Abstract
Drug-target interactions map patterns, associations and relationships between drugs and target proteins. Identifying interactions between drug and target is critical in drug discovery, but biochemically validating these interactions are both laborious and expensive. In this paper, we propose a novel interaction profiles based method to predict potential drug-target interactions by using matrix completion. Our method first arranges the drug-target interactions in a matrix, whose entries include interaction pairs, non-interaction pairs and undetermined pairs, and finds its approximation matrix which contains the predicted values at undetermined positions. Then our method learns an approximation matrix by minimizing the distance between the drug-target interaction matrix and its approximation subject that the values in the observed positions equal to the known interactions at the corresponding positions. As a consequence, our method can directly predict new potential interactions according to the high values at the undetermined positions. We evaluated our method by comparing against five counterpart methods on “gold standard” datasets. Our method outperforms the counterparts, and achieves high AUC and \(F_1\)-score on enzyme, ion channel, GPCR, nuclear receptor and integrated datasets, respectively. We showed the intelligibility of our method by validating some predicted interactions in both DrugBank and KEGG databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hopkins, A.L.: Drug discovery: predicting promiscuity. Nature 462(7270), 167–168 (2009)
Cai, J.-F., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)
Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673–683 (2004)
Dudley, J.T., Deshpande, T., Butte, A.J.: Exploiting drug-disease relationships for computational drug repositioning. Briefings Bioinform. 12(4), 303–311 (2011)
Swamidass, S.J.: Mining small-molecule screens to repurpose drugs. Briefings Bioinform. 12(4), 327–335 (2011)
Moriaud, F., Richard, S.B., Adcock, S.A., Chanas-Martin, L., Surgand, J.-S., Jelloul, M.B., Delfaud, F.: Identify drug repurposing candidates by mining the protein data bank. Briefings Bioinform. 12(4), 336–340 (2011)
Whitebread, S., Hamon, J., Bojanic, D., Urban, L.: Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today 10(21), 1421–1433 (2005)
Haggarty, S.J., Koeller, K.M., Wong, J.C., Butcher, R.A., Schreiber, S.L.: Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. Chem. Biol. 10(5), 383–396 (2003)
Kuruvilla, F.G., Shamji, A.F., Sternson, S.M., Hergenrother, P.J., Schreiber, S.L.: Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 416(6881), 653–657 (2002)
Manly, C.J., Louise-May, S., Hammer, J.D.: The impact of informatics and computational chemistry on synthesis and screening. Drug Discov. Today 6(21), 1101–1110 (2001)
Cheng, A.C., Coleman, R.G., Smyth, K.T., Cao, Q., Soulard, P., Caffrey, D.R., Salzberg, A.C., Huang, E.S.: Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25(1), 71–75 (2007)
Rarey, M., Kramer, B., Lengauer, T., Klebe, G.: A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261(3), 470–489 (1996)
Shoichet, B.K., Kuntz, I.D., Bodian, D.L.: Molecular docking using shape descriptors. J. Comput. Chem. 13(3), 380–397 (1992)
Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins Struct. Funct. Bioinform. 47(4), 409–443 (2002)
Shoichet, B.K., McGovern, S.L., Wei, B., Irwin, J.J.: Lead discovery using molecular docking. Curr. Opin. Chem. Biol. 6(4), 439–446 (2002)
Kolb, P., Ferreira, R.S., Irwin, J.J., Shoichet, B.K.: Docking and chemoinformatic screens for new ligands and targets. Curr. Opin. Biotechnol. 20(4), 429–436 (2009)
Zhu, S., Okuno, Y., Tsujimoto, G., Mamitsuka, H.: A probabilistic model for mining implicit chemical compound–generelations from literature. Bioinformatics 21(Suppl. 2), ii245–ii251 (2005)
Butina, D., Segall, M.D., Frankcombe, K.: Predicting adme properties in silico: methods and models. Drug Discov. Today 7(11), S83–S88 (2002)
Byvatov, E., Fechner, U., Sadowski, J., Schneider, G.: Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43(6), 1882–1889 (2003)
Keiser, M.J., Roth, B.L., Armbruster, B.N., Ernsberger, P., Irwin, J.J., Shoichet, B.K.: Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197–206 (2007)
Klabunde, T., Hessler, G.: Drug design strategies for targeting g-protein-coupled receptors. Chembiochem 3(10), 928–944 (2002)
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 24(13), i232–i240 (2008)
Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009)
Gönen, M.: Predicting drug-target interactions from chemical and genomic kernels using bayesian matrix factorization. Bioinformatics 28(18), 2304–2310 (2012)
van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21), 3036–3043 (2011)
Xia, Z., Wu, L.-Y., Zhou, X., Wong, S.T.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(Suppl. 2), S6 (2010)
Jacob, L., Vert, J.-P.: Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24(19), 2149–2156 (2008)
Klabunde, T.: Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol. 152(1), 5–7 (2007)
Schuffenhauer, A., Floersheim, P., Acklin, P., Jacoby, E.: Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 43(2), 391–405 (2003)
Nagamine, N., Sakakibara, Y.: Statistical prediction of protein-chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 23(15), 2004–2012 (2007)
Nagamine, N., Shirakawa, T., Minato, Y., Torii, K., Kobayashi, H., Imoto, M., Sakakibara, Y.: Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLoS Comput. Biol. 5(6), e1 000 397–e1 000 397 (2009)
Yabuuchi, H., Niijima, S., Takematsu, H., Ida, T., Hirokawa, T., Hara, T., Ogawa, T., Minowa, Y., Tsujimoto, G., Okuno, Y.: Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol. 7(1), 472 (2011)
Wang, Y., Zeng, J.: Predicting drug-target interactions using restricted boltzmann machines. Bioinformatics 29(13), i126–i134 (2013)
Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L.J., Bork, P.: Stitch 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 40(D1), D876–D880 (2012)
Ballesteros, J., Palczewski, K.: G protein-coupled receptor drug discovery: implications from the crystal structure of rhodopsin. Curr. Opin. Drug Discov. Dev. 4(5), 561 (2001)
Yamanishi, Y., Kotera, M., Kanehisa, M., Goto, S.: Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26(12), i246–i254 (2010)
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)
Yu, G., Li, F., Qin, Y., Bo, X., Wu, Y., Wang, S.: Gosemsim: an r package for measuring semantic similarity among go terms and gene products. Bioinformatics 26(7), 976–978 (2010)
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, pp. 1025–1033. ACM (2013)
Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., Tang, Y.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)
Cobanoglu, M.C., Liu, C., Hu, F., Oltvai, Z.N., Bahar, I.: Predicting drug-target interactions using probabilistic matrix factorization. J. Chem. Inf. Model. 53(12), 3399–3409 (2013)
Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., Hirakawa, M.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34(Suppl. 1), D354–D357 (2006)
Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., Schomburg, D.: Brenda, the enzyme database: updates and major new developments. Nucleic Acids Res. 32(Suppl. 1), D431–D433 (2004)
Günther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E.G., Gewiess, A., Jensen, L.J., et al.: Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36(Suppl. 1), D919–D922 (2008)
Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M.: Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(Suppl. 1), D901–D906 (2008)
Bertsekas, D.P.: Nonlinear programming (1999)
Elman, H.C., Golub, G.H.: Inexact and preconditioned uzawa algorithms for saddle point problems. SIAM J. Numer. Anal. 31(6), 1645–1661 (1994)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2009)
Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S.: Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings in Bioinform., bbt056 (2013)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)
Wu, C., Liao, Q.: An useful tool for finding drug-target interaction in drugbank and KEGG. http://www.cse.ust.hk/~qnature/
Hu, Y., Bajorath, J.: Compound promiscuity: what can we learn from current data? Drug Discov. Today 18(13), 644–650 (2013)
Acknowledgments
This work was supported by The National Natural Science Foundation of China (under grant No. U1435222 and No. 61502515). And this work was also supported in part by grants from 973 project 2013CB329006, RGC under the contract CERG 16212714.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Liao, Q., Guan, N., Wu, C., Zhang, Q. (2016). Predicting Unknown Interactions Between Known Drugs and Targets via Matrix Completion. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-31753-3_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31752-6
Online ISBN: 978-3-319-31753-3
eBook Packages: Computer ScienceComputer Science (R0)