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 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 | 0.001 |
| 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".