Abstract
Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multi-source data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the inter-institutional collaborations for DDI predictions. We propose MPCDDI, a secure multiparty computation-based deep learning framework for drug-drug interaction predictions. MPCDDI leverages the secret sharing technologies to incorporate the drug-related feature data from multiple institutions and develops a deep learning model for DDI predictions. In MPCDDI, all data transmission and deep learning operations are integrated into secure multiparty computation (MPC) frameworks to enable high-quality collaboration among pharmaceutical institutions without divulging private drug-related information. The results suggest that MPCDDI is superior to other five baselines and achieves the similar performance to that of the corresponding plaintext collaborations. More interestingly, MPCDDI significantly outperforms methods that use private data from the single institution. In summary, MPCDDI is an effective framework for promoting collaborative and privacy-preserving drug discovery.
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References
Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420–432. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-46766-1_34
Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900 (2015)
Cho, H., Wu, D.J., Berger, B.: Secure genome-wide association analysis using multiparty computation. Nat. Biotechnol. 36(6), 547–551 (2018)
Cramer, R., Damgård, I., Ishai, Y.: Share conversion, pseudorandom secret-sharing and applications to secure computation. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 342–362. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30576-7_19
Damgård, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643–662. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_38
Davis, A.P., et al.: The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 41(D1), D1104–D1114 (2013)
Deac, A., Huang, Y.H., Veličković, P., Liò, P., Tang, J.: Drug-drug adverse effect prediction with graph co-attention. ar**v preprint ar**v:1905.00534 (2019)
Demmler, D., Schneider, T., Zohner, M.: ABY-a framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)
Deng, Y., Xu, X., Qiu, Y., **a, J., Zhang, W., Liu, S.: A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics 36(15), 4316–4322 (2020)
Edwards, I.R., Aronson, J.K.: Adverse drug reactions: definitions, diagnosis, and management. Lancet 356(9237), 1255–1259 (2000)
Evans, W.E., McLeod, H.L.: Pharmacogenomics-drug disposition, drug targets, and side effects. N. Engl. J. Med. 348(6), 538–549 (2003)
Giacomini, K.M., Krauss, R.M., Roden, D.M., Eichelbaum, M., Hayden, M.R., Nakamura, Y.: When good drugs go bad. Nature 446(7139), 975–977 (2007)
Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game, or a completeness theorem for protocols with honest majority. In: Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali, pp. 307–328 (2019)
Hecker, N., et al.: SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res. 40(D1), D1113–D1117 (2012)
Hie, B., Cho, H., Berger, B.: Realizing private and practical pharmacological collaboration. Science 362(6412), 347–350 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Jagadeesh, K.A., Wu, D.J., Birgmeier, J.A., Boneh, D., Bejerano, G.: Deriving genomic diagnoses without revealing patient genomes. Science 357(6352), 692–695 (2017)
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., van der Maaten, L.: CRYPTEN: secure multi-party computation meets machine learning. In: Advances in Neural Information Processing Systems, vol. 34, pp. 4961–4973 (2021)
Knox, C., et al.: DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39(suppl_1), D1035–D1041 (2010)
Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 343 (2010)
Landrum, G.: RDKit documentation. Release 1(1–79), 4 (2013)
Law, V., et al.: DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42(D1), D1091–D1097 (2014)
Luo, Y., et al.: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8(1), 1–13 (2017)
Ma, R., et al.: Secure multiparty computation for privacy-preserving drug discovery. Bioinformatics 36(9), 2872–2880 (2020)
Ma, T., **ao, C., Zhou, J., Wang, F.: Drug similarity integration through attentive multi-view graph auto-encoders. ar**v preprint ar**v:1804.10850 (2018)
Qin, C., et al.: Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res. 42(D1), D1118–D1123 (2014)
Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385–394 (2017)
Ryu, J.Y., Kim, H.U., Lee, S.Y.: Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Natl. Acad. Sci. 115(18), E4304–E4311 (2018)
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
Vilar, S., et al.: Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protoc. 9(9), 2147–2163 (2014)
Wang, X., Cheng, Y., Yang, Y., Li, F., Peng, S.: Multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery. ar**v preprint ar**v:2201.04437 (2022)
Yue, X., et al.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020)
Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457–i466 (2018)
Acknowledgements
This work was supported by NSFC Grants U19A2067; National Key R &D Program of China 2022YFC3400404; Science Foundation for Distinguished Young Scholars of Hunan Province (2020JJ2009); Science Foundation of Changsha Z202069420652, kq2004010; JZ20195242029, JH20199142034; The Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics, the National Supercomputing Center in Changsha (http://nscc.hnu.edu.cn/), and Peng Cheng Lab.
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**ao, X., Wang, X., Liu, S., Peng, S. (2022). MPCDDI: A Secure Multiparty Computation-Based Deep Learning Framework for Drug-Drug Interaction Predictions. In: Bansal, M.S., Cai, Z., Mangul, S. (eds) Bioinformatics Research and Applications. ISBRA 2022. Lecture Notes in Computer Science(), vol 13760. Springer, Cham. https://doi.org/10.1007/978-3-031-23198-8_24
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