Fig. 1: Overview of computational drug repurposing platform for COVID-19.

a COVID-19 is associated with more severe outcomes in older individuals, suggesting that gene expression programs associated with SARS-CoV-2 and aging must be analyzed in tandem. A potential hypothesis regarding the cross-talk between SARS-CoV-2 and aging relies on changes in tissue stiffness in older individuals, outlined in ref. ²¹. Ciliated cells are denoted in blue, stromal/fibroblast cells are in orange and SARS-CoV-2 viral particles are in red. b In order to identify potential drug candidates for COVID-19, we integrated RNA-seq data from SARS-CoV-2-infected cells, obtained from ref. ²³, and RNA-seq data from the lung tissue of young and old individuals, collected as part of the Genotype-Tissue Expression (GTEx) project²⁴, with protein–protein interaction data (from ref. ⁴²), drug–target data (from DrugCentral⁴⁵) and the large-scale transcriptional drug screen Connectivity Map (CMap)². c, d Based on this data, we develop a drug repurposing pipeline, which consists of first, mining relevant drugs by matching their signatures with the reverse disease signature in the latent embedding obtained by an overparameterized autoencoder and sharing data across cell types to obtain missing drug signatures via synthetic interventions. Blue and orange points in the latent space represent data associated with the drug screen and the SARS-CoV-2 infection study. Second, we identify a disease interactome within the protein–protein interaction network by identifying a minimal subnetwork that connects the genes differentially expressed by SARS-CoV-2 infection and aging using a Steiner tree analysis. Third, we validate the drugs identified in the first step that have targets in the interactome (greed diamond) by identifying the potential drug mechanism using causal structure discovery. Nodes are colored according to the \({\mathrm{log}\,}_{2}\)-fold gene expression change associated with SARS-CoV-2 infection, and gray nodes indicate Steiner nodes.