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

Molecular Diversity (2022)Cite this article

Abstract

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

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Data availability

The datasets used in this project can be found in http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

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

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Authors and Affiliations

  1. Department of Computer Engineering, Yazd University, Yazd, Iran

    Seyedeh Zahra Sajadi & Mohammad Ali Zare Chahooki

  2. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Maryam Tavakol

  3. Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Sajjad Gharaghani

Authors
  1. Seyedeh Zahra Sajadi
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  2. Mohammad Ali Zare Chahooki
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  3. Maryam Tavakol
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  4. Sajjad Gharaghani
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Contributions

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.

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Correspondence to Mohammad Ali Zare Chahooki.

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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). https://doi.org/10.1007/s11030-022-10492-8

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  • DOI: https://doi.org/10.1007/s11030-022-10492-8

Keywords

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