Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

Autoři: Aanchal Mongia aff001;  Angshul Majumdar aff002
Působiště autorů: Dept. of Computer Science and Engineering, IIIT-Delhi, Delhi, India aff001;  Dept. of Electronics and Communications Engineering, IIIT-Delhi, Delhi, India aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226484


The identification of potential interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification. In this work, we propose a new framework, namely, Multi-Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and target proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively. Extensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin. Software is publicly available at https://github.com/aanchalMongia/MGRNNMforDTI.

Klíčová slova:

Algorithms – Cosine similarity – Drug discovery – Drug information – Drug-drug interactions – Protein interactions – Structural genomics


1. Dai YF, Zhao XM. A survey on the computational approaches to identify drug targets in the postgenomic era. BioMed research international. 2015;2015. doi: 10.1155/2015/239654

2. Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Briefings in bioinformatics. 2018.

3. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research. 2011;40(D1):D1100–D1107. doi: 10.1093/nar/gkr777 21948594

4. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic acids research. 2007;36(suppl_1):D901–D906. doi: 10.1093/nar/gkm958 18048412

5. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic acids research. 2011;40(D1):D109–D114. doi: 10.1093/nar/gkr988 22080510

6. Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, Jensen LJ, et al. STITCH 4: integration of protein–chemical interactions with user data. Nucleic acids research. 2013;42(D1):D401–D407. doi: 10.1093/nar/gkt1207 24293645

7. Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic acids research. 2007;36(suppl_1):D919–D922. doi: 10.1093/nar/gkm862 17942422

8. Masoudi-Nejad A, Mousavian Z, Bozorgmehr JH. Drug-target and disease networks: polypharmacology in the post-genomic era. In silico pharmacology. 2013;1(1):17. 25505661

9. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nature reviews Drug discovery. 2004;3(8):673. doi: 10.1038/nrd1468 15286734

10. Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS computational biology. 2012;8(5):e1002503. doi: 10.1371/journal.pcbi.1002503 22589709

11. McLean SR, Gana-Weisz M, Hartzoulakis B, Frow R, Whelan J, Selwood D, et al. Imatinib binding and cKIT inhibition is abrogated by the cKIT kinase domain I missense mutation Val654Ala. Molecular cancer therapeutics. 2005;4(12):2008–2015. doi: 10.1158/1535-7163.MCT-05-0070 16373716

12. Frantz S. Drug discovery: playing dirty; 2005.

13. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nature biotechnology. 2007;25(2):197. doi: 10.1038/nbt1284 17287757

14. Johnson A, Wiley-Interscience MM. Concepts and Applications of Molecular Similarity. Edited; 1991.

15. Xie L, Evangelidis T, Xie L, Bourne PE. Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir. PLoS computational biology. 2011;7(4):e1002037. doi: 10.1371/journal.pcbi.1002037 21552547

16. Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, et al. TarFisDock: a web server for identifying drug targets with docking approach. Nucleic acids research. 2006;34(suppl_2):W219–W224. doi: 10.1093/nar/gkl114 16844997

17. Pujadas G, Vaque M, Ardevol A, Blade C, Salvado M, Blay M, et al. Protein-ligand docking: A review of recent advances and future perspectives. Current Pharmaceutical Analysis. 2008;4(1):1–19. doi: 10.2174/157341208783497597

18. Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, et al. Structure-based maximal affinity model predicts small-molecule druggability. Nature biotechnology. 2007;25(1):71. doi: 10.1038/nbt1273 17211405

19. Nagamine N, Shirakawa T, Minato Y, Torii K, Kobayashi H, Imoto M, et al. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLoS computational biology. 2009;5(6):e1000397. doi: 10.1371/journal.pcbi.1000397 19503826

20. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM; 2001. p. 285–295.

21. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24(13):i232–i240. doi: 10.1093/bioinformatics/btn162 18586719

22. Shi JY, Yiu SM. SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2015. p. 1636–1641.

23. van Laarhoven T, Marchiori E. Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PloS one. 2013;8(6):e66952. doi: 10.1371/journal.pone.0066952 23840562

24. Shi JY, Li JX, Lu HM, Zhang Y. Predicting Drug-Target Interactions Between New Drugs and New Targets via Pairwise K-nearest Neighbor and Automatic Similarity Selection. In: He X, Gao X, Zhang Y, Zhou ZH, Liu ZY, Fu B, et al., editors. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Cham: Springer International Publishing; 2015. p. 477–486.

25. Bleakley K, Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics. 2009;25(18):2397–2403. doi: 10.1093/bioinformatics/btp433 19605421

26. van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics. 2011;27(21):3036–3043. doi: 10.1093/bioinformatics/btr500 21893517

27. Mei JP, Kwoh CK, Yang P, Li XL, Zheng J. Drug–target interaction prediction by learning from local information and neighbors. Bioinformatics. 2012;29(2):238–245. doi: 10.1093/bioinformatics/bts670 23162055

28. He Z, Zhang J, Shi XH, Hu LL, Kong X, Cai YD, et al. Predicting drug-target interaction networks based on functional groups and biological features. PloS one. 2010;5(3):e9603. doi: 10.1371/journal.pone.0009603 20300175

29. Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PloS one. 2012;7(5):e37608. doi: 10.1371/journal.pone.0037608 22666371

30. Chen H, Zhang Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PloS one. 2013;8(5):e62975. doi: 10.1371/journal.pone.0062975 23667553

31. Shi JY, Li JX, Lu HM. Predicting existing targets for new drugs base on strategies for missing interactions. BMC bioinformatics. 2016;17(8):282. doi: 10.1186/s12859-016-1118-2 27585458

32. Shi JY, Liu Z, Yu H, Li YJ. Predicting drug-target interactions via within-score and between-score. BioMed research international. 2015;2015. doi: 10.1155/2015/350983

33. Shi JY, Yiu SM, Li Y, Leung HC, Chin FY. Predicting drug–target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods. 2015;83:98–104. doi: 10.1016/j.ymeth.2015.04.036 25957673

34. Chen X, Liu MX, Yan GY. Drug–target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems. 2012;8(7):1970–1978. doi: 10.1039/c2mb00002d 22538619

35. Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;(8):30–37. doi: 10.1109/MC.2009.263

36. Cobanoglu MC, Liu C, Hu F, Oltvai ZN, Bahar I. Predicting drug–target interactions using probabilistic matrix factorization. Journal of chemical information and modeling. 2013;53(12):3399–3409. doi: 10.1021/ci400219z 24289468

37. Ezzat A, Zhao P, Wu M, Li XL, Kwoh CK. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2017;14(3):646–656. doi: 10.1109/TCBB.2016.2530062

38. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401(6755):788. doi: 10.1038/44565 10548103

39. Candès EJ, Tao T. The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory. 2010;56(5):2053–2080. doi: 10.1109/TIT.2010.2044061

40. Candès EJ, Recht B. Exact matrix completion via convex optimization. Foundations of Computational mathematics. 2009;9(6):717. doi: 10.1007/s10208-009-9045-5

41. Recht B. A simpler approach to matrix completion. Journal of Machine Learning Research. 2011;12(Dec):3413–3430.

42. Wang M, Tang C, Chen J. Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion. BioMed Research International. 2018;2018. doi: 10.1155/2018/1425608

43. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic acids research. 2006;34(suppl_1):D354–D357. doi: 10.1093/nar/gkj102 16381885

44. Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, et al. BRENDA, the enzyme database: updates and major new developments. Nucleic acids research. 2004;32(suppl_1):D431–D433. doi: 10.1093/nar/gkh081 14681450

45. Hattori M, Tanaka N, Kanehisa M, Goto S. SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic acids research. 2010;38(suppl_2):W652–W656. doi: 10.1093/nar/gkq367 20460463

46. AMoZ J. Identification of Common Molecular Subsequences.

47. Majumdar A, Ward RK. Some empirical advances in matrix completion. Signal Processing. 2011;91(5):1334–1338. doi: 10.1016/j.sigpro.2010.12.005

48. Chung FR. Spectral graph theory (CBMS regional conference series in mathematics, No. 92). 1996.

49. Combettes PL, Pesquet JC. Proximal splitting methods in signal processing. In: Fixed-point algorithms for inverse problems in science and engineering. Springer; 2011. p. 185–212.

50. Nishihara R, Lessard L, Recht B, Packard A, Jordan MI. A general analysis of the convergence of ADMM. arXiv preprint arXiv:150202009. 2015.

51. Boyd S. Alternating direction method of multipliers. In: Talk at NIPS workshop on optimization and machine learning; 2011.

52. Kirrinnis P. Fast algorithms for the Sylvester equation AX- XBT = C. Theoretical Computer Science. 2001;259(1-2):623–638. doi: 10.1016/S0304-3975(00)00322-4

53. Zheng X, Ding H, Mamitsuka H, Zhu S. 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; 2013. p. 1025–1033.

54. Liu Y, Wu M, Miao C, Zhao P, Li XL. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS computational biology. 2016;12(2):e1004760. doi: 10.1371/journal.pcbi.1004760 26872142

55. Shi JY, Zhang AQ, Zhang SW, Mao KT, Yiu SM. A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization. BMC systems biology. 2018;12(9):136. doi: 10.1186/s12918-018-0663-x 30598094

56. Kalofolias V, Bresson X, Bronstein M, Vandergheynst P. Matrix completion on graphs. arXiv preprint arXiv:14081717. 2014.

57. Belkin M, Niyogi P, Sindhwani V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of machine learning research. 2006;7(Nov):2399–2434.

58. Pahikkala T, Airola A, Pietilä S, Shakyawar S, Szwajda A, Tang J, et al. Toward more realistic drug–target interaction predictions. Briefings in bioinformatics. 2014;16(2):325–337. doi: 10.1093/bib/bbu010 24723570

59. Albert I, Albert R. Conserved network motifs allow protein–protein interaction prediction. Bioinformatics. 2004;20(18):3346–3352. doi: 10.1093/bioinformatics/bth402 15247093

60. Alkan C, Karakoc E, Nadeau JH, Sahinalp SC, Zhang K. RNA–RNA interaction prediction and antisense RNA target search. Journal of Computational Biology. 2006;13(2):267–282. doi: 10.1089/cmb.2006.13.267 16597239

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