Rational Design of Highly Potent SARS-CoV-2 nsp14 Methyltransferase Inhibitors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The search for new drugs against COVID-19 and its causative agent, SARS-CoV-2, is one of the major trends in the current medicinal chemistry. Targeting capping machinery could be one of the therapeutic concepts based on a unique mechanism of action. Viral RNA cap synthesis involves two methylation steps, the first of which is mediated by the nsp14 protein. Here, we rationally designed and synthesized a series of compounds capable of binding to both the S -adenosyl- l -methionine and the RNA-binding site of SARS-CoV-2 nsp14 N 7 -methyltransferase. These hybrid molecules showed excellent potency, high selectivity toward various human methyltransferases, nontoxicity, and high cell permeability. Despite the outstanding activity against the enzyme, our compounds showed poor antiviral performance in vitro. This suggests that the activity of this viral methyltransferase has no significant effect on virus transcription and replication at the cellular level. Therefore, our compounds represent unique tools to further explore the role of the SARS-CoV-2 nsp14 methyltransferase in the viral life cycle and the pathogenesis of COVID-19.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".