DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction

Abstract

Background: Computational approaches for accurate prediction of drug interactions, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), are highly demanded for biochemical researchers. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure.

Methods: In this paper, we develop DeepDrug, a deep learning framework for overcoming the above limitation by using residual graph convolutional networks (Res-GCNs) and convolutional networks (CNNs) to learn the comprehensive structure- and sequence-based representations of drugs and proteins.

Results: DeepDrug outperforms state-of-the-art methods in a series of systematic experiments, including binary-class DDIs, multi-class/multi-label DDIs, binary-class DTIs classification and DTIs regression tasks. Furthermore, we visualize the structural features learned by DeepDrug Res-GCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2, where 7 out of 10 top-ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019 (COVID-19).

Conclusions: To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.

Author summary

Computational methods for DDIs and DTIs prediction are essential for accelerating the drug discovery process. We proposed a novel deep learning method DeepDrug, to tackle these two problems within a unified framework. DeepDrug is capable of extracting comprehensive features of both drug and target protein, thus demonstrating a superior prediction performance in a series of experiments. The downstream applications show that DeepDrug is useful in facilitating drug repositioning and discovering the potential drug against specific disease.

Graphical abstract

Keywords

drug-drug interaction / drug-target interaction / graph neural network / deep learning

References

[1] Bleakley, K. (2009). Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 25: 2397–2403

[2] Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A. Hoffman, M. (2019). Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf. Fusion, 50: 71–91

[3] Boolell, M., Allen, M. J., Ballard, S. A., Gepi-Attee, S., Muirhead, G. J., Naylor, A. M., Osterloh, I. H. (1996). Sildenafil: an orally active type 5 cyclic GMP-specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction. Int. J. Impot. Res., 8: 47–52

[4] Jia, J., Zhu, F., Ma, X., Cao, Z., Cao, Z. W., Li, Y., Li, Y. X. Chen, Y. (2009). Mechanisms of drug combinations: interaction and network perspectives. Nat. Rev. Drug Discov., 8: 111–128

[5] Han, K., Jeng, E. E., Hess, G. T., Morgens, D. W., Li, A. Bassik, M. (2017). Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol., 35: 463–474

[6] Sun, Y., Sheng, Z., Ma, C., Tang, K., Zhu, R., Wu, Z., Shen, R., Feng, J., Wu, D., Huang, D. . (2015). Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat. Commun., 6: 8481

[7] Lazarou, J., Pomeranz, B. H. Corey, P. (1998). Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA, 279: 1200–1205

[8] Gallagher, P. F., Barry, P. J., Ryan, C., Hartigan, I. (2008). Inappropriate prescribing in an acutely ill population of elderly patients as determined by Beers’ Criteria. Age Ageing, 37: 96–101

[9] Meinertz, T. (2001). Mibefradil—a drug which may enhance the propensity for the development of abnormal QT prolongation. Eur. Heart J. Suppl., 3: K89–K92

[10] Staffa, J. A., Chang, J. (2002). Cerivastatin and reports of fatal rhabdomyolysis. N. Engl. J. Med., 346: 539–540

[11] Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 34: D668–D672

[12] Tatonetti, N. P., Ye, P. P., Daneshjou, R. Altman, R. (2012). Data-driven prediction of drug effects and interactions. Sci. Transl. Med., 4: 125ra31

[13] Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Di Costanzo, L., Christie, C., Dalenberg, K., Duarte, J. M., Dutta, S. . (2019). RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res., 47: D464–D474

[14] Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B. . (2019). PubChem 2019 update: improved access to chemical data. Nucleic Acids Res., 47: D1102–D1109

[15] Rohani, N. (2019). Drug-drug interaction predicting by neural network using integrated similarity. Sci. Rep., 9: 13645

[16] Ryu, J. Y., Kim, H. U. Lee, S. (2018). Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Natl. Acad. Sci. USA, 115: E4304–E4311

[17] Liu, Q., Hu, Z., Jiang, R. (2020). DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics, 36: i911–i918

[18] Ma, T., Liu, Q., Li, H., Zhou, M., Jiang, R. (2022). DualGCN: a dual graph convolutional network model to predict cancer drug response. BMC Bioinformatics, 23: 129

[19] Yan, X., Zhang, S., Yiu, S. (2021). Interpretable prediction of drug-cell line response by triple matrix factorization. Quant. Biol., 9: 426–439

[20] Wang, C. (2020). Survey of similarity-based prediction of drug-protein interactions. Curr. Med. Chem., 27: 5856–5886

[21] Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W. (2008). Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24: i232–i240

[22] Zitnik, M., Agrawal, M. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34: i457–i466

[23] Zhang, T., Leng, J. (2020). Deep learning for drug-drug interaction extraction from the literature: a review. Brief. Bioinform., 21: 1609–1627

[24] Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska-Coleska, Z. (2021). Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief. Bioinform., 22: 247–269

[25] Huang, K., Fu, T., Glass, L. M., Zitnik, M., Xiao, C. (2021). DeepPurpose: a deep learning library for drug-target interaction prediction. Bioinformatics, 36: 5545–5547

[26] rk, H., (2018). DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 34: i821–i829

[27] NguyenT.,LeH.,QuinnT. P.,NguyenT.,LeT. D.. (2020) Graphdta: predicting drug–target binding affinity with graph neural networks. bioRxiv. 684662

[28] Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W. (2020). A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics, 36: 4316–4322

[29] Huang, K., Xiao, C., Glass, L. M., Zitnik, M. (2020). SkipGNN: predicting molecular interactions with skip-graph networks. Sci. Rep., 10: 21092

[30] Bajusz, D., cz, A. (2015). Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J.. Cheminform, 7: 20

[31] Mousavian, Z. (2014). Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin. Drug Metab. Toxicol., 10: 1273–1287

[32] Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L. (2017). A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun., 8: 573

[33] KipfT. N.. (2016) Semi-supervised classification with graph convolutional networks. arXiv, 160902907

[34] CucurullG.,CasanovaA.,RomeroA.,LioP.. (2017) Graph attention networks. arXiv, 171010903

[35] LiY.,TarlowD.,BrockschmidtM.. (2015) Gated graph sequence neural networks. arXiv,151105493

[36] BressonX.. (2017) Residual gated graph convnets. arXiv,171107553

[37] Xu, C., Liu, Q., Huang, M. (2020). Reinforced molecular optimization with neighborhood-controlled grammars. Adv. Neural Inf. Process. Syst., 33: 8366–8377

[38] DingK.,ZhouM.,WangZ.,LiuQ.,ArnoldC. W.,ZhangS.MetaxasD.. (2022) Graph convolutional networks for multi-modality medical imaging: methods, architectures, and clinical applications. arXiv, 220208916

[39] Yin, Q., Liu, Q., Fu, Z., Zeng, W., Zhang, B., Zhang, X., Jiang, R. (2022). scGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics, 38: 2996–3003

[40] DuvenaudD. K.,MaclaurinD.,IparraguirreJ.,BombarellR.,HirzelT.,Aspuru-GuzikA.AdamsR.. (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of the 28th International Conference on Neural Information Processing Systems Adv. Neural Inf. Process. Syst., pp. 2224–2232

[41] FoutA.,ByrdJ.,ShariatB.. (2017) Protein interface prediction using graph convolutional networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6533–6542

[42] FengQ.,DuevaE.,. and Ester, M. (2018) Padme: a deep learning-based framework for drug-target interaction prediction. arXiv,180709741

[43] Zamora-ResendizR.. (2019) Structural learning of proteins using graph convolutional neural networks. bioRxiv. 610444

[44] Schwarz, K., Allam, A., Perez Gonzalez, N. A. (2021). AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions. BMC Bioinformatics, 22: 412

[45] Xiong, G., Yang, Z., Yi, J., Wang, N., Wang, L., Zhu, H., Wu, C., Lu, A., Chen, X., Liu, S. . (2022). DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Res., 50: D1200–D1207

[46] Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., Xiao, G., Li, Y., Allen, J., Zhong, R. . (2014). A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol., 32: 1213–1222

[47] Huang, K., Fu, T., Glass, L. M., Zitnik, M., Xiao, C. (2021). DeepPurpose: a deep learning library for drug-target interaction prediction. Bioinformatics, 36: 5545–5547

[48] Tsubaki, M., Tomii, K. (2019). Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 35: 309–318

[49] Huang, K., Xiao, C., Glass, L. M. (2021). MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics, 37: 830–836

[50] Chen, L., Tan, X., Wang, D., Zhong, F., Liu, X., Yang, T., Luo, X., Chen, K., Jiang, H. (2020). TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics, 36: 4406–4414

[51] Herrero-Zazo, M., Segura-Bedmar, I., nez, P. (2013). The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J. Biomed. Inform., 46: 914–920

[52] Surjit, M. Lal, S. (2008). The SARS-CoV nucleocapsid protein: a protein with multifarious activities. Infect. Genet. Evol., 8: 397–405

[53] Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L. . (2020). A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature, 583: 459–468

[54] Stukalov, A., Girault, V., Grass, V., Karayel, O., Bergant, V., Urban, C., Haas, D. A., Huang, Y., Oubraham, L., Wang, A. . (2021). Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. Nature, 594: 246–252

[55] Kang, K., Kim, H. H. (2020). Tiotropium is predicted to be a promising drug for COVID-19 through transcriptome-based comprehensive molecular pathway analysis. Viruses, 12: 776

[56] Chen, H., Zhang, Z., Wang, L., Huang, Z., Gong, F., Li, X., Chen, Y. Wu, J. (2020). First clinical study using HCV protease inhibitor danoprevir to treat COVID-19 patients. Medicine (Baltimore), 99: e23357

[57] Wang, Z., Liu, M., Luo, Y., Xu, Z., Xie, Y., Wang, L., Cai, L., Qi, Q., Yuan, Z., Yang, T. . (2022). Advanced graph and sequence neural networks for molecular property prediction and drug discovery. Bioinformatics, 38: 2579–2586

[58] Chen, X., Chen, S., Song, S., Gao, Z., Hou, L., Zhang, X., Lv, H. (2022). Cell type annotation of single-cell chromatin accessibility data via supervised bayesian embedding. Nat. Mach. Intell., 4: 116–126

[59] Liu, Q., Chen, S., Jiang, R. Wong, W. (2021). Simultaneous deep generative modeling and clustering of single cell genomic data. Nat. Mach. Intell., 3: 536–544

[60] Duren, Z., Chang, F., Naqing, F., Xin, J., Liu, Q. Wong, W. (2022). Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG. Genome Biol., 23: 114

[61] Yin, Q., Wu, M., Liu, Q., Lv, H. (2019). DeepHistone: a deep learning approach to predicting histone modifications. BMC Genomics, 20: 193

[62] LanceC.,LueckenM. D.,BurkhardtD. B.,CannoodtR.,RautenstrauchP.,LaddachA.,UbingazhibovA.,CaoZ.DengK.,KhanS.,. (2022) Multimodal single cell data integration challenge: results and lessons learned. In: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, pp.162–176

[63] Liu, Q., Hua, K., Zhang, X., Wong, W. H. (2022). Deepcage: incorporating transcription factors in genome-wide prediction of chromatin accessibility. Genomics Proteomics Bioinformatics, 20: 496–507

[64] LiuQ.,ChenZ.WongW.. (2022) Causalegm: a general causal inference framework by encoding generative modeling. arXiv, 221205925

[65] Zeng, W., Liu, Q., Yin, Q., Jiang, R. Wong, W. (2023). HiChIPdb: a comprehensive database of HiChIP regulatory interactions. Nucleic Acids Res., 51: D159–D166

[66] Chen, S., Liu, Q., Cui, X., Feng, Z., Li, C., Wang, X., Zhang, X., Wang, Y. (2021). OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. Nucleic Acids Res., 49: W483–W490

[67] Davis, A. P., Wiegers, T. C., Johnson, R. J., Sciaky, D., Wiegers, J. Mattingly, C. (2023). Comparative toxicogenomics database (ctd): Update 2023. Nucleic Acids Res., 51: D1257–D1262

[68] RamsundarB.,EastmanP.,WaltersP.. (2019) Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More. Sebastopol, CA: O’Reilly Media

[69] Minhas, F., Geiss, B. J. (2014). PAIRpred: partner-specific prediction of interacting residues from sequence and structure. Proteins, 82: 1142–1155

[70] LiG.,XiongC.,ThabetA.. (2020) Deepergcn: All you need to train deeper GCNs. arXiv, 2006.07739

[71] BaJ. L.,KirosJ. R.HintonG.. (2016) Layer normalization. arXiv, 160706450

[72] Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L. (2016). BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 44: D1045–D1053

[73] PaszkeA.,GrossS.,MassaF.,LererA.,BradburyJ.,ChananG.,KilleenT.,LinZ.,GimelsheinN.. (2019) Pytorch: an imperative style, high-performance deep learning library. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 8026–8037

[74] MoritzP.,NishiharaR.,WangS.,TumanovA.,LiawR.,LiangE.,ElibolM.,YangZ.,PaulW.JordanM.. (2018) Ray: a distributed framework for emerging AI applications. In: 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 561–577

AVAILABILITY AND IMPLEMENTATION

DeepDrug can be freely downloaded from GitHub website (wanwenzeng/deepdrug).

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-022-0320.

ACKNOWLEDGEMENTS

We thank Daisy and Nicole for their helpful discussion. We acknowledge the fundings from National Key Research and Development Program of China (Nos. 2021YFF1200902 and 2020YFA0712402), and National Natural Science Foundation of China (Nos. 62273194, 61873141, 61721003 and 62003178).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Qijin Yin, Rui Fan, Xusheng Cao, Qiao Liu, Rui Jiang and Wanwen Zeng declare that they have no conflict of interest or financial conflicts to disclose.

This article does not contain any studies with human or animal materials performed by any of the authors.

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