Discovery of Novel Peptidomimetics as Irreversible <scp>CHIKV</scp> Ns<scp>P</scp>2 Protease Inhibitors Using Quantum Mechanical‐Based Ligand Descriptors
Bibliographic record
Abstract
Chikungunya virus (CHIKV) is a mosquito-borne alphavirus. Recent outbreaks of CHIKV infections have been reported in Asia, Africa, and Europe. The symptoms of CHIKV infection include fever, headache, nausea, vomiting, myalgia, rash, and chronic persistent arthralgia. To date, no vaccines or selective antiviral drugs against this important emerging virus have been reported. In this study, the design, synthesis, and antiviral activity screening of new topographical peptidomimetics revealed three potential prototype agents 3a, 4b, and 5d showing 93-100% maximum inhibition of CHIKV replication in cell-based assay having EC90 of 8.76-9.57 μg/mL. Intensive molecular modeling studies including covalent docking, lowest unoccupied molecular orbital energies, and the atomic condensed Fukui functions calculations strongly suggested the covalent binding of peptidomimetics 3a, 4b, and 5d to CHIKV nsP2 protease leading to permanent enzyme inactivation via Michael adduct formation between α/β-unsaturated ketone functionality in our designed peptidomimetics and active site catalytic cysteine1013. Furthermore, small molecular weight peptidomimetics 3a and 4b satisfied the Lipinski rule of five for drug-likeness and showed promising intestinal absorption and aqueous solubility via computational admet studies making them promising hits for further optimization.
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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) | 0.000 | 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) | 0.001 | 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".