k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space
Bibliographic record
Abstract
Abstract Molecular dynamics (MD) simulations are computationally expensive, a limiting factor when simulating biomolecular systems. Adaptive sampling approaches can accelerate the exploration of 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 neighbour (k-NN) graph of the sampled conformational space to preferentially launch explorations from boundary states. We term this approach k-Nearest Neighbor Adaptive Sampling (kNN-AS) and show it has 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 better suited to complex real-world applications where the dimension and shape of the energy landscape is unknown.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".