In Silico Evaluation of Quinolone–Triazole and Conazole–Triazole Hybrids as Promising Antimicrobial and Anticancer Agents
Bibliographic record
Abstract
Cancer remains one of the leading causes of death globally, highlighting the urgent need for novel anticancer therapies with higher efficacy and reduced toxicity. Similarly, the rise in multidrug-resistant pathogens and emerging infectious diseases underscores the critical demand for new antimicrobial agents that target resistant infections through unique mechanisms. This study used computational approaches to investigate twenty quinolone–triazole and conazole–triazole hybrid derivatives as antimicrobial and anticancer agents (1–20) with nine reference drugs. By studying their interactions with 6 bacterial DNA gyrase and 10 cancer-inducing target proteins (E. faecalis, M. tuberculosis, S. aureus, E. coli, M. smegmatis, P. aeruginosa and EGFR, MPO, VEGFR, CDK6, MMP1, Bcl-2, LSD1, HDAC6, Aromatase, ALOX15) and comparing them with established drugs such as ampicillin, cefatrizine, fluconazole, gemcitabine, itraconazole, ribavirin, rufinamide, streptomycin, and tazobactam, compounds 15 and 16 emerged as noteworthy antimicrobial and anticancer agents, respectively. In molecular dynamics simulations, compounds 15 and 16 had the strongest binding at −10.6 kcal mol−1 and −12.0 kcal mol−1 with the crucial 5CDQ and 2Z3Y proteins, respectively, exceeded drug-likeness criteria, and displayed extraordinary stability within the enzyme’s pocket over varied temperatures (300–320 K). In addition, we used density functional theory (DFT) to calculate dipole moments and molecular orbital characteristics and analyze the thermodynamic stability of putative antimicrobial and anticancer derivatives. This finding reveals a well-defined, possibly therapeutic relationship, supported by theoretical and future in vitro and in vivo studies. Compounds 15 and 16, thus, emerged as intriguing contenders in the fight against infectious diseases and cancer.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".