Design, Synthesis, and Molecular Docking of Novel Benzothiazinone Derivatives as DprE1 Inhibitors with Potential Antitubercular Activities
Bibliographic record
Abstract
Abstract Objective: As a possible antitubercular agent, we disclose in this study the design and synthesis of a novel series of benzothiazinone derivatives (Va–Vi), contributing to the worldwide fight to eradicate TB, one of the deadliest infectious killers in the world. Methods: The newly synthesized benzothiazinone derivatives were characterized using various spectroscopic and elemental analysis techniques. The antituberculosis activity of the synthesized benzothiazinone derivatives was evaluated against drug-sensitive Mtb H37Rv and MDR-TB strains. To explain their inhibitory qualities, potent compounds underwent molecular docking studies. The synthetic molecules’ ability to function as lead-like molecules and the drug-likeness of the compounds were computed using the SwissADME online tool. Results and Discussion: With a MIC of 0.01 and 0.21 µM, respectively, compound (Vi) showed the most promising antitubercular efficacy against drug-sensitive Mtb H37Rv and MDR-TB strains. Four of the nine studied compounds had strong DprE1 inhibitory action, with IC50 values ranging from 0.02 to 0.79 μM. The molecular docking findings indicated that these compounds had a high docking score and a strong binding affinity to the target DprE1 protein’s active pocket. Conclusions: The current study demonstrated the potential significance of novel benzothiazinone derivatives as antitubercular prospects, and further investigation into optimization may lead to the creation of new antitubercular medication candidates.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".