Bibliographic record
Abstract
Although the ability of different molecules to crystallize in a single solid has been known for a long time [1], some of these materials became a subject of intense studies in the last decade due to pharmaceutical and other applications [2]. Short peptides may become a very useful class of co-crystallization agents due to their wide diversity, eco-friendliness and biocompatibility [3]. In this work, we screened the dipeptide leucyl-alanine (LA, see Figure) for the ability to form co-crystals with a variety of solid bioactive compounds. Solvent-assisted grinding of two compounds was followed by a powder X-ray diffraction test. The tested bioactive compounds were found to co-crystallize successfully with the peptide when they possessed both a hydrophobic part and strong hydrogen-bonding functionality. They were primarily derivatives of benzene, phenol, pyridine, pyrazine, quinoline and isoquinoline. Nearly all compounds with an amine or amide group formed a co-crystal, whereas most carboxylic acids did not form a new phase. For the successful combinations, single crystals were obtained when possible and studied using the single-crystal X-ray diffraction analysis. To our surprise, many of the co-crystals formed contained more than the two intended components due to the incorporation of the organic solvent and/or water. For example, one of the co-crystals studied displayed a complex hydrogen bonding framework built by four types of molecules: LA, 8-quinolinecarboxylic acid, ethanol and water in a 1:0.5:0.5:0.5 ratio.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".