Summary 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/ . Graphical abstract Highlights • A matrix decomposition model for repurposing broad-spectrum antivirals • A graph kernel approach to model perturbations induced by drugs on the interactome • Graph kernels can integrate transcriptomics data to improve drug repurposing • CoREx: a free online tool to formulate hypothesis for drug repurposing for COVID-19 The bigger picture The development timeline for treatments against emergent viral diseases can be significantly reduced by re-using drugs already available on the market—a concept known as drug repositioning. We present two complementary machine learning approaches for drug repositioning that target SARS- CoV-2 and host factors, respectively. Our matrix decomposition approach exploits drug developmental information to predict the effectiveness of broad-spectrum antiviral drugs. Our graph kernel-based approach, rooted in ideas from network medicine, predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. We also introduce CoREx, a freely available online tool that enables scientists to reason and formulate hypotheses about drug repurposing in the context of biological networks and pharmacological information. While we have developed these methodologies for COVID-19, our approaches can be applied to any viral disease. We present two complementary machine learning approaches for drug repositioning against COVID-19 that target SARS-CoV-2 and its cellular processes in the host, respectively. Our matrix decomposition approach exploits drug developmental information to predict broad-spectrum antivirals; our graph kernel-based approach, rooted in ideas from network medicine, predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. We also introduce CoREx, a freely available online tool to reason and formulate hypothesis about drug repurposing in the context of biological networks and pharmacological information.
【저자키워드】 COVID-19, Drug repurposing, SARS-CoV-2, network medicine, non-negative matrix factorization, kernels on graphs, graph visualization, 【초록키워드】 Treatment, antiviral drugs, transcriptomics, drug, Protein, Algorithm, SARS- CoV-2, Effectiveness, Factors, broad-spectrum antivirals, viral disease, experiment, methodology, information, predict, compassionate use, Hypothesis, Perturbation, complementary, online tool, Clinical use, Abstract, FDA-approved drug, host protein, graph, connectivity, while, pharmacological, Host, broad-spectrum antiviral, approach, IMPROVE, predicted, significantly, include, indicated, reduced, induce, hypothese, cellular processe, developmental, Map, explorer, machine learning approach, 【제목키워드】 machine, approach,