Drug–DNA interaction, a joint DFT-D3/MD study on safranal as an anticancer and DNA nanostructure model
Bibliographic record
Abstract
In this research, using a combination of quantum mechanics and molecular dynamic (MD) simulations, the interaction of safranal (2,6,6-trimethylcyclohexa-1,3-dien-1-carboxaldehyde) as an anti-cancer drug and Dickerson B-DNA was studied. MD simulations were executed for 35 ns in water. Binding energy analysis in three definite parts of the B-DNA and comparison between different contributions of the binding energy shows that the van der Waals energy part of the interaction is impressive among the standard molecular mechanic energy terms. On the basis of Gibbs energies, it is confirmed that the most important interactions in the safranal complex are related to the A–T and C–G rich regions, which is in agreement with the experimental data. Quantum theory of atoms in molecules and natural bond orbital analyses were applied. A diminution in the electronic chemical potential of the safranal–DNA complex in comparison with the isolated DNA, 0.026 and 0.022 au for the S1 region and 0.012 and 0.017 au for the S2 region, was obtained in the gas phase and water, respectively, which increases the complex stability. An enhancement in the electrophilicity character, during the complexation process, shows the electron charge flux between the safranal and DNA, especially in water. The strengths of the CH⋯O bonds at the center of safranal–DNA interaction were also evaluated. A mean value of 0.06 au for the electron density of the bond critical point of the H⋯O in the complex confirms the H-bond formation during the complexation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".