Evaluation of novel pyridoxal isonicotinoyl hydrazone (<scp>PIH</scp>) derivatives as potential <scp>anti‐tuberculosis</scp> agents: An in silico investigation
Bibliographic record
Abstract
Abstract This investigation employed computational methodologies to assess the therapeutic potential of derivatives (1–16) of pyridoxal isonicotinoyl hydrazone (PIH) as potential treatments for tuberculosis. Various computational techniques, including molecular dynamics simulation, molecular docking, density functional theory, and global chemical descriptors, were employed to analyze the interactions between the ligands and target proteins. Docking results indicated that ligands 6, 7, 8, and rifampin exhibited binding affinities of −8.4, −7.4, −9.2, and − 7.2 kcal mol−1, respectively, against mycobacterium tuberculosis enoyl acyl carrier protein reductase (INHA), with ligand 8 demonstrating superior inhibition. Molecular dynamics (MD) simulations were utilized to assess the stability of protein‐ligand interactions. Remarkably, the Root Mean Square Deviation (RMSD) of the INHA‐ligand 8 complex remained minimal, with peak values at .40, .56, .37, and .50 nm at temperatures of 300, 305, 310, and 320 K, respectively. This suggests superior stability compared to the reference drug rifampin and INHA complex, which exhibited an RMSD range of .2 to .8 nm at 300 K. Furthermore, analysis using Frontier Molecular Orbital (FMO) revealed that the Egap value of ligand 8 (4.407 eV) is lower than all the reference drugs except rifampin. This comprehensive theoretical analysis positions ligand 8 as a promising candidate for anti‐tuberculosis drug development, underscoring the need for further exploration through in vitro and in vivo studies.
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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) | 0.001 | 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 | 0.000 |
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".