Network medicine framework for identifying drug-repurposing opportunities for COVID-19

TLDR

The pandemic has underscored the need for rapid prioritization of approved drugs, and network medicine offers validated algorithms that exploit drug–target and disease‑gene network relationships to predict repurposing candidates. We applied AI‑driven network diffusion and proximity algorithms to rank 6,340 drugs, then benchmarked their predictions against 918 experimentally screened compounds and clinical‑trial lists to assess efficacy against SARS‑CoV‑2. While individual methods show predictive power, none is consistently reliable; a multimodal consensus of all algorithms consistently outperforms the best single pipeline, and 76 of 77 effective drugs act via network mechanisms rather than direct viral protein binding, illustrating a pathway for repurposing future pathogens.

Abstract

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.