Deep learning-guided adaptive sampling with uncertainty rewards enhances exploration in molecular dynamics simulations
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Abstract Methods development for the rapid exploration of the conformational ensemble of biological molecules remains an active area of research due to the difficulty of sampling rare state transitions in Molecular Dynamics (MD) simulations. In recent years, an increasing number of studies have exploited Machine Learning (ML) models to guide and analyze MD trajectories. Notably, diverse ML models have been developed to approximate optimal biasing potentials to force rare state transitions. On the other hand, Deep Neural Network (DNN) models have been proposed to extract the kinetic properties of a simulated system. In this work, we show that the latter type of model can accelerate the exploration of a thermodynamic ensemble without introducing biasing forces. For this purpose, we combined a VAMPNet (a DNN model that learns transformations to maximize the VAMP-2 score of a set of trajectories) with different adaptive sampling approaches. In brief, we propose an iterative procedure where a reward function selects restarting conformations from the latent space learned by a VAMPNet which is then refined by training on the collected data. Since DNNs do not produce isometric transformations in general, we validated our assumption that reward functions based on distance metrics tend to perform poorly against uncertainty-based rewards in latent spaces. We support our observations through empirical results obtained on typical test systems, which show up to 100% improvement in exploration.
