228 related articles for article (PubMed ID: 37382437)
1. An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.
Qiu Y; O'Connor MS; Xue M; Liu B; Huang X
J Chem Theory Comput; 2023 Jul; 19(14):4728-4742. PubMed ID: 37382437
[TBL] [Abstract][Full Text] [Related]
2. Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems.
Meng L; Sheong FK; Zeng X; Zhu L; Huang X
J Chem Phys; 2017 Jul; 147(4):044112. PubMed ID: 28764388
[TBL] [Abstract][Full Text] [Related]
3. A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules.
Bandyopadhyay S; Mondal J
J Chem Phys; 2021 Sep; 155(11):114106. PubMed ID: 34551528
[TBL] [Abstract][Full Text] [Related]
4. Kinetic network study of the diversity and temperature dependence of Trp-Cage folding pathways: combining transition path theory with stochastic simulations.
Zheng W; Gallicchio E; Deng N; Andrec M; Levy RM
J Phys Chem B; 2011 Feb; 115(6):1512-23. PubMed ID: 21254767
[TBL] [Abstract][Full Text] [Related]
5. An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling.
Wang W; Liang T; Sheong FK; Fan X; Huang X
J Chem Phys; 2018 Aug; 149(7):072337. PubMed ID: 30134698
[TBL] [Abstract][Full Text] [Related]
6. Automatic state partitioning for multibody systems (APM): an efficient algorithm for constructing Markov state models to elucidate conformational dynamics of multibody systems.
Sheong FK; Silva DA; Meng L; Zhao Y; Huang X
J Chem Theory Comput; 2015 Jan; 11(1):17-27. PubMed ID: 26574199
[TBL] [Abstract][Full Text] [Related]
7. RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics.
Gu H; Wang W; Cao S; Unarta IC; Yao Y; Sheong FK; Huang X
Phys Chem Chem Phys; 2022 Jan; 24(3):1462-1474. PubMed ID: 34985469
[TBL] [Abstract][Full Text] [Related]
8. Assessment and optimization of collective variables for protein conformational landscape: GB1
Ahalawat N; Mondal J
J Chem Phys; 2018 Sep; 149(9):094101. PubMed ID: 30195312
[TBL] [Abstract][Full Text] [Related]
9. Explore Protein Conformational Space With Variational Autoencoder.
Tian H; Jiang X; Trozzi F; Xiao S; Larson EC; Tao P
Front Mol Biosci; 2021; 8():781635. PubMed ID: 34869602
[TBL] [Abstract][Full Text] [Related]
10. Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders.
Ghorbani M; Prasad S; Klauda JB; Brooks BR
J Chem Phys; 2021 Nov; 155(19):194108. PubMed ID: 34800961
[TBL] [Abstract][Full Text] [Related]
11. Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.
Konovalov KA; Unarta IC; Cao S; Goonetilleke EC; Huang X
JACS Au; 2021 Sep; 1(9):1330-1341. PubMed ID: 34604842
[TBL] [Abstract][Full Text] [Related]
12. Transition path theory analysis of c-Src kinase activation.
Meng Y; Shukla D; Pande VS; Roux B
Proc Natl Acad Sci U S A; 2016 Aug; 113(33):9193-8. PubMed ID: 27482115
[TBL] [Abstract][Full Text] [Related]
13. LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories.
Tian H; Jiang X; Xiao S; La Force H; Larson EC; Tao P
J Chem Inf Model; 2023 Jan; 63(1):67-75. PubMed ID: 36472885
[TBL] [Abstract][Full Text] [Related]
14. Assessments of Variational Autoencoder in Protein Conformation Exploration.
Xiao S; Song Z; Tian H; Tao P
J Comput Biophys Chem; 2023 Jun; 22(4):489-501. PubMed ID: 38826699
[TBL] [Abstract][Full Text] [Related]
15. The folding mechanism and key metastable state identification of the PrP127-147 monomer studied by molecular dynamics simulations and Markov state model analysis.
Zhou S; Wang Q; Wang Y; Yao X; Han W; Liu H
Phys Chem Chem Phys; 2017 May; 19(18):11249-11259. PubMed ID: 28406520
[TBL] [Abstract][Full Text] [Related]
16. The Adaptive Path Collective Variable: A Versatile Biasing Approach to Compute the Average Transition Path and Free Energy of Molecular Transitions.
Pérez de Alba Ortíz A; Vreede J; Ensing B
Methods Mol Biol; 2019; 2022():255-290. PubMed ID: 31396907
[TBL] [Abstract][Full Text] [Related]
17. Insights into the dynamics of HIV-1 protease: a kinetic network model constructed from atomistic simulations.
Deng NJ; Zheng W; Gallicchio E; Levy RM
J Am Chem Soc; 2011 Jun; 133(24):9387-94. PubMed ID: 21561098
[TBL] [Abstract][Full Text] [Related]
18. Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning.
Narayan B; Yuan Y; Fathizadeh A; Elber R; Buchete NV
Prog Mol Biol Transl Sci; 2020; 170():215-237. PubMed ID: 32145946
[TBL] [Abstract][Full Text] [Related]
19. Transition paths of Met-enkephalin from Markov state modeling of a molecular dynamics trajectory.
Banerjee R; Cukier RI
J Phys Chem B; 2014 Mar; 118(11):2883-95. PubMed ID: 24571787
[TBL] [Abstract][Full Text] [Related]
20. Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.
Chen H; Roux B; Chipot C
J Chem Theory Comput; 2023 Jul; 19(14):4414-4426. PubMed ID: 37224455
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]