Paper
Synthyra and the University of Delaware
Atlas Host-Pathogen Interaction Report
A technical report applying Atlas to host-pathogen interaction prediction, with case studies on COVID-19 anosmia, drug repurposing for Andes hantavirus, and a blind recall test on Bundibugyo ebolavirus.
Logan Hallee, Jason P. Gleghorn
May 9, 2026

What is in the report
Benchmarks. Per-pair performance on the leakage-controlled Bernett split, proteome-scale intra-actome screens for human and nineteen bacterial pathogens against a per-species supervised baseline, and a homology-controlled human-SARS-CoV-2 surface where prior methods sit at chance.
Case Study I, COVID-19 anosmia. The seventeen-protein SARS-CoV-2 proteome screened against the human proteome, with a checkpoint that had all SARS-CoV-2 sequences held out under a strict homology cut. The screen recovers chemosensory and mitochondrial signal consistent with the published non-cell-autonomous mechanism, and the report includes a nine-row calibration scorecard that grades itself: two clean recoveries, five weak or contested, two outright misses.
Case Study II, Andes hantavirus. Three viral proteins screened against 2,638 FDA-approved small molecules and 626 approved biologics in 33 seconds, followed by independent structural investigation of the top candidates. Two decades-old approved drugs outscore every antiviral with documented hantavirus activity on their respective targets.
Case Study III, Bundibugyo ebolavirus. A blind recall test. Four approved monoclonal antibodies bind ebolavirus glycoprotein and nothing else in the viral proteome, and the model was asked to place them across nine viral proteins with no target annotation. It assigned all four correctly with no score overlap. The same screen misses the canonical VP24 mechanism entirely, and an independent folding model fails to corroborate the antibody result.
What this does not show
Everything here is a model prediction. Nothing in this report establishes binding, efficacy, selectivity, or safety, and the candidates it nominates are interesting because they are cheap to test rather than because the prediction is strong. Clinical translation requires independent experimental validation.
The full report follows below, and the platform this work is built on is described in the Atlas article.
Full report