Computational investigation of hydrazone derivatives as potential COVID-19 receptor inhibitors: DFT analysis, molecular docking, dynamics simulations, topological indices and ADMET profiling
Bibliographic record
Abstract
A molecule (E)-N′-((E)-2-(2-(furan-3-carbonyl)hydrazono)-1,2-diphenylethylidene) furan-2-carbohydrazide (DPFC) plays a significant role in the treatment of SARS-CoV-2. The current study investigated the molecular structure of the titled compound through a DFT method. The geometric parameters obtained theoretically closely match experimental findings. Various calculations were conducted, including dipole moment, polarizability, hyperpolarizability (NLO), occupied and unoccupied molecular orbitals (HOMO-LUMO), charge localisation and delocalisation (NBO), molecular stability, and the chemical activity region (MEP) of the molecule. The interaction between the DPFC ligand and COVID-19 receptors (6WCF/6Y84/6LU7) was investigated through molecular docking to elucidate the binding modes of this compound at the active sites. Molecular dynamics simulation was employed on the COVID-19 main protease (Mpro: 6WCF/6Y84/6LU7) to discern the factors influencing the inhibitory effect and the stability of interaction under dynamic conditions. Molecular descriptors play a significant role in molecular structural analysis by investigating quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".