Co-crystals and inclusion compounds of leucyl-containing dipeptides
Bibliographic record
Abstract
Short peptides are ecologically friendly and non-toxic molecules, so they can be safely utilized in green chemistry processes or incorporated in pharmaceuticals and food additives. It has been shown that some dipeptides can form crystals that incorporate other molecules through intermolecular hydrogen bonding and van der Waals interactions[1]. The utilization of such dipeptides for solid state organic synthesis or storage and stabilization of bioactive molecules would be of great practical interest, but the principles that define the successful combinations are not clear. In order to identify what factors lead to complementary pairs of a dipeptide and a second component, a series of leucine-containing dipeptides was screened against 40 organic solvents and solids. Direct or solvent-assisted grinding was used followed by PXRD analysis. It was found that each dipeptide was able to form new phases with some of the utilized reactive and bioactive molecules. The Figure illustrates three experimental powder patterns in the 5-35 2θ degree range. The dipeptide leucyl-valine (1) and the second component 5-acetylsalicylamide (2) combine to form a new crystalline phase (3). After screening was complete, a series of crystallizations was performed and several crystals comprised of both a dipeptide and another molecule have been isolated and studied. A number of structural motifs were observed, although a layered architecture with the second component included in the interlayer space prevailed.
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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".