相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors (Publication with Expression of Concern. See vol. 23, 2022)
Gang Xu et al.
BRIEFINGS IN BIOINFORMATICS (2022)
Adaptive Monte Carlo augmented with normalizing flows
Marylou Gabrie et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2022)
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Sam Bond-Taylor et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)
Normalizing Flows: An Introduction and Review of Current Methods
Ivan Kobyzev et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)
Multi-body effects in a coarse-grained protein force field
Jiang Wang et al.
JOURNAL OF CHEMICAL PHYSICS (2021)
DeepBAR: A Fast and Exact Method for Binding Free Energy Computation
Xinqiang Ding et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)
Machine learning implicit solvation for molecular dynamics
Yaoyi Chen et al.
JOURNAL OF CHEMICAL PHYSICS (2021)
Highly accurate protein structure prediction with AlphaFold
John Jumper et al.
NATURE (2021)
Backmapping coarse-grained macromolecules: An efficient and versatile machine learning approach
Wei Li et al.
JOURNAL OF CHEMICAL PHYSICS (2020)
Targeted free energy estimation via learned mappings
Peter Wirnsberger et al.
JOURNAL OF CHEMICAL PHYSICS (2020)
Machine learning approach for accurate backmapping of coarse-grained models to all-atom models
Yaxin An et al.
CHEMICAL COMMUNICATIONS (2020)
The SIRAH 2.0 Force Field: Altius, Fortius, Citius
Matias R. Machado et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Jiang Wang et al.
ACS CENTRAL SCIENCE (2019)
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noe et al.
SCIENCE (2019)
Neural Importance Sampling
Thomas Mueller et al.
ACM TRANSACTIONS ON GRAPHICS (2019)
EncoderMap: Dimensionality Reduction and Generation of Molecule Conformations
Tobias Lemke et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo et al.
PHYSICAL REVIEW D (2019)
Neural Network Renormalization Group
Shuo-Hui Li et al.
PHYSICAL REVIEW LETTERS (2018)
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman et al.
PLOS COMPUTATIONAL BIOLOGY (2017)
Globally and Locally Consistent Image Completion
Satoshi Iizuka et al.
ACM TRANSACTIONS ON GRAPHICS (2017)
SIRAH tools: mapping, backmapping and visualization of coarse-grained models
Matias R. Machado et al.
BIOINFORMATICS (2016)
HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
S. Doerr et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Identification of slow molecular order parameters for Markov model construction
Guillermo Perez-Hernandez et al.
JOURNAL OF CHEMICAL PHYSICS (2013)
Markov models of molecular kinetics: Generation and validation
Jan-Hendrik Prinz et al.
JOURNAL OF CHEMICAL PHYSICS (2011)
Crystal Structure of a Ten-Amino Acid Protein
Shinya Honda et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2008)
Practical conversion from torsion space to Cartesian space for in silico protein synthesis
J Parsons et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2005)
Energy landscape of a small peptide revealed by dihedral angle principal component analysis
YG Mu et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2005)