Exploring the anticancer and antibacterial potential of naphthoquinone derivatives: a comprehensive computational investigation
Bibliographic record
Abstract
This study investigates the potential of 2-(4-butylbenzyl)-3-hydroxynaphthalene-1,4-dione (11) and its 12 derivatives as anticancer and biofilm formation inhibitors for methicillin-resistant staphylococcus aureus using in silico methods. The study employed various computational methods, including molecular dynamics simulation molecular docking, density functional theory, and global chemical descriptors, to evaluate the interactions between the compounds and the target proteins. The docking results revealed that compounds 9, 11, 13, and ofloxacin exhibited binding affinities of −7.6, −7.9, −7.5, and −7.8 kcal mol−1, respectively, against peptide methionine sulfoxide reductase msrA/msrB (PDB: 3E0M). Ligand (11) showed better inhibition for methicillin-resistant staphylococcus aureus msrA/msrB enzyme. The complex of the 3E0M-ligand 11 remained highly stable across all tested temperatures (300, 305, 310, and 320 K). Principal Component Analysis (PCA) was employed to evaluate the behavior of the complex at various temperatures (300, 305, 310, and 320 K), demonstrating a total variance of 85%. Convergence was confirmed by the eigenvector’s cosine content value of 0.43, consistently displaying low RMSD values, with the minimum observed at 310 K. Furthermore, ligand 11 emerges as the most promising candidate among the compounds examined, showcasing notable potential when considering a combination of in vitro, in vivo, and now in silico data. While the naphthoquinone derivative (11) remains the primary candidate based on comprehensive in silico studies, further analysis using Frontier molecular orbital (FMO) suggests while the Egap value of compound 11 (2.980 eV) and compound 13 (2.975 eV) is lower than ofloxacin (4.369 eV), indicating their potential, so it can be a statement that compound 13 can also be investigated in further research.
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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) | 0.002 | 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 | 0.001 |
| 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".