In Silico Prediction of Antibacterial Activity of Quinolone Derivatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The rising antimicrobial resistance crisis has diminished the effectiveness of traditional antibiotics against pathogenic bacteria. This study addresses this urgent challenge by exploring the antibacterial potential of novel quinolone derivatives ( 1–33 ). Using computational in silico modeling to simulate biological interactions, we aimed to identify candidates with potent antibacterial activity. A total of 33 quinolone derivatives were assessed for their physicochemical properties and effectiveness against a range of clinically relevant pathogens, including methicillin‐resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae , Streptococcus pneumoniae , and Enterococcus faecalis . Molecular docking studies identified compounds 28 , 29 , 32 , and 33 as having notable binding affinities, particularly against MRSA. Further molecular dynamics simulations of compound 29 confirmed its favorable stability and potential for disrupting MRSA, reinforcing the docking results and showing strong alignment with in vitro findings. These findings position compound 29 as a promising lead for developing alternative MRSA therapies and underscore the need for further in vivo studies to evaluate its therapeutic potential.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it