Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders

22 December 2019

Papers citing "Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders"

8 / 8 papers shown

Title
Foundation Inference Models for Markov Jump Processes David Berghaus K. Cvejoski Patrick Seifner C. Ojeda Ramses J. Sanchez 47 1 0 10 Jun 2024
Neural Markov Jump Processes Patrick Seifner Ramses J. Sanchez BDL 45 7 0 31 May 2023
Fast conformational clustering of extensive molecular dynamics simulation data Simon Hunkler K. Diederichs O. Kukharenko Christine Peter 25 9 0 11 Jan 2023
Learning Geometrically Disentangled Representations of Protein Folding Simulations N. Joseph Tatro Payel Das Pin-Yu Chen Vijil Chenthamarakshan Rongjie Lai AI4CE 29 0 0 20 May 2022
EXoN: EXplainable encoder Network SeungHwan An Hosik Choi Jong-June Jeon BDL DRL 34 5 0 23 May 2021
Chasing Collective Variables using Autoencoders and biased trajectories Zineb Belkacemi P. Gkeka T. Lelièvre G. Stoltz AI4CE 8 60 0 22 Apr 2021
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins A. Ramanathan Henglong Ma Akash Parvatikar C. Chennubhotla AI4CE 31 40 0 01 Dec 2020
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 111 357 0 30 Oct 2017