Bibliographic record
Abstract
Helicases are essential motor enzymes that couple nucleoside-triphosphate hydrolysis with DNA or RNA strand unwinding. They are integral to replication, transcription, splicing, and translation of the genome, and are also implicated in cancer, viral replication, and neurodegenerative diseases. Drug discovery efforts targeting helicases face challenges due to their dynamic enzymatic cycles, transient conformational states, and conserved active sites. Co-crystal structure of inhibitor-helicase complexes revealed four distinct mechanisms of inhibition: allosteric, ATP-competitive, RNA-competitive, and interfacial inhibitors. Using this classification, fragments crystallized with SARS-CoV-2 NSP13 highlighted a pocket potentially targeted by RNA-competitive ligands. This prompted an AI-assisted virtual screen, which yielded 244 potential compounds. A dendrogram including 445 human and viral helicases was constructed, mapping known inhibitors and fragments to highlight sequence similarities and chemical coverage. As more helicase inhibitors emerge, the dendrogram could inform mechanistic strategies for chemical inhibition, scaffold hopping opportunities or risks of unwanted poly-pharmacology.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".