Detailed Computational Study of the Active Site of the Hepatitis C Viral RNA Polymerase to Aid Novel Drug Design
Bibliographic record
Abstract
The hepatitis C virus (HCV) RNA polymerase, NS5B, is a leading target for novel and selective HCV drug design. The enzyme has been the subject of intensive drug discovery aimed at developing direct acting antiviral (DAA) agents that inhibit its activity and hence prevent the virus from replicating its genome. In this study, we focus on one class of NS5B inhibitors, namely nucleos(t)ide mimetics. Forty-one distinct nucleotide structures have been modeled within the active site of NS5B for the six major HCV genotypes. Our comprehensive modeling protocol employed 287 different molecular dynamics simulations combined with the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) methodology to rank and analyze these structures for all genotypes. The binding interactions of the individual compounds have been investigated and reduced to the atomic level. The present study significantly refines our understanding of the mode of action of NS5B-nucleotide-inhibitors, identifies the key structural elements necessary for their activity, and implements the tools for ranking the potential of additional much needed novel inhibitors of NS5B.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".