k-Nearest Neighbor Adaptive Sampling, a Simple Tool to Efficiently Explore Conformational Space
Why this work is in the frame
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Bibliographic record
Abstract
Molecular dynamics (MD) simulations are computationally expensive, which is a limiting factor when simulating biomolecular systems. Adaptive sampling approaches can accelerate the exploration of the conformational space by running repeated short MD simulations from well-chosen starting points. Existing approaches to adaptive sampling have been optimized to either guide sampling in a desired direction or explore well-formed convex spaces. Here, we describe a novel adaptive sampling algorithm that leverages a k-nearest neighbor (k-NN) graph of the sampled conformational space to preferentially launch explorations from boundary states. We term this approach k-NN adaptive sampling (kNN-AS) and show state-of-the-art performance on simple and complex artificial energy functions and generalizes well on a protein test case. Implementation of kNN-AS is light, simple, and suited to complex real-world applications where the dimension and shape of the energy landscape is unknown.
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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 | 0.001 | 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 | 0.000 |
| 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