ArticleScientific reports2021
A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks.Bioinformatics advances · 2024Review
- A machine learning method for the identification and characterization of novel COVID-19 drug targets.Scientific reports · 2023Article
- Editorial: Impacts of public-private collaborative research on Alzheimer's disease: the case of the innovative medicines initiative.Frontiers in neurology · 2023Article
- A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism.Patterns (New York, N.Y.) · 2022Article
- Repurposing of Chemotherapeutics to Combat COVID-19.Current topics in medicinal chemistry · 2022Article
- Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.Artificial intelligence in the life sciences · 2021Article
- Estrogen Receptor Modulators in Viral Infections Such as SARS-CoV-2: Therapeutic Consequences.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
31 authors.
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Abstract
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
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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.