“… | Title | Datasets used | Repurposed Drugs | Evaluation Criteria | Tools used | Ref. |
1 | Network medicine framework for identifying drug-repurposing opportunities for COVID-19 | 13 Datasets, DrugBank, STRING | 989 Drugs, 77 Validated in VeroE6 Cells, 76/77 validated in Human Cells | Network proximity, network diffusion, Network AI | Experimental, Ensembl algorithmic prediction | [176] |
2 | Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence | CTDbase, STRING, Hetionet, DrugBank | 22 drugs and drug combinations | Validation with multiple sources, In-vitro screening | GSEA, Plotly, Gephi, GNN | [178] |
3 | COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning | Chemical Molecules, ZINC, ChEMBL, | – | Loss function and metrics | Few-shot learning | [179] |
4 | Machine learning and network medicine approaches for drug repositioning for COVID-19 | STRING, ChEMBL, DrugBank | – | In-vitro, In-vivo, clinical trials, and CMAP | – | [180] |
5 | Using informative features in machine learning based method for COVID-19 drug repurposing | STRING, DrugBank, Uniprot | 80 % of the predictions were verified | Statistical and clinical evidence | Enrichment analysis, | [182] |
6 | An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 | Literature, DrugBank, CTD, Uniprot, CMap | 41 Drugs | wet-lab, Expression profiles of patients and drug perturbation cells, clinical trials data | Molecular docking | [183] |
7 | Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19 | Uniprot, STRING, CMap, LINCS, GEO | – | Gene expression profiling, CMap ranking | LINCS tools | [184] |
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