A multimodal deep learning-based drug repurposing approach for treatment of COVID-19
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ORIGINAL ARTICLE
A multimodal deep learning‑based drug repurposing approach for treatment of COVID‑19 Seyed Aghil Hooshmand1,2 · Mohadeseh Zarei Ghobadi2 · Seyyed Emad Hooshmand3 · Sadegh Azimzadeh Jamalkandi4 · Seyed Mehdi Alavi5 · Ali Masoudi‑Nejad1,2 Received: 14 June 2020 / Accepted: 12 September 2020 © Springer Nature Switzerland AG 2020
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
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11030-020-10144-9) contains supplementary material, which is available to authorized users. * Ali Masoudi‑Nejad [email protected] 1
Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, University of Tehran, Kish International Campus, Kish Island, Iran
2
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
3
Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
4
Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Tehran, Iran
5
Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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Vol.:(0123456789)
Molecular Diversity
Graphic abstract
Keywords Drug repurposing · Deep learning · Multimodal data fusion · Restricted Boltzmann machine · COVID-19
Introduction COVID-19 pandemic, which became a trigger for a series of serious challenges, was brought about by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019. Until now, various FDA-approved
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