VAMPnets: Deep learning of molecular kinetics
This addresses the problem of reducing modeling expertise and errors in biomolecular kinetics analysis for researchers in computational biology and chemistry, representing a novel method for a known bottleneck.
The authors tackled the challenge of modeling molecular kinetics from simulations by developing VAMPnets, a deep learning framework that encodes the entire mapping from coordinates to Markov states in an end-to-end pipeline, achieving performance equal to or better than state-of-the-art methods with interpretable few-state models.
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the 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, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the art Markov modeling methods and provides easily interpretable few-state kinetic models.