Quantum Chemical Associations Ligand−Residue: Their Role to Predict Flavonoid Binding Sites in Proteins
Bibliographic record
Abstract
A novel approach is applied for the prediction of potential binding sites in ligand-protein interactions. This methodology introduces an integral strategy based on the calculation of protein geometrical parameters and the use of a quantum mechanical descriptor, Binding Local Site (B(LS)). A screening of the most likely cavities in the protein crystal structure is carried out where the analysis of geometric cavities is performed, and the virtual centers for binding (VCB) are located. The VCB surrounding amino acid residues (AA) are evaluated through the calculation of the B(LS) by using the theoretical affinity order between the ligand and each AA. It includes a quantum scoring function based on the ligand-AA association energies and entropies. A contribution to the understanding of flavonoid-protein interactions is provided as well. The new bioinformatic strategy makes good predictions for flavonoid ligands. The calculated binding sites are quite in agreement with the crystal binding sites of 10 flavonoid binding proteins. This is a contribution of quantum mechanics in some phases of in silico drug design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".