  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.
