ArticleApplied soft computing2021
Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Benchmarking heterogeneous network-based methods for drug repurposing.NPJ systems biology and applications · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.Interdisciplinary sciences, computational life sciences · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 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
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.Scientific reports · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction.Frontiers in pharmacology · 2025Article
- Enhancing drug-target interaction prediction with graph representation learning and knowledge-based regularization.Frontiers in bioinformatics · 2025Article
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.Interdisciplinary sciences, computational life sciences · 2024Article
- Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network.Current medicinal chemistry · 2024Article
- Drug repositioning based on weighted local information augmented graph neural network.Briefings in bioinformatics · 2023Article
- Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.Biomedicines · 2023Article
- DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response.Bioinformatics (Oxford, England) · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.Computers & chemical engineering · 2022Article
- Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.Journal of cellular and molecular medicine · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug-virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug-virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug-virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug-virus network, which integrates the known drug-virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug-virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug-virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF.
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