Molecular design, ADMET evaluation, and molecular dynamic simulation of some potent sulfonamide-based compounds as anti-tuberculosis agents
Bibliographic record
Abstract
Tuberculosis (TB) is a chronic bacterial infection caused by Mycobacterium tuberculosis, affecting millions of people worldwide. Despite the availability of anti-TB drugs, the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB strains has become a significant public health concern. Therefore, there is an urgent need to discover and develop new anti-TB agents with improved efficacy and reduced toxicity. In this study, we use computational methods to identify and design new chemical entities that could be effective anti-tuberculosis agents. The developed model meets numerous organizations' recommendations for statistically valid QSAR, with R² values of 0.990 and 0.978 for internal and external validation, respectively. The designed molecules 29f and 29l exhibit higher binding affinities (∆G) of -37.15 kcal/mol and -37.31 kcal/mol, respectively, when compared to rifampin as the reference drug (RC) with ∆G of -24.13 kcal/mol, which indicates that it is more stable than RC. ADMET evaluation shows improved therapeutic qualities of these newly developed compounds, demonstrated by a reduced maximum acceptable dosage. Further validation was carried out via molecular dynamic simulation, and these analyses prove that changes in protein conformation and dynamics as a response to ligand binding provide an in-depth view of the molecular mechanisms that determine the ligand’s efficacy and protein roles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".