In Silico Prediction of Antibacterial Activity of Quinolone Derivatives
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".