Cargando…

Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks

Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular...

Descripción completa

Detalles Bibliográficos
Autores principales:	Airas, Justin, Ding, Xinqiang, Zhang, Bin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Cold Spring Harbor Laboratory 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515757/ https://www.ncbi.nlm.nih.gov/pubmed/37745447 http://dx.doi.org/10.1101/2023.09.08.556923

Ejemplares similares

Graph neural network based coarse-grained mapping prediction
por: Li, Zhiheng, et al.
Publicado: (2020)

Correction: Graph neural network based coarse-grained mapping prediction
por: Li, Zhiheng, et al.
Publicado: (2021)

Coarse-graining of the dynamics seen in neural networks
por: Ben-Tal, Alona, et al.
Publicado: (2013)

Coarse-grained statistics for attributing criticality to heterogeneous neural networks
por: Corcoran, Thomas Gregory, et al.
Publicado: (2011)

Coarse-Grained Neural Network Model of the Basal Ganglia to Simulate Reinforcement Learning Tasks
por: Drapała, Jarosław, et al.
Publicado: (2022)

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data
por: Ivanov, Mikhail, et al.
Publicado: (2023)

Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation
por: Yang, Xing-Yao, et al.
Publicado: (2023)

Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure
por: Huang, Lan, et al.
Publicado: (2021)

Machine learning coarse grained models for water
por: Chan, Henry, et al.
Publicado: (2019)

Machine learning coarse-grained potentials of protein thermodynamics
por: Majewski, Maciej, et al.
Publicado: (2023)

Transferable Coarse-Grained Potential for De Novo Protein Folding and Design
por: Coluzza, Ivan
Publicado: (2014)

Revealing latent characteristics of mobility networks with coarse-graining
por: Hamedmoghadam, Homayoun, et al.
Publicado: (2019)

Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks
por: Yu, Zongjian, et al.
Publicado: (2022)

Coupling Coarse-Grained to Fine-Grained Models via Hamiltonian Replica Exchange
por: Liu, Yang, et al.
Publicado: (2020)

Fast protein structure comparison through effective representation learning with contrastive graph neural networks
por: Xia, Chunqiu, et al.
Publicado: (2022)

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
por: Wang, Jiang, et al.
Publicado: (2019)

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows
por: Zanardi, Ivan, et al.
Publicado: (2023)

Coarse Grained Parallel Function Evaluation
por: Rulon, P, et al.
Publicado: (1992)

Chaos and coarse graining in statistical mechanics
por: Castiglione, Patrizia, et al.
Publicado: (2008)

The power of coarse graining in biomolecular simulations
por: Ingólfsson, Helgi I, et al.
Publicado: (2014)

Multiconfigurational Coarse-Grained Molecular Dynamics
por: Sharp, Morris E., et al.
Publicado: (2019)

Thermodynamics of the Coarse-Graining Master Equation
por: Schaller, Gernot, et al.
Publicado: (2020)

De Sitter stability and coarse graining
por: Markkanen, T.
Publicado: (2018)

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network
por: Yan, Junyi, et al.
Publicado: (2023)

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels
por: Wong, Daniel R., et al.
Publicado: (2023)

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels
por: Wong, Daniel R., et al.
Publicado: (2023)

Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
por: Qin, Zengyi, et al.
Publicado: (2020)

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models
por: Genheden, Samuel
Publicado: (2017)

Electronic structure at coarse-grained resolutions from supervised machine learning
por: Jackson, Nicholas E., et al.
Publicado: (2019)

Macroscopic coherent structures in a stochastic neural network: from interface dynamics to coarse-grained bifurcation analysis
por: Avitable, Daniele, et al.
Publicado: (2017)

Benchmarking coarse-grained models of organic semiconductors via deep backmapping
por: Stieffenhofer, Marc, et al.
Publicado: (2022)

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models
por: Togashi, Yuichi, et al.
Publicado: (2018)

Optimal Coarse-Grained Site Selection in Elastic Network Models of Biomolecules
por: Diggins, Patrick, et al.
Publicado: (2018)

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities
por: Bertalan, Tom, et al.
Publicado: (2017)

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
por: Wee, Chong-Yaw, et al.
Publicado: (2019)

Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations (GCLSDA): Predicting snoRNA–Disease Associations via Graph Convolutional Network and Contrastive Learning
por: Zhang, Liangliang, et al.
Publicado: (2023)

Efficient Graph Collaborative Filtering via Contrastive Learning
por: Pan, Zhiqiang, et al.
Publicado: (2021)

The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network
por: Wang, Mengyao, et al.
Publicado: (2021)

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials
por: Zhang, Jianan, et al.
Publicado: (2023)

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks
por: Tangherloni, Andrea, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_86f2rq5gsu60laqoc5r4ljo8rt