Molecular Recognition of Hydrophilic Molecules in Water by Combining the Hydrophobic Effect with Hydrogen Bonding
Bibliographic record
Abstract
During the last half a century, great achievements have been made in molecular recognition in parallel with the invention of numerous synthetic receptors. However, the selective recognition of hydrophilic molecules in water remains a generally accepted challenge in supramolecular chemistry but is commonplace in nature. In an earlier Communication [Huang et al. J. Am. Chem. Soc. 2016, 138, 14550], we reported a pair of endo -functionalized molecular tubes that surprisingly prefer highly hydrophilic molecules over hydrophobic molecules of a similar size and shape. The hydrophobic effect and hydrogen bonding were proposed to be responsible, but their exact roles were not fully elucidated. In this Article, we present a thorough study on the binding behavior of these molecular tubes toward 44 hydrophilic molecules in water. Principal component analysis reveals that the binding strength is weakly correlated to the hydrophobicity, volume, surface area, and dipole moment of guests. Furthermore, molecular dynamics simulations show the hydrophobic effect through releasing the poorly hydrogen-bonded cavity water contributes to the binding of all the hydrophilic molecules, while hydrogen bonding differentiates these molecules and is thus the key to achieve a high selectivity toward certain hydrophilic molecules over other molecules with a similar size and shape. Therefore, a good guest for these molecular tubes should meet the following criteria: the hydrogen-bonding sites should be complementary, and the molecular volume should be large enough to expel all the cavity water but not too large to cause steric hindrance. This rule of thumb may also be used to design a selective receptor for certain hydrophilic molecules. Following these guidelines, a “best-fit” guest was found for the syn -configured molecular tube with a binding constant as high as 10 6 M –1 .
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".