Repurposing screen identifies novel candidates for broad-spectrum coronavirus antivirals and druggable host targets
Bibliographic record
Abstract
ABSTRACT Libraries composed of licensed drugs represent a vast repertoire of molecules modulating physiological processes in humans, providing unique opportunities for the discovery of host-targeting antivirals. We screened the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) repurposing library with approximately 12,000 molecules for broad-spectrum coronavirus antivirals and discovered 134 compounds inhibiting an alphacoronavirus and mapping to 58 molecular target categories. Dominant targets included the 5-hydroxytryptamine receptor, the dopamine receptor, and cyclin-dependent kinases. Gene knock-out of the drugs’ host targets including cathepsin B and L (CTSB/L; VBY-825), the aryl hydrocarbon receptor (AHR; Phortress), the farnesyl-diphosphate farnesyltransferase 1 (FDFT1; P-3622), and the kelch-like ECH-associated protein 1 (KEAP1; Omaveloxolone), significantly modulated HCoV-229E infection, providing evidence that these compounds inhibited the virus through acting on their respective host targets. Counter-screening of all 134 primary compound candidates with SARS-CoV-2 and validation in primary cells identified Phortress, an AHR activating ligand, P-3622-targeting FDFT1, and Omaveloxolone, which activates the NFE2-like bZIP transcription factor 2 (NFE2L2) by liberating it from its endogenous inhibitor KEAP1, as antiviral candidates for both an Alpha - and a Betacoronavirus . This study provides an overview of HCoV-229E repurposing candidates and reveals novel potentially druggable viral host dependency factors hijacked by diverse coronaviruses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".