Exploring the effectiveness of flavone derivatives for treating liver diseases: Utilizing DFT, molecular docking, and molecular dynamics techniques
Bibliographic record
Abstract
In exploring nature's potential in addressing liver-related conditions, this study investigates the therapeutic capabilities of flavonoids. Utilizing in silico methodologies, we focus on flavone and its analogs ( 1 – 14 ) to assess their therapeutic potential in treating liver diseases. Molecular change calculations using density functional theory (DFT) were conducted on these compounds, accompanied by an evaluation of each analog's physiochemical and biochemical properties. The study further assesses these flavonoids' binding effectiveness and locations through molecular docking studies against six target proteins associated with human cancer. Tropoflavin and taxifolin served as reference drugs. The structurally modified flavone analogs ( 1 – 14 ) displayed a broad range of binding affinities, ranging from -7.0 to -9.4 kcal mol⁻¹, surpassing the reference drugs. Notably, flavonoid ( 7 ) exhibited significantly higher binding affinities with proteins Nrf2 (PDB:1 × 2 J) and DCK (PDB:1 × 2 J) (-9.4 and -8.1 kcal mol⁻¹) compared to tropoflavin (-9.3 and -8.0 kcal mol⁻¹) and taxifolin (-9.4 and -7.1 kcal mol⁻¹), respectively. Molecular dynamics (MD) simulations revealed that the docked complexes had a root mean square deviation (RMSD) value ranging from 0.05 to 0.2 nm and a root mean square fluctuation (RMSF) value between 0.35 and 1.3 nm during perturbation. The study concludes that 5,7-dihydroxyflavone ( 7 ) shows substantial promise as a potential therapeutic agent for liver-related conditions. However, further validation through in vitro and in vivo studies is necessary. Key insights from this study include: • Screening of flavanols and their derivatives to determine pharmacological and bioactive properties using ADMET, molinspiration, and pass prediction analysis. • Docking of shortlisted flavone derivatives with proteins having essential functions. • Analysis of the best protein-flavonoid docked complexes using molecular dynamics simulation to determine the flavonoid's efficiency and stability within a system.
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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".