AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations.

Journal of Chemical Theory and Computation
Adrià PérezGianni De Fabritiis

Abstract

Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

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Citations

Jan 10, 2021·Current Opinion in Structural Biology·Arvind RamanathanS Chakra Chennubhotla
Aug 14, 2020·Journal of Chemical Information and Modeling·Pablo Herrera-NietoGianni De Fabritiis
Sep 1, 2021·Journal of Chemical Theory and Computation·Sergei V Krivov

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