The discovery of Zika virus NS2B-NS3 inhibitors with antiviral activity via an integrated virtual screening approach
Bibliographic record
Abstract
With expanding recent outbreaks and a lack of treatment options, the Zika virus (ZIKV) poses a severe health concern. The availability of ZIKV NS2B-NS3 co-crystallized structures paved the way for rational drug discovery. A computer-aided structure-based approach was used to screen a diverse library of compounds against ZIKV NS2B-NS3 protease. The top hits were selected based on various binding free energy calculations followed by per-residue decomposition analysis. The selected hits were then evaluated for their biological potential with ZIKV protease inhibition assay and antiviral activity. Among 26 selected compounds, 8 compounds showed promising activity against ZIKV protease with a percentage inhibition of greater than 25 and 3 compounds displayed ∼50% at 10 µM, which indicates an enrichment rate of approximately 36% (threshold IC50 < 10 µM) in the ZIKV-NS2B-NS3 protease inhibition assay. Of these, only one compound (23) produced whole-cell anti-ZIKV activity, and the binding mode of 23 was extensively analyzed through long-run molecular dynamics simulations. The current study provides a promising starting point for the further development of novel compounds against ZIKV.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".