A comprehensive investigation on one-pot synthesis of imidazole derivatives: quantum computational analysis, molecular docking, molecular dynamics simulations and antiviral activity against SARS-CoV-2
Bibliographic record
Abstract
New derivatives of 4-(2-(2-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-4,5-diphenyl-1H-imidazol-1-yl)ethyl)morpholine (DDIM) have been successfully synthesised and characterised using spectral methods such as FT-IR, 1H NMR, and 13C NMR. Density functional theory (DFT) with the B3LYP/6-311G (d, p) level of theory was used to determine optimised bond parameters and single crystal XRD investigations confirmed the structure of DDIM. The results of single crystal XRD measurements aligned well with the optimised geometrical parameters from DFT calculations. Frontier molecular orbital computations provided insights into the molecule's stability, chemical reactivity and charge transfer. Atomic charges were determined using mulliken population analysis. The molecular electrostatic potential (MEP) mapped to electron density surfaces identified potential reactive sites. This molecule shows promise as a potential NLO material due to its high μβ0 value. Binding affinities were determined via molecular docking against the COVID-19 major protease (Mpro: 6WCF/6Y84/6LU7). A 100 ns molecular dynamics simulation under in silico physiological conditions confirmed the stability of the complex structure formed with the COVID-19 protein, revealing a stable conformation and binding pattern in an imidazole derivative environment. Additionally, in-silico analysis predicted favourable to moderate anti-viral activity and anticipated the compound's absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles.
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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".