HYDROPHOBE Challenge: A Joint Experimental and Computational Study on the Host–Guest Binding of Hydrocarbons to Cucurbiturils, Allowing Explicit Evaluation of Guest Hydration Free-Energy Contributions
Bibliographic record
Abstract
The host–guest complexation of hydrocarbons (22 guest molecules) with cucurbit[7]uril was investigated in aqueous solution using the indicator displacement strategy. The binding constants (10 3 –10 9 M –1 ) increased with guest size, pointing to the hydrophobic effect and dispersion interactions as driving forces. The measured affinities provide unique benchmark data for the binding of neutral guest molecules. Consequently, a computational blind challenge, the HYDROPHOBE challenge, was conducted to allow a comparison with state-of-the-art computational methods for predicting host–guest affinity constants. In total, three quantum-chemical (QM) data sets and two explicit-solvent molecular dynamics (MD) submissions were received. When searching for sources of uncertainty in predicting the host–guest affinities, the experimentally known hydration energies of the investigated hydrocarbons were used to test the employed solvation models (explicit solvent for MD and COSMO-RS for QM). Good correlations were obtained for both solvation models, but a rather constant offset was observed for the COSMO data, by ca. +2 kcal mol –1, which was traced back to a required reference-state correction in the QM submissions (2.38 kcal mol –1 ). Introduction of the reference-state correction improved the predictive power of the QM methods, particularly for small hydrocarbons up to C5.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".