Striving to Understand the Properties of Universal Nucleobases: A Computational Study of Azole Carboxamides
Bibliographic record
Abstract
Density functional theory (DFT) is used to study the properties of a series of azole carboxamides in attempts to better understand why these molecules do not have an equal affinity for all natural DNA (RNA) nucleobases, which is an important criterion for universal bases. The thermodynamics and kinetics for bond rotations that afford four azole carboxamide conformers, which each bind to a different natural (DNA) base, are studied. It is concluded that a particular conformer of some azole carboxamides is favorably stabilized; therefore, these molecules will likely preferentially bind to a particular natural base. The geometries and binding energies are calculated for complexes formed between azole carboxamides and natural bases. Our calculations indicate that some complexes are highly distorted and therefore likely reduce the stability of duplexes. Our calculations also indicate that azole carboxamides bind to natural bases with varying affinities. Furthermore, the azole carboxamide binding interactions are generally significantly less than those in the corresponding natural base pair, with the exception of the thymine (or uracil) azole carboxamide complexes. Our calculations provide insight into interactions between azole carboxamides and the natural bases and allow suggestions to be made regarding why these compounds do not function as universal nucleobases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".