, Qin C, Zeng X, Chen Z, Chen YZ, Zhu F (2018) Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 46(D1):D1121–D1127. https://doi.org/10.1093/nar/gkx1076

    Article  CAS  PubMed  Google Scholar 

  35. Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A (2015) OMIM.org: online Mendelian inheritance in man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res 43(Database issue):D789–D798. https://doi.org/10.1093/nar/gku1205

    Article  CAS  PubMed  Google Scholar 

  36. Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(Database issue):D267–D270. https://doi.org/10.1093/nar/gkh061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. UniProt Consortium T (2018) UniProt: the universal protein knowledgebase. Nucleic Acids Res 46(5):2699. https://doi.org/10.1093/nar/gky092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. https://doi.org/10.1093/nar/gku1003

    Article  CAS  PubMed  Google Scholar 

  39. Rogers DJ, Tanimoto TT (1960) A computer program for classifying plants. Science 132(3434):1115–1118. https://doi.org/10.1126/science.132.3434.1115

    Article  CAS  PubMed  Google Scholar 

  40. Liu X, Zhu F, Ma XH, Shi Z, Yang SY, Wei YQ, Chen YZ (2013) Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. Curr Med Chem 20(13):1646–1661

    Article  CAS  PubMed  Google Scholar 

  41. Bolton EE, Kim S, Bryant SH (2011) PubChem3D: similar conformers. J Cheminform 3:13. https://doi.org/10.1186/1758-2946-3-13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kim S, Bolton EE, Bryant SH (2016) Similar compounds versus similar conformers: complementarity between PubChem 2-D and 3-D neighboring sets. J Cheminform 8:62. https://doi.org/10.1186/s13321-016-0163-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hattori M, Okuno Y, Goto S, Kanehisa M (2003) Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc 125(39):11853–11865. https://doi.org/10.1021/ja036030u

    Article  CAS  PubMed  Google Scholar 

  44. King MD, Long T, Pfalmer DL, Andersen TL, McDougal OM (2018) SPIDR: small-molecule peptide-influenced drug repurposing. BMC Bioinformatics 19(1):138. https://doi.org/10.1186/s12859-018-2153-y

    Article  PubMed  PubMed Central  Google Scholar 

  45. Skinnider MA, Dejong CA, Franczak BC, McNicholas PD, Magarvey NA (2017) Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. J Cheminform 9(1):46. https://doi.org/10.1186/s13321-017-0234-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM (2016) Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 11(3):225–239. https://doi.org/10.1517/17460441.2016.1146250

    Article  CAS  PubMed  Google Scholar 

  47. DeSantis TZ, Keller K, Karaoz U, Alekseyenko AV, Singh NN, Brodie EL, Pei Z, Andersen GL, Larsen N (2011) Simrank: rapid and sensitive general-purpose k-mer search tool. BMC Ecol 11:11. https://doi.org/10.1186/1472-6785-11-11

    Article  PubMed  PubMed Central  Google Scholar 

  48. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147(1):195–197

    Article  CAS  PubMed  Google Scholar 

  49. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13):i232–i240. https://doi.org/10.1093/bioinformatics/btn162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25(10):1119–1126. https://doi.org/10.1038/nbt1338

    Article  CAS  PubMed  Google Scholar 

  51. Phatak SS, Zhang S (2013) A novel multi-modal drug repurposing approach for identification of potent ACK1 inhibitors. Pac Symp Biocomput:29–40

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 81601573), the National Key Research and Development Program of China (Grant No. 2016YFC0901901), the Key Laboratory of Medical Information Intelligent Technology Chinese Academy of Medical Sciences, the National Population and Health Scientific Data Sharing Program of China, and the Knowledge Centre for Engineering Sciences and Technology (Medical Centre).

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Authors and Affiliations

  1. Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China

    Si Zheng, Hetong Ma, Jiayang Wang & Jiao Li

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  1. Si Zheng
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  2. Hetong Ma
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  3. Jiayang Wang
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  4. Jiao Li
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Corresponding author

Correspondence to Jiao Li .

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  1. Insilico Medicine, Inc., Rockville, MD, USA

    Quentin Vanhaelen

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Zheng, S., Ma, H., Wang, J., Li, J. (2019). A Computational Bipartite Graph-Based Drug Repurposing Method. In: Vanhaelen, Q. (eds) Computational Methods for Drug Repurposing. Methods in Molecular Biology, vol 1903. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8955-3_7

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  • DOI: https://doi.org/10.1007/978-1-4939-8955-3_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8954-6

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