Skip to main content

Matrix factorization with denoising autoencoders for prediction of drug–target interactions


Drug–target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug–target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with matrix factorization-based methods. Therefore, some matrix factorization methods utilize side information to address both the sparsity issue of the interaction matrix and the cold-start issue. By combining matrix factorization and autoencoders, we propose a hybrid DTI prediction model that simultaneously learn the hidden factors of drugs and targets from their side information and interaction matrix. The proposed method is composed of two steps: the pre-processing of the interaction matrix, and the hybrid model. We leverage the similarity matrices of both drugs and targets to address the sparsity problem of the interaction matrix. The comparison of our approach against other algorithms on the same reference datasets has shown good results regarding area under receiver operating characteristic curve and the area under precision–recall curve. More specifically, experimental results achieve high accuracy on golden standard datasets (e.g., Nuclear Receptors, GPCRs, Ion Channels, and Enzymes) when performed with five repetitions of tenfold cross-validation.

Graphical abstract

Display graphical of the hybrid model of Matrix Factorization with Denoising Autoencoders with the help side information of drugs and targets for Prediction of Drug-Target Interactions

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Data availability

The datasets used in this project can be found in


  1. Wang H, Wang J, Dong C et al (2020) A novel approach for drug-target interactions prediction based on multimodal deep autoencoder. Front Pharmacol 10:1592.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sajadi SZ, Zare Chahooki MA, Gharaghani S, Abbasi K (2021) AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders. BMC Bioinformatics 22:204.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen R, Liu X, Jin S, et al Machine learning for drug-target interaction prediction.

  4. Zhang W, Lin W, Zhang D et al (2018) Recent advances in the machine learning-based drug-target interaction prediction. Curr Drug Metab 20:194–202.

    CAS  Article  Google Scholar 

  5. Ezzat A, Wu M, Li XL, Kwoh CK (2019) Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 20:1337–1357.

    CAS  Article  PubMed  Google Scholar 

  6. Chen YZ, Zhi DG (2001) Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43:217–226.

    CAS  Article  PubMed  Google Scholar 

  7. Periole X, Knepp AM, Sakmar TP et al (2012) Structural determinants of the supramolecular organization of G protein-coupled receptors in bilayers. J Am Chem Soc 134:10959–10965.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Opella SJ (2013) Structure determination of membrane proteins by nuclear magnetic resonance spectroscopy. Palo Alto Calif 6:305–328

    CAS  Google Scholar 

  9. Wen M, Zhang Z, Niu S et al (2017) Deep-learning-based drug-target interaction prediction. J Proteome Res 16:1401–1409.

    CAS  Article  PubMed  Google Scholar 

  10. Abbasi K, Razzaghi P, Poso A et al (2020) Deep learning in drug target interaction prediction: current and future perspectives. Curr Med Chem 28:2100–2113.

    CAS  Article  Google Scholar 

  11. Pan X, Fan YX, Yan J, Bin SH (2016) IPMiner: Hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genomics 17:1–14.

    Article  Google Scholar 

  12. Deng L, Fan C, Zeng Z (2017) A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction. BMC Bioinformatics 18:211–220.

    Article  Google Scholar 

  13. Fu L, Peng Q (2017) A deep ensemble model to predict miRNA-disease association. Sci Rep 7:1–13

    Article  Google Scholar 

  14. Gligorijević V, Barot M, Bonneau R (2018) deepNF: deep network fusion for protein function prediction. Bioinformatics 34:3873–3881.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zeng X, Zhu S, Liu X et al (2019) deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35:5191–5198.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Hu PW, Chan KCC, You ZH (2016) Large-scale prediction of drug-target interactions from deep representations. Proceedings of the International Joint Conference on Neural Networks 2016-Octob:1236–1243.

  17. Öztürk H, Özgür A, Ozkirimli E (2018) DeepDTA: deep drug-target binding affinity prediction. Bioinformatics 34:i821–i829.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Lee I, Keum J, Nam H (2019) DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol 15:e1007129.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Yasuo N, Nakashima Y, Sekijima M (2019) CoDe-DTI: collaborative deep learning-based drug-target interaction prediction. proceedings-2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 792–797.

  20. Zheng X, Ding H, Mamitsuka H, Zhu S (2013) Collaborative matrix factorization with multiple similarities for predicting drug-Target interactions. Proceedings of the ACM SIGKDD Int Conf Knowl Discov Data Min Part 1288:1025–1033.

    Article  Google Scholar 

  21. Liu Y, Wu M, Miao C et al (2016) Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction. PLoS Comput Biol 12:1–26.

    CAS  Article  Google Scholar 

  22. Dong X, Yu L, Wu Z, Sun Y, Yuan L, & Zhang F (2017) A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence. Accessed 14 May 2022

  23. Olayan RS, Ashoor H, Bajic VB (2018) DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches. Bioinformatics 34:1164–1173.

    CAS  Article  PubMed  Google Scholar 

  24. Hao M, Bryant SH, Wang Y (2017) Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci Rep 17:1–11.

    CAS  Article  Google Scholar 

  25. Nascimento ACA, Prudêncio RBC, Costa IG (2016) A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinform 17:1–16.

    Article  Google Scholar 

  26. Mei JP, Kwoh CK, Yang P et al (2013) Drug–target interaction prediction by learning from local information and neighbors. Bioinformatics 29:238–245.

    CAS  Article  PubMed  Google Scholar 

  27. Lim H, Gray P, Xie L, Poleksic A (2016) Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem. Scientific 2016 6: 1–11. Doi:

  28. Hattori M, Tanaka N, Kanehisa M, Goto S (2010) SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res 38:W652–W656.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Smite TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147:195–197

    Article  Google Scholar 

  30. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37.

    Article  Google Scholar 

  31. Chen M, Li Y, Zhou X (2020) Autoencoders for drug-target interaction prediction.

  32. Bahi M (2018) Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform. Doi:

  33. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv.

    Article  Google Scholar 

  34. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning 1096–1103.

  35. Ahmadibeni A (2020) Aerial Vehicles Automated Target Recognition of Synthetic SAR Imagery Using Hybrid Stacked Denoising Autoencoders. Dissertation, Tennessee State University

  36. Ezzat A, Zhao P, Wu M et al (2017) Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Trans Comput Biol Bioinf 14:646–656.

    CAS  Article  Google Scholar 

  37. Lunnon WF, Brunvoll J, Cyvin SJ et al (1988) SMILES, a chemical language and information system: 1: introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36.

    Article  Google Scholar 

  38. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474.

    CAS  Article  PubMed  Google Scholar 

  39. Cao DS, Xu QS, Liang YZ (2013) propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962.

    CAS  Article  PubMed  Google Scholar 

  40. Yamanishi Y, Araki M, Gutteridge A et al (2008) Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232–i240.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Pahikkala T, Airola A, Pietilä S et al (2015) Toward more realistic drug-target interaction predictions. Brief Bioinform 16:325–337.

    CAS  Article  PubMed  Google Scholar 

  42. Raghavan V, Bollmann P, Jung GS (1989) A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans Inform Syst (TOIS) 7:205–229.

    Article  Google Scholar 

  43. Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. ACM Int Conf Proc Ser 148:233–240.

    Article  Google Scholar 

Download references


Not applicable.


No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations



SZS developed and implemented the method, executed the experiments, and wrote the manuscript. MAZCH, MT, and SGH conceptualized the study, interpreted the results, administered the project, supervised the work, and edited the.

Corresponding author

Correspondence to Mohammad Ali Zare Chahooki.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose manuscript. The manuscript has been read and approved by all authors.

Consent for publication

Not applicable.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sajadi, S.Z., Zare Chahooki, M.A., Tavakol, M. et al. Matrix factorization with denoising autoencoders for prediction of drug–target interactions. Mol Divers (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Drug–target interactions prediction
  • Deep learning
  • Hybrid model
  • Latent feature
  • Denoising autoencoder