VAMPnets swMATH ID: 32927 Software Authors: Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé Description: 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. Homepage: https://arxiv.org/abs/1710.06012 Source Code: https://github.com/markovmodel/deeptime/tree/master/vampnet Keywords: Machine Learning; arXiv_stat.ML; Biological Physics; arXiv_physics.bio-ph; Chemical Physics; arXiv_physics.chem-ph; arXiv_physics.comp-ph Related Software: PDE-Net; torchdiffeq; TensorFlow; SINDy; GAIO; PRMLT; Adam; AlexNet; ImageNet; SINDy-PI; DeepXDE; HODMD; odmd; DeepONet; PyDMD; rsvd; EnKF; U-Net; PyEMMA; MCTDH Cited in: 22 Publications all top 5 Cited by 44 Authors 8 Brunton, Steven L. 8 Kutz, J. Nathan 4 Klus, Stefan 4 Xiu, Dongbin 3 Nüske, Feliks 3 Wu, Kailiang 2 Clementi, Cecilia 2 Kaiser, Eurika 2 Noé, Frank 2 Qin, Tong 2 Rudy, Samuel H. 2 Schütte, Christof 1 Alla, Alessandro 1 Bittracher, Andreas 1 Bramburger, Jason J. 1 Budišić, Marko 1 Champion, Kathleen P. 1 Chen, Zhen 1 Chou, Ching-Shan 1 Dinner, Aaron R. 1 Dow, Douglas 1 Gelß, Patrick 1 Gin, Craig 1 Gribonval, Rémi 1 Hamzi, Boumediene 1 Husic, Brooke E. 1 Kaheman, Kadierdan 1 Kaltenbach, Sebastian 1 Kamb, Mason 1 Koltai, Péter 1 Koutsourelakis, Phaedon-Stelios 1 Kutyniok, Gitta 1 Lücke, Marvin 1 Lusch, Bethany 1 Mollenhauer, Mattes 1 Nielsen, Morten 1 Niemann, Jan-Hendrik 1 Peitz, Sebastian 1 Su, Wei-Hung 1 Thiede, Erik H. 1 Tian, Wenchong 1 Voigtlaender, Felix 1 Weare, Jonathan Quincy 1 Webber, Robert J. all top 5 Cited in 14 Serials 3 Journal of Computational Physics 3 Physica D 3 Journal of Nonlinear Science 3 SIAM Journal on Applied Dynamical Systems 1 Bulletin of Mathematical Biology 1 Constructive Approximation 1 Journal of Scientific Computing 1 European Journal of Applied Mathematics 1 SIAM Review 1 SIAM Journal on Scientific Computing 1 Chaos 1 Computational Methods in Applied Mathematics 1 Proceedings of the Royal Society of London. A. Mathematical, Physical and Engineering Sciences 1 SIAM Journal on Mathematics of Data Science all top 5 Cited in 15 Fields 11 Dynamical systems and ergodic theory (37-XX) 11 Numerical analysis (65-XX) 7 Computer science (68-XX) 3 Partial differential equations (35-XX) 3 Operator theory (47-XX) 3 Probability theory and stochastic processes (60-XX) 2 Linear and multilinear algebra; matrix theory (15-XX) 2 Ordinary differential equations (34-XX) 2 Statistical mechanics, structure of matter (82-XX) 2 Systems theory; control (93-XX) 1 Approximations and expansions (41-XX) 1 Harmonic analysis on Euclidean spaces (42-XX) 1 Statistics (62-XX) 1 Mechanics of deformable solids (74-XX) 1 Biology and other natural sciences (92-XX) Citations by Year