Computational screening of antiviral candidates for Monkeypox virus DNA polymerase and A42R protein
Bibliographic record
Abstract
Monkeypox virus (MPXV) is emerging as a major concern in the field of infectious diseases. Current treatments are limited, highlighting the need for new therapeutic options. The use of computational methods, such as molecular docking and molecular dynamic (MD) simulations, is a valuable approach in identifying potential compounds that can target specific proteins of the virus, like the DNA polymerase and profilin-like protein profilin-like protein A42R (A42R) in this case, with the aim of controlling the disease. Our study focused on screening various libraries of compounds for predicted binding to MPXV DNA Polymerase (DPol) and A42R proteins, with the top-performing molecules identified based on their docking scores. Among these, Dorsilurin K and Mangostin in complex with DPol, whereas [2-oxo-2-[3-(3,4,5,6-tetrahydro-2H-azepin-7-ylsulfamoyl)anilino]ethyl] 3,5-dimethylbenzoate and N-[4-[2-[4-(4-methylphenyl)sulfonylpiperazin-1-yl]-2-oxoethoxy]phenyl]furan-2-carboxamide in complex with A42R stand out with notably high docking scores, suggesting they may have a good affinity for binding to the DPol and A42R proteins of MPXV respectively. MD simulations confirmed the stability of these ligand-protein complexes followed by evaluation of the ADMET and oral bioavailability analysis. However, it is important that computational methods can suggest promising candidates, in vitro and eventually in vivo studies are essential to validate these therapeutic candidates. Further studies on these compounds will provide insights into their efficacy, safety, and potential side effects. In conclusion, this study offers promising avenues for developing potential treatments for MPXV. If the identified compounds prove effective in further studies, it could be a significant breakthrough in managing this zoonotic disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".