Intermolecular Forces in Bioactive Resveratrol Complexes with Alcohols: A Study of Stability and Electronic Structure
Bibliographic record
Abstract
Noncovalent interactions, particularly hydrogen bonding, play a pivotal role in determining the structural stability and functional properties of molecules, including bioactive compounds like resveratrol. This study focuses on the hydrogen-bonding behavior and other noncovalent interactions in gas-phase resveratrol-ethanol (EtOH) and resveratrol-methanol (MtOH) complexes, referred to as System 1 and System 2, respectively. These systems were optimized using the ωB97XD functional and cc-pVDZ basis set, providing a detailed picture of their stability and intermolecular interactions. By employing advanced methods such as Domain-Based Local Pair Natural Orbital Coupled Cluster (DLPNO-CCSD)(T) for energy decomposition, natural bond orbital (NBO) for charge analysis, atoms in molecule (AIM) for electron density topology, and noncovalent interaction (NCI) techniques, we decompose interaction energies into meaningful components like electrostatic, dispersion, and exchange-repulsion. The findings indicate that, while hydrogen bonding contributes to the stability of these complexes, London dispersion and other attractive interactions are substantial factors as well. The resveratrol-EtOH and resveratrol-MtOH systems demonstrate a robust electronic environment with significant contributions from various intermolecular forces, underscoring the importance of noncovalent interactions in stabilizing bioactive compounds. This study adds to our understanding of molecular interactions in resveratrol complexes, with potential implications for medicinal chemistry and material science, particularly where solvation effects are critical.
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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) | 0.002 | 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".