Name
Abstract | ||
|---|---|---|
We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patter.2021.100396 | Patterns |
Keywords | DocType | Volume |
drug repurposing,COVID-19,SARS-CoV-2,non-negative matrix factorization,network medicine,kernels on graphs,graph visualization | Journal | 3 |
Issue | ISSN | Citations |
1 | 2666-3899 | 1 |
PageRank | References | Authors |
0.48 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suzana de Siqueira Santos | 1 | 1 | 0.48 |
| Mateo Torres | 2 | 1 | 0.48 |
| Diego Galeano | 3 | 1 | 0.48 |
| María Del Mar Sánchez | 4 | 1 | 0.48 |
| luca cernuzzi | 5 | 120 | 23.62 |
| Alberto Paccanaro | 6 | 206 | 24.14 |