VAMPnets: Deep learning of molecular kinetics
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Here we develop a deep learning framework for molecular kinetics from molecular dynamics (MD) simulation data. There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of complex biomolecular processes, such as protein-drug binding, from high-throughput MD simulations. State-of-the art methods employ a handcrafted data processing pipeline, involving (i) transformation of simulated coordinates into a set of features characterizing the molecular structure, (ii) dimension reduction to collective variables, (iii) clustering the dimension-reduced data, and (iv) estimation of a Markov state model (MSM) or related model of the interconversion rates between molecular structures. This approach demands a substantial amount of modeling expertise, as poor decisions at every step will lead to large modeling errors. Here we employ the recently developed variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states and learns optimal feature transformations, nonlinear dimension reduction, cluster discretization and MSM estimation within a single end-to-end learning framework. Our results, ranging from toy models to protein folding, are competitive or outperform state-of-the art Markov modeling methods and readily provide easily interpretable few-state kinetic models.
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