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Cited by 70Computational Drug Discovery MethodsBioinformatics and Genomic NetworksMachine Learning in Materials Science

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19

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Hooshmand, Seyed Aghil, Zarei Ghobadi, Mohadeseh, Hooshmand, Seyyed Emad, Azimzadeh Jamalkandi, Sadegh, Alavi, Seyed Mehdi, Masoudi-Nejad, Ali·2021·Molecular Diversity
DOI 10.1007/s11030-020-10144-9PMC PMC7525234
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Abstract

Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.

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  • DBNs have been extensively used to fuse biomedical modalities for drug repurposing, clustering of cancer patients, and predicting disease-gene pairs.

    “DBNs have been extensively used to fuse biomedical modalities for drug repurposing [52], clustering of cancer patients [53] and predicting disease-gene pairs [54].”

    From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [52]

    The Introduction and Conclusion describe fusing drug chemical-structure and gene-expression data to identify candidates for COVID-19 drug repurposing, which supports the portion attached to citation [52]. The paper calls its method a multimodal restricted Boltzmann machine (MM-RBM), not a DBN, and does not establish that DBNs have been extensively used. The cancer-patient and disease-gene portions are attached to other citations. (LLM, conf=0.83)

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Authors
Hooshmand, S. A., Zarei Ghobadi, M., Hooshmand, S. E., Azimzadeh Jamalkandi, S., Alavi, S. M., & Masoudi-Nejad, A.
Affiliations
  • Iran University of Medical Sciences (IR)
  • National Institute of Genetic Engineering and Biotechnology (IR)
ORCID
Venue
Molecular Diversity
Publisher
Springer Science and Business Media LLC
Published
2020-09-30
Volume
25
Issue
3
Pages
1717-1730
ISSN
  • 1381-1991 (ISSN-L)
  • 1573-501X
DOI
10.1007/s11030-020-10144-9
PMC
PMC7525234
OpenAlex
W3090109244
Cited by
70
References
50
Citations by year
2026: 6 · 2025: 7 · 2024: 7 · 2023: 19 · 2022: 17 · 2021: 12 · 2020: 2
FWCI
6.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Diversity (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
drug repurposing, COVID-19 treatment, multimodal data integration, differentially expressed genes, small molecule perturbations, computational drug discovery
MeSH terms
Deep Learning, COVID-19 Drug Treatment, Probability, Small Molecule Libraries, Drug Repositioning

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