Search of Novel Small Molecule Inhibitors for the Main Protease of SARS-CoV-2
Bibliographic record
Abstract
The current outbreak of coronavirus disease 2019 (COVID-19) has prompted the necessity of efficient treatment strategies. The COVID-19 pandemic was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Main protease (Mpro), also called 3-chymotrypsin-like protease (3CL protease), plays an essential role in cleaving virus polyproteins for the functional replication complex. Therefore, Mpro is a promising drug target for COVID-19 therapy. Through molecular modelling, docking and a protease activity assay, we found four novel inhibitors targeting Mpro with the half maximal inhibitory concentration (IC50) and their binding affinities shown by the dissociation constants (KDs). Our new inhibitors CB-21, CB-25, CP-1 and LC24-20 have IC50s at 14.88 µM (95% Confidence Interval (95% CI): 10.35 µM to 20.48 µM), 22.74 µM (95% CI: 13.01 µM to 38.16 µM), 18.54µM (95% CI: 6.54 µM to 36.30 µM) and 32.87µM (95% CI: 18.37 µM to 54.80 µM)), respectively. The evaluation of interactions suggested that each inhibitor has a hydrogen bond or hydrophobic interactions with important residues, including the most essential catalytic residues: His41 and Cys145. All the four inhibitors have a much higher 50% lethal dose (LD50) compared with the well-known Mpro inhibitor GC376, demonstrating its low toxicity. These four inhibitors can be potential drug candidates for further in vitro and in vivo studies against COVID-19.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".