Docking Ligands into Flexible and Solvated Macromolecules. 2. Development and Application of F<scp>itted</scp> 1.5 to the Virtual Screening of Potential HCV Polymerase Inhibitors
Bibliographic record
Abstract
HCV NS5B polymerase is a validated target for the treatment of hepatitis C, known to be one of the most challenging enzymes for docking programs. In order to improve the low accuracy of existing docking methods observed with this challenging enzyme, we have significantly modified and updated F itted 1.0, a recently reported docking program, into F itted 1.5. This enhanced version is now applicable to the virtual screening of compound libraries and includes new features such as filters and pharmacophore- or interaction-site-oriented docking. As a first validation, F itted 1.5 was applied to the testing set previously developed for F itted 1.0 and extended to include hepatitis C virus (HCV) polymerase inhibitors. This first validation showed an increased accuracy as well as an increase in speed. It also shows that the accuracy toward HCV polymerase is better than previously observed with other programs. Next, application of F itted 1.5 to the virtual screening of the Maybridge library seeded with known HCV polymerase inhibitors revealed its ability to recover most of these actives in the top 5% of the hit list. As a third validation, further biological assays uncovered HCV polymerase inhibition for selected Maybridge compounds ranked in the top of the hit list.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".