Molecular dynamics and combined docking studies for the identification of Zaire ebola virus inhibitors
Bibliographic record
Abstract
specifically has the highest fatality rate amongst other species. There is a need for continuous effort towards having therapies, as a single licensed treatment to neutralize the EBOV is yet to come into reality. This present study virtually screened the MCULE database containing almost 36 million compounds against the structure of a Zaire Ebola viral protein (VP) 35 and a consensus scoring of both MCULE and CLCDDW docking programs remarked five compounds as potential hits. These compounds, with binding energies ranging from -7.9 to -8.9 kcal/mol, were assessed for predictions of their physicochemical and bioactivity properties, as well as absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria. The results of the 50 ns molecular dynamics simulations showed the presence of dynamic stability between ligand and protein complexes, and the structures remained significantly unchanged at the ligand-binding site throughout the simulation period. Both docking analysis and molecular dynamics simulation studies suggested strong binding affinity towards the receptor cavity and these selected compounds as potential inhibitors against the Zaire Ebola VP 35. With respect to inhibition constant values, bioavailability radar and other physicochemical properties, compound A (MCULE-1018045960-0-1) appeared to be the most promising hit compound. However, the ligand efficiency and ligand efficiency scale need improvement during optimization, and also validation via in vitro and in vivo studies are necessary to finally make a lead compound in treating Ebola virus diseases. Communicated by Ramaswamy H. Sarma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".