Bibliographic record
Abstract
Water plays an active role in many fundamental phenomena in cellular systems such as molecular recognition, folding and conformational equilibria, reaction kinetics and phase partitioning. Hence, our ability to account for the energetics of these processes is highly dependent on the models we use for calculating solvation effects. For example, theoretical prediction of protein-ligand binding modes (i.e., docking) and binding affinities (i.e., scoring) requires an accurate description of the change in hydration that accompanies solute binding. In this review, we discuss the challenges of constructing solvation models that capture these effects, with an emphasis on continuum models and on more recent developments in the field. In our discussion of methods, relatively greater attention will be given to boundary element solutions to the Poisson equation and to nonpolar solvation models, two areas that have become increasingly important but are likely to be less familiar to many readers. The other focus will be upon the trending efforts for evaluating solvation models in order to uncover limitations, biases, and potentially attractive directions for their improvement and applicability. The prospective and retrospective performance of a variety of solvation models in the SAMPL blind challenges will be discussed in detail. After just a few years, these benchmarking exercises have already had a tangible effect in guiding the improvement of solvation models.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".