VAMPnets: Deep learning of molecular kinetics. 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.
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References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
- Chen, Zhen; Wu, Kailiang; Xiu, Dongbin: Methods to recover unknown processes in partial differential equations using data (2020)
- Kamb, Mason; Kaiser, Eurika; Brunton, Steven L.; Kutz, J. Nathan: Time-delay observables for Koopman: theory and applications (2020)
- Wu, Hao; Noé, Frank: Variational approach for learning Markov processes from time series data (2020)
- Wu, Kailiang; Qin, Tong; Xiu, Dongbin: Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data (2020)
- Champion, Kathleen P.; Brunton, Steven L.; Kutz, J. Nathan: Discovery of nonlinear multiscale systems: sampling strategies and embeddings (2019)
- Klus, Stefan; Husic, Brooke E.; Mollenhauer, Mattes; Noé, Frank: Kernel methods for detecting coherent structures in dynamical data (2019)
- Qin, Tong; Wu, Kailiang; Xiu, Dongbin: Data driven governing equations approximation using deep neural networks (2019)
- Rudy, Samuel; Alla, Alessandro; Brunton, Steven L.; Kutz, J. Nathan: Data-driven identification of parametric partial differential equations (2019)
- Rudy, Samuel H.; Nathan Kutz, J.; Brunton, Steven L.: Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (2019)