The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization
Schultz, B. T.; Zaliani, A.; Ebeling, C.; Reinshagen, J.; Bojkova, D.; Lage-Rupprecht, V.; Karki, R.; Lukassen, S.; Gadiya, Y.; Ravindra, N. G.; Das, S.; Baksi, S.; Domingo-Fernandez, D.; Lentzen, M.; Strivens, M.; Raschka, T.; Cinatl, J.; DeLong, L.; Gribbon, P.; Geisslinger, G.; Ciesek, S.; van Dijk, D.; Gardner, S.; Tom Kodamullil, A.; Froehlich, H.; Peitsch, M.; Jacobs, M.; Hoeng, J.; Eils, R.; Claussen, C.; Hofmann-Apitius, M.
Show abstract
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys 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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations 96%
- DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal 94%
- ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells 92%
Similar papers in this journal
- A machine learning and network framework to discover new indications for small molecules 95%
- Protein Domain-Based Prediction of Compound-Target Interactions and Experimental Validation on LIM Kinases 95%
- Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity 95%
Similar papers in this journal
- Fragment-Guided New Therapeutic Molecule Discovery and Mapping of Clinically Relevant Interactomes 95%
- Predicting antimicrobial class specificity of small molecules using machine learning 94%
- A Graph Convolutional Network-based screening strategy for rapid identification of SARS-CoV-2 cell-entry inhibitors 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.