MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction

Abstract

Precise prediction of drug-drug interactions (DDIs) is essential for pharmaceutical research and clinical applications to minimize adverse reactions, optimize therapies, and reduce costs. However, existing methods still face challenges in effectively integrating multidimensional drug features and fully utilizing edge features in molecular graphs, which are crucial for predicting DDIs precisely. Moreover, current methods may not adequately capture the complex relationships between different types of features, limiting predictive performance. This paper proposes the MFCN-DDI model for DDI type prediction. The model consists of a multimodal feature extraction module, a capsule network-based feature fusion module, and a DDI predictor module. In the multimodal feature extraction module, four kinds of features are used to provide rich and comprehensive representations for subsequent DDI type prediction, where molecular graph features are generated by considering molecular graphs with edge features. The capsule network-based feature fusion module captures complex feature relationships to generate high- quality integrated representations. In the DDI predictor module, multiclass and multilabel classification predictions are performed accurately. Experimental results show that MFCN-DDI outperforms existing comparison models in prediction tasks. Case studies further prove its practical applicability. In summary, MFCN-DDI provides an efficient and reliable solution for DDI prediction.

Keywords

capsule network / drug-drug interaction / edge-featured graph attention network / multimodal feature

References

[1] Liu J , Gefen O , Ronin I , Bar-Meir M , Balaban NQ . Effect of tolerance on the evolution of antibiotic resistance under drug combinations. Science. 2020; 367 (6474): 200- 4.

[2] Sun Y , Sheng Z , Ma C , Tang K , Zhu R , Wu Z , et al. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat Commun. 2015; 6 (1): 8481.

[3] Cheng F , Zhao Z . Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inf Assoc. 2014; 21 (e2): e278- 86.

[4] Xiong G , Yang Z , Yi J , Wang N , Wang L , Zhu H , et al. DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Res. 2022; 50 (D1): D1200- 7.

[5] He C , Liu Y , Li H , Zhang H , Mao Y , Qin X , et al. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction. BMC Bioinf. 2022; 23 (1): 224.

[6] Huang K , Xiao C , Hoang T , Glass L , Sun J . Caster:predicting drug interactions with chemical substructure representation. Proc AAAI Conf Artif Intell. 2020; 34 (1): 702- 9.

[7] Qiu Y , Zhang Y , Deng Y , Liu S , Zhang W . A comprehensive review of computational methods for drug-drug interaction detection. IEEE ACM Trans Comput Biol Bioinf. 2022; 19 (4): 1968- 85.

[8] Chen J , Sun X , Jin X , Sutcliffe R . Extracting drug-drug interactions from no-blinding texts using key semantic sentences and GHM loss. J Biomed Inf. 2022; 135: 104192.

[9] Asada M , Miwa M , Sasaki Y . Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature. Bioinformatics. 2023; 39 (1): btac754.

[10] Zhan C , Roughead E , Liu L , Pratt N , Li J . Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data. J Biomed Inf. 2020; 112: 103603.

[11] Mei S , Zhang K . A machine learning framework for predicting drug-drug interactions. Sci Rep. 2021; 11 (1): 17619.

[12] Yu Y , Huang K , Zhang C , Glass LM , Sun J , Xiao C . SumGNN:multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics. 2021; 37 (18): 2988- 95.

[13] Chen Y , Ma T , Yang X , Wang J , Song B , Zeng X . MUFFIN:multi-scale feature fusion for drug-drug interaction prediction. Bioinformatics. 2021; 37 (17): 2651- 8.

[14] Shi J-Y , Huang H , Li J-X , Lei P , Zhang Y-N , Dong K , et al. TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs. BMC Bioinf. 2018; 19 (14): 411.

[15] Ryu JY , Kim HU , Lee SY . Deep learning improves prediction of drug-drug and drug-food interactions. Proc Natl Acad Sci. 2018; 115 (18): E4304- 11.

[16] Deng Y , Xu X , Qiu Y , Xia J , Zhang W , Liu S . A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics. 2020; 36 (15): 4316- 22.

[17] Masumshah R , Eslahchi C . DPSP: a multimodal deep learning framework for polypharmacy side effects prediction. Bioinform. Adv. 2023; 3 (1): vbad110.

[18] Deng Y , Qiu Y , Xu X , Liu S , Zhang Z , Zhu S , et al. META-DDIE:predicting drug-drug interaction events with few-shot learning. Briefings Bioinf. 2022; 23 (1): bbab514.

[19] Wang Z , Xiong Z , Huang F , Liu X , Zhang W . ZeroDDI: a zero-shot drug-drug interaction event prediction method with semantic enhanced learning and dual-modal uniform alignment. 2024. Preprint at arXiv: 2407.00891.

[20] Zitnik M , Agrawal M , Leskovec J . Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics. 2018; 34 (13): i457- 66.

[21] Li Z , Zhu S , Shao B , Zeng X , Wang T , Liu TY . DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. Briefings Bioinf. 2023; 24 (1): bbac597.

[22] Xiong Z , Liu S , Huang F , Wang Z , Liu X , Zhang Z , et al. Multi-relational contrastive learning graph neural network for drug-drug interaction event prediction. Proc AAAI Conf Artif Intell. 2023; 37 (4): 5339- 47.

[23] Zhao Y , Yin J , Zhang L , Zhang Y , Chen X . Drug-drug interaction prediction:databases, web servers and computational models. Briefings Bioinf. 2024; 25 (1): bbad445.

[24] Han C-D , Wang C-C , Huang L , Chen X . MCFF-MTDDI:multi-channel feature fusion for multi-typed drug-drug interaction prediction. Briefings Bioinf. 2023; 24 (4): bbad215.

[25] Vadaddi SM , Zhao Q , Savoie BM . Graph to activation energy models easily reach irreducible errors but show limited transferability. J Phys Chem A. 2024; 128 (13): 2543- 55.

[26] Zhao H , Du R , Zhou R , Li S , Duan G , Wang J . SCN-MLTPP: a multi-label classifier for predicting therapeutic properties of peptides using the stacked capsule network. IEEE ACM Trans Comput Biol Bioinf. 2023; 20 (6): 3715- 24.

[27] Cohen J . A coefficient of agreement for nominal scale. Educ Psychol Meas. 1960; 20 (1): 37- 46.

[28] Lin S , Wang Y , Zhang L , Chu Y , Liu Y , Fang Y , et al. MDF-SA-DDI:predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. Briefings Bioinf. 2022; 23 (1): bbab421.

[29] Yu L , Xu Z , Cheng M , Lin W , Qiu W , Xiao X . MSEDDI:multi-scale embedding for predicting drug-drug interaction events. Int J Mol Sci. 2023; 24 (5): 4500.

[30] Wishart DS , Feunang YD , Guo AC , Lo EJ , Marcu A , Grant JR , et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018; 46 (D1): D1074- 82.

[31] Gülker H , Haverkamp W , Hindricks G . Ion regulation disorders and cardiac arrhythmia. The relevance of sodium, potassium, calcium, and magnesium. Arzneim Forsch. 1989; 39 (1A): 130- 4.

[32] Ioannidis VN , Song X , Manchanda S , Li M , Pan X , Zheng D , et al. Drkg-drug repurposing knowledge graph for COVID-19. 2020. Preprint at arXiv: 2010.09600.

[33] Liu R , AbdulHameed MDM , Kumar K , Yu X , Wallqvist A , Reifman J . Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC Pharmacol. Toxicol. 2017; 18 (1): 44.

[34] Bordes A , Usunier N , Garcia-Duran A , Weston J , Yakhnenko O . Translating embeddings for modeling multi-relational data. In:Burges CJC, Bottou L, Ghahramani Z, Weinberger KQ, editors. Advances in Neural Information Processing Systems 26:27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, USA; 2013. p. 2787- 95.

[35] Kipf TN , Welling M . Semi-supervised classification with graph convolutional networks. 2016. Preprint at arXiv:1609.02907.

[36] Yang J , Cai Y , Zhao K , Xie H , Chen X . Concepts and applications of chemical fingerprint for hit and lead screening. Drug Discov Today. 2022; 27 (11): 103356.

[37] Durant JL , Leland BA , Henry DR , Nourse JG . Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci. 2002; 42 (6): 1273- 80.

[38] Rogers D , Hahn M . Extended-connectivity fingerprints. J Chem Inf Model. 2010; 50 (5): 742- 54.

[39] Estrada E , Uriarte E . Recent advances on the role of topological indices in drug discovery research. Curr Med Chem. 2001; 8 (13): 1573- 88.

[40] Stiefl N , Watson IA , Baumann K , Zaliani A . ErG:2D pharmacophore descriptions for scaffold hopping. J Chem Inf Model. 2006; 46 (1): 208- 20.

[41] Sabour S , Frosst N , Hinton GE . Dynamic routing between capsules. 2017. Preprint at arXiv:1710.09829.

[42] Li P , Shao B , Zhao G , Liu Z-P . Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning. BMC Biol. 2025; 23 (1): 123.

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