Synthesis, Urease Inhibition, Molecular Docking, and Optical Analysis of a Symmetrical Schiff Base and Its Selected Metal Complexes
Bibliographic record
Abstract
Designing and developing small organic molecules for use as urease inhibitors is challenging due to the need for ecosystem sustainability and the requirement to prevent health risks related to the human stomach and urinary tract. Moreover, imaging analysis is widely utilized for tracking infections in intracellular and in vivo systems, which requires drug molecules with emissive potential, specifically in the low-energy region. This study comprises the synthesis of a Schiff base ligand and its selected transition metals to evaluate their UV/fluorescence properties, inhibitory activity against urease, and molecular docking. Screening of the symmetrical cage-like ligand and its metal complexes with various eco-friendly transition metals revealed significant urease inhibition potential. The IC50 value of the ligand for urease inhibition was 21.80 ± 1.88 µM, comparable to that of thiourea. Notably, upon coordination with transition metals, the ligand–nickel and ligand–copper complexes exhibited even greater potency than the reference compound, with IC50 values of 11.8 ± 1.14 and 9.31 ± 1.31 µM, respectively. The ligand–cobalt complex exhibited an enzyme inhibitory potential comparable with thiourea, while the zinc and iron complexes demonstrated the least activity, which might be due to weaker interactions with the investigated protein. Meanwhile, all the metal complexes demonstrated a pronounced optical response, which could be utilized for fluorescence-guided targeted drug delivery applications in the future. Molecular docking analysis and IC50 values from in vitro urease inhibition screening showed a trend of increasing activity from compounds 7d to 7c to 7b. Enzyme kinetics studies using the Lineweaver–Burk plot indicated mixed-type inhibition against 7c and non-competitive inhibition against 7d.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".