Deep Drug Discovery of Mac Domain of SARS-CoV-2 (WT) Spike Inhibitors: Using Experimental ACE2 Inhibition TR-FRET Assay, Screening, Molecular Dynamic Simulations and Free Energy Calculations
Bibliographic record
Abstract
SARS-CoV-2 exploits the homotrimer transmembrane Spike glycoproteins (S protein) during host cell invasion. The Omicron XBB subvariant, delta, and prototype SARS-CoV-2 receptor-binding domain show similar binding strength to hACE2 (human Angiotensin-Converting Enzyme 2). Here we utilized multiligand virtual screening to identify small molecule inhibitors for their efficacy against SARS-CoV-2 virus using QPLD, pseudovirus ACE2 Inhibition -Time Resolved Forster/Fluorescence energy transfer (TR-FRET) Assay Screening, and Molecular Dynamics simulations (MDS). Three hundred and fifty thousand compounds were screened against the macrodomain of the nonstructural protein 3 of SARS-CoV-2. Using TR-FRET Assay, we filtered out two of 10 compounds that had no reported activity in in vitro screen against Spike S1: ACE2 binding assay. The percentage inhibition at 30 µM was found to be 79% for "Compound F1877-0839" and 69% for "Compound F0470-0003". This first of its kind study identified "FILLY" pocket in macrodomains. Our 200 ns MDS revealed stable binding poses of both leads. They can be used for further development of preclinical candidates.
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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 | 0.000 | 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) | 0.001 | 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".