Integration of Computational and Experimental Techniques for the Discovery of SARS-CoV-2 PLpro Covalent Inhibitors
Bibliographic record
Abstract
Papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro or Mpro) are viral enzymes essential for the replication of SARS-CoV-2, the virus causing COVID-19 infection. While 3CLpro has been the main target of many potential antivirals including nirmatrelvir (active ingredient of Paxlovid), PLpro has proven to be more challenging to target and only a handful of inhibitors have been disclosed. However, PLpro inhibitors would enrich the therapeutic arsenal against COVID-19 resistant strains to 3CLpro inhibitors and in future coronavirus pandemics. Combining our experience with 3CLpro covalent inhibitors with our expertise in structure-based covalent drug discovery, we rationally designed PLpro inhibitors achieving a maximum potency (IC50) of 13 µM through fusion of GRL-0617 and VIR-251 PLpro inhibitors. In parallel, we launched an integrated large scale virtual screening/experimental approach, identifying four novel chemical series active at micromolar concentrations against PLpro. We report herein our investigations including rational design, virtual screening, synthesis of selected structures and in vitro assays leading to novel PLpro inhibitors. Surprisingly, these two very distinct approaches led to similar structures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".