Pyrrolo[1,2-a]pyrazine-4,7-dicarboxylates: Synthesis, structural modification, bioactivity prediction, antimicrobial properties, and docking studies
Bibliographic record
Abstract
A three-step method for obtaining pyrrolo[1,2-a]pyrazine-4,7-dicarboxylates was presented. The method involves the N-alkylation of 5-formylpyrrole-3-carboxylates with bromoacetate, followed by the aminoalkenylation of the N-alkoxycarbonylmethyl group using dimethylformamide di-tert-butyl acetal, and further annulation of the pyrazine ring in the presence of ammonium acetate. Procedures for selective hydrolysis, halogenation, arylation, and alkynylation of the synthesized dicarboxylates were described. The in silico evaluation of the potential bioactivity of the synthesized dicarboxylates 4a–f, dicarboxylic acids 7a–c,e, halogenated dicarboxylates 8f–j, and dicarboxylic acids 10a–e was carried out. As seen from the screening of antimicrobial activity, the synthesized compounds 7a–e, 8c,f–j, 10a–e exhibit inhibitory and bactericidal activity against several bacteria and fungi. The highest activity against Klebsiella pneumonia, Staphylococcus aureus, and Bacillus subtilis has been established for the compound 8f with a MIC of 15.625 µg/mL, and the highest antifungal activity against Candida albicans was found for the compounds 8f, 8g, and 8i (МІС=15.625 µg/mL). The molecular docking data show that the compound 8i has the highest affinity to the ThiM Klebsiella pneumoniae kinase, and compounds 8i, 8j are noted for their highest affinity to the DNA gyrase from Staphylococcus aureus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".