Computational prediction of the supramolecular self-assembling properties of organic molecules: the role of conformational flexibility of amide moieties
Bibliographic record
Abstract
Two families of organic molecules with different backbones have been considered. The first family is based on a macrolactam-like unit that is constrained in a particular conformation. The second family is composed by a substituted central phenyl that allows a larger mobility for its substituents. They have however a common feature, three amide moieties (within the cycle for the macrolactam-like molecule and as substituents for the phenyl) that permit hydrogen bonding when molecules are stacked. In this study we propose a computational protocol to unravel the ability of the different families to self-assemble into organic nanotubes. Starting from the monomer and going towards larger assemblies like dimers, trimers, and pentamers we applied the different protocols to rationalize the behavior of the different assemblies. Both structures and thermodynamics were investigated to give a complete picture of the process. Thanks to the combination of a quantum mechanics approach and molecular dynamics simulations along with the use of tailored tools (non covalent interaction visualization) and techniques (umbrella sampling), we have been able to differentiate the two families and highlight the best candidate for self-assembling purposes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".