Molecular Recognition of Hydrophilic Molecules in Water by Combining the Hydrophobic Effect with Hydrogen Bonding
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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 106 M–1.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it