Integrated Strategies for Identifying Leads That Target the NS3 Helicase of the Hepatitis C Virus
Bibliographic record
Abstract
Future treatments for individuals infected by the hepatitis C virus (HCV) will likely involve combinations of compounds that inhibit multiple viral targets. The helicase of HCV is an attractive target with no known drug candidates in clinical trials. Herein we describe an integrated strategy for identifying fragment inhibitors using structural and biophysical techniques. Based on an X-ray structure of apo HCV helicase and in silico and bioinformatic analyses of HCV variants, we identified that one site in particular (labeled 3 + 4) was the most conserved and attractive pocket to target for a drug discovery campaign. Compounds from multiple sources were screened to identify inhibitors or binders to this site, and enzymatic and biophysical assays (NMR and SPR) were used to triage the most promising ligands for 3D structure determination by X-ray crystallography. Medicinal chemistry and biophysical evaluations focused on exploring the most promising lead series. The strategies employed here can have general utility in drug discovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".