Rational Design of Macrocyclic Noncovalent Inhibitors of SARS-CoV-2 M<sup>pro</sup> from a DNA-Encoded Chemical Library Screening Hit That Demonstrate Potent Inhibition against Pan-Coronavirus Homologues and Nirmatrelvir-Resistant Variants
Bibliographic record
Abstract
The recent global COVID-19 pandemic has highlighted treatments for coronavirus infection as an unmet medical need. The main protease (M pro ) has been an important target for the development of SARS-CoV-2 direct-acting antivirals. Nirmatrelvir as a covalent M pro inhibitor was the first such approved therapy. Although M pro inhibitors of various chemical classes have been reported, they are generally less active against nirmatrelvir-resistant variants and have limited pan-coronavirus potential, presenting a significant human health risk upon future outbreaks. We here present a novel approach and utilized DNA-encoded chemical library screening to identify the noncovalent M pro inhibitor 5, which demonstrated a distinct binding mode to nirmatrelvir. A macrocyclization strategy designed to lock the active conformation resulted in lactone 12 with significantly improved antiviral activity. Further optimization led to the potent lactam 26, which demonstrated exceptional potency against nirmatrelvir-resistant variants as well as against a panel of viral main proteases from other coronaviruses.
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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 | 0.000 |
| 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".