Identification of Novel Small-Molecule Inhibitors of West Nile Virus Infection
Bibliographic record
Abstract
West Nile virus (WNV) has spread throughout the United States and Canada and now annually causes a clinical spectrum of human disease ranging from a self-limiting acute febrile illness to acute flaccid paralysis and lethal encephalitis. No therapy or vaccine is currently approved for use in humans. Using high-throughput screening assays that included a luciferase expressing WNV subgenomic replicon and an NS1 capture enzyme-linked immunosorbent assay, we evaluated a chemical library of over 80,000 compounds for their capacity to inhibit WNV replication. We identified 10 compounds with strong inhibitory activity against genetically diverse WNV and Kunjin virus isolates. Many of the inhibitory compounds belonged to a chemical family of secondary sulfonamides and have not been described previously to inhibit WNV or other related or unrelated viruses. Several of these compounds inhibited WNV infection in the submicromolar range, had selectivity indices of greater than 10, and inhibited replication of other flaviviruses, including dengue and yellow fever viruses. One of the most promising compounds, AP30451, specifically blocked translation of a yellow fever virus replicon but not a Sindbis virus replicon or an internal ribosome entry site containing mRNA. Overall, these compounds comprise a novel class of promising inhibitors for therapy against WNV and other flavivirus infections in humans.
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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) | 0.002 | 0.000 |
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".