2,4-dichloro-6-(1,4,5-triphenyl-1 <i>H</i> -imidazol-2-yl) phenol: synthesis, DFT analysis, Molecular docking, molecular dynamics, ADMET properties against COVID-19 main protease (Mpro: 6WCF/6Y84/6LU7)
Bibliographic record
Abstract
New derivatives of 2,4-dichloro-6-(1,4,5-triphenyl-1H-imidazol-2-yl) phenol (DPIP) have been successfully synthesised and characterised using spectral methods such as FT-IR, 1H NMR and 13C NMR. Density functional theory (DFT) approach at B3LYP/6-311 G (d, p) level of theory is used to determine optimised bond parameters and single crystal XRD investigation of related derivatives confirms the structure of DPIP bond parameters. The single crystal XRD measurements and the optimised geometrical parameters produced by the DFT calculation agree well. The FT-IR bands seen in the experiment were attributed to distinct normal modes of the molecule. Frontier molecular orbital computations described the molecule stability, chemical reactivity and charge transfer. Atomic charges determined via Mulliken population analysis on the different DPIP atoms. MEP, which is mapped to the electron density surfaces, has discovered potential reactive sites of the molecule. The reported molecule is used as a potential NLO material since it has a high μβ0 value. Binding affinities were discovered using molecular docking against the COVID-19 major protease (Mpro: 6WCF/6Y84/6LU7). The behaviour of the complex structure formed by the Covid-19 protein under in silico physiological conditions was then confirmed by a 100 ns molecular dynamic simulation which looked at the structure stability over time and revealed a stable conformation and binding pattern in an environment of imidazole derivatives. Furthermore, favourable to moderate anti-viral activity was revealed by an in-silico analysis that anticipated the compound absorption, distribution, metabolism, excretion and toxicity profiles (ADMET).
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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".