Exploring the potential of fluoro‐flavonoid derivatives as <scp>anti‐lung</scp> cancer agents: <scp>DFT</scp>, molecular docking, and molecular dynamics techniques
Bibliographic record
Abstract
Abstract The present investigation utilized in silico methodologies to explore the diverse pharmacological activities, toxicity profiles, and chemical reactivity of a series of fluoro‐flavonoid compounds (1–14), which are secondary metabolites of plants known for their broad range of biological effects. A comprehensive strategy is utilized, incorporating methods such as prediction of activity spectra for substances (PASS) prediction, absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessments, and density functional theory (B3LYP) calculations using three basis sets: 6‐31G(d,p), 6‐311G(d,p), and 6‐311++G(d,p). Furthermore, the study employed molecular docking technique to identify target proteins, including HER2 (7JXH), EGFR (4UV7), FPPS (1YQ7), HPGDS (1V40), DCK (1P60), and KEAP1 on Nrf2 (1X2J), for the investigated compounds, with cianidanol and genistein serving as reference drugs for the docking process. The investigated fluoro‐flavonoid compounds exhibited significantly greater binding affinities (ranging from −8.3 to −10.6 kcal mol−1) toward HER2, HPGDS, and KEAP1 compared to the reference drugs, cianidanol and genistein, which displayed binding affinities ranging from −8.4 to −9.4 kcal mol−1. Furthermore, molecular dynamics simulations were conducted to assess the stability of the protein‐ligand interaction, using the root‐mean‐square deviation (RMSD), root‐mean‐square fluctuation (RMSF), Radius of gyration (Rg) parameters and principle component analysis (PCA). Among the tested fluoro‐flavonoid analogs, analog 11 showed a RMSD value of .15 nm with the HER2 protein target, indicating a stable interaction. Based on in silico results, it appears that the fluoro‐flavonoid compound 11 has the potential to serve as a targeted anti‐lung cancer drug. However, additional in vivo and in vitro studies are necessary to confirm this hypothesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".