A novel, covalent broad-spectrum inhibitor targeting human coronavirus Mpro
Bibliographic record
Abstract
Human coronaviruses (CoV) cause respiratory infections that range from mild to severe. CoVs are a large family of viruses with considerable genetic heterogeneity and a multitude of viral types, making preventing and treating these viruses difficult. Comprehensive treatments that inhibit CoV infections fulfill a pressing medical need and may be immensely valuable in managing emerging and endemic CoV infections. As the main protease (Mpro) is highly conserved across many CoVs, this protease has been identified as a route for broad CoV inhibition. We utilize the advanced generative chemistry platform Chemistry42 for de novo molecular design and obtained novel small-molecule, non-peptide-like inhibitors targeting the SARS-CoV-2 Mpro. ISM3312 is identified as an irreversible, covalent Mpro inhibitor from extensive virtual screening and structure-based optimization efforts. ISM3312 exhibits low off-target risk and outstanding antiviral activity against multiple human coronaviruses, including SARS-CoV-2, MERS-CoV, 229E, OC43, NL63, and HKU1 independent of P-glycoprotein (P-gp) inhibition. Furthermore, ISM3312 shows significant inhibitory effects against Nirmatrelvir-resistant Mpro mutants, suggesting ISM3312 may contribute to reduced viral escape in these settings. Incorporating ISM3312 and Nirmatrelvir into antiviral strategy could improve preparedness and reinforce defenses against future coronavirus threats. A novel covalent inhibitor, ISM3312, targets the main protease of multiple human coronaviruses, including drug-resistant strains, and shows broad antiviral activity. It offers a promising therapeutic strategy against current and future coronavirus threats.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".