Exploring the Chemical Space of Mycobacterial Oxidative Phosphorylation Inhibitors Using Molecular Modeling
Bibliographic record
Abstract
Mycobacteria are opportunistic intracellular pathogens that have plagued humans and other animals throughout history and still are today. They manipulate and hijack phagocytic cells of immune systems, enabling them to occupy this peculiar infection niche. Mycobacteria exploit a plethora of mechanisms to resist antimicrobials (e. g., waxy cell walls, efflux pumps, target modification, biofilms, etc.) thereby evolving into superbugs, such as extensively drug-resistant tuberculosis (XDR TB) bacilli and the emerging pathogenic Mycobacterium abscessus complex. This review summarizes the mechanisms of action of some of the surging antimycobacterial strategies. Exploiting the fact that mycobacteria are obligate aerobes and the differences between their oxidative phosphorylation pathways versus their human counterpart opens a promising avenue for drug discovery. The polymorphism of respiratory complexes across mycobacterial pathogens imposes challenges on the repositioning of antimycobacterial agents to battle the rise in nontuberculous mycobacterial infections. In silico strategies exploiting mycobacterial respiratory machinery data to design novel therapeutic agents are touched upon. The potential druggability of mycobacterial respiratory elements is reviewed. Future research addressing the health challenges associated with mycobacterial pathogens is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".