ArticleMolecular diversity2021
A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.
Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Article
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.Scientific reports · 2025Article
- A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.Molecular diversity · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.Multimedia tools and applications · 2023Article
- Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases.Bioinformatics advances · 2023Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.Scientific reports · 2022Article
- Article
- Multimodal deep learning for biomedical data fusion: a review.Briefings in bioinformatics · 2022Review
- Machine Learning Applications in Drug Repurposing.Interdisciplinary sciences, computational life sciences · 2022Review
- A fuzzy logic-based computational method for the repurposing of drugs against COVID-19.BioImpacts : BI · 2022Article
- Riboflavin for COVID-19 Adjuvant Treatment in Patients With Mental Health Disorders: Observational Study.Frontiers in pharmacology · 2022Article
- Deep Learning-Based Intelligent Robot in Sentencing.Frontiers in psychology · 2022Article
- Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.Structural chemistry · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.