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bioRxiv · 10.1101/2025.03.05.641673

k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space

Rovers, E. M.·Thudi, A.·Maddison, C.·Schapira, M.
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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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Rovers, E. M., Thudi, A., Maddison, C., Schapira, M.. 2025-03-11. k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space. https://doi.org/10.1101/2025.03.05.641673

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