Ligand Bioactive Conformation Plays a Critical Role in the Design of Drugs That Target the Hepatitis C Virus NS3 Protease
Bibliographic record
Abstract
A ligand-focused strategy employed NMR, X-ray, modeling, and medicinal chemistry to expose the critical role that bioactive conformation played in the design of a variety of drugs that target the HCV protease. The bioactive conformation (bound states) were determined for key inhibitors identified along our drug discovery pathway from the hit to clinical compounds. All adopt similar bioactive conformations for the common core derived from the hit peptide DDIVPC. A carefully designed SAR analysis, based on the advanced inhibitor 1 in which the P1 to P3 side chains and the N-terminal Boc were sequentially truncated, revealed a correlation between affinity and the relative predominance of the bioactive conformation in the free state. Interestingly, synergistic conformation effects on potency were also noted. Comparisons with clinical and recently marketed drugs from the pharmaceutical industry showed that all have the same core and similar bioactive conformations. This suggested that the variety of appendages discovered for these compounds also properly satisfy the bioactive conformation requirements and allowed for a large variety of HCV protease drug candidates to be designed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".