Cargando…

Predicting chemical shifts with graph neural networks

Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yang, Ziyue, Chakraborty, Maghesree, White, Andrew D.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The Royal Society of Chemistry 2021
Materias:	Chemistry
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372537/ https://www.ncbi.nlm.nih.gov/pubmed/34476061 http://dx.doi.org/10.1039/d1sc01895g

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)

A graph-convolutional neural network model for the prediction of chemical reactivity
por: Coley, Connor W., et al.
Publicado: (2018)

Real-time prediction of (1)H and (13)C chemical shifts with DFT accuracy using a 3D graph neural network
por: Guan, Yanfei, et al.
Publicado: (2021)

Rapid prediction of protein natural frequencies using graph neural networks
por: Guo, Kai, et al.
Publicado: (2022)

Dual graph convolutional neural network for predicting chemical networks
por: Harada, Shonosuke, et al.
Publicado: (2020)

Explainable graph neural networks for organic cages
por: Yuan, Qi, et al.
Publicado: (2022)

Drug–target affinity prediction using graph neural network and contact maps
por: Jiang, Mingjian, et al.
Publicado: (2020)

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
por: Yang, Ziduo, et al.
Publicado: (2022)

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
por: Wen, Mingjian, et al.
Publicado: (2020)

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks
por: Venetos, Maxwell C., et al.
Publicado: (2023)

Uncertainty-aware prediction of chemical reaction yields with graph neural networks
por: Kwon, Youngchun, et al.
Publicado: (2022)

Improving Small Molecule pK( a ) Prediction Using Transfer Learning With Graph Neural Networks
por: Mayr, Fritz, et al.
Publicado: (2022)

Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation
por: Kong, Yue, et al.
Publicado: (2022)

Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
por: Yang, Ziduo, et al.
Publicado: (2022)

Predicting potentially hazardous chemical reactions using an explainable neural network
por: Kim, Juhwan, et al.
Publicado: (2021)

Prediction of disulfide dihedral angles using chemical shifts
por: Armstrong, David A., et al.
Publicado: (2018)

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
por: Sakai, Miyuki, et al.
Publicado: (2021)

Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs
por: Alves, Luiz Anastacio, et al.
Publicado: (2022)

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction
por: Suviriyapaisal, Natchanon, et al.
Publicado: (2023)

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation
por: Blau, Samuel M., et al.
Publicado: (2021)

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
por: Chen, Jiarui, et al.
Publicado: (2021)

Chemical space exploration guided by deep neural networks
por: Karlov, Dmitry S., et al.
Publicado: (2019)

Equivariant Graph Neural Networks for Toxicity Prediction
por: Cremer, Julian, et al.
Publicado: (2023)

Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data
por: Li, Jie, et al.
Publicado: (2020)

Efficient prediction of reaction paths through molecular graph and reaction network analysis
por: Kim, Yeonjoon, et al.
Publicado: (2017)

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction
por: Pu, Shilin, et al.
Publicado: (2022)

Federated learning of molecular properties with graph neural networks in a heterogeneous setting
por: Zhu, Wei, et al.
Publicado: (2022)

A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
por: Ryu, Seongok, et al.
Publicado: (2019)

Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals
por: Ito, Kengo, et al.
Publicado: (2018)

Uncertainty quantification for predictions of atomistic neural networks
por: Vazquez-Salazar, Luis Itza, et al.
Publicado: (2022)

Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation
por: Ishida, Sho, et al.
Publicado: (2021)

A quantitative uncertainty metric controls error in neural network-driven chemical discovery
por: Janet, Jon Paul, et al.
Publicado: (2019)

Neural network activation similarity: a new measure to assist decision making in chemical toxicology
por: Allen, Timothy E. H., et al.
Publicado: (2020)

Effects of fluorine bonding and nonbonding interactions on (19)F chemical shifts
por: Lu, Yang, et al.
Publicado: (2022)

Graph neural network and multi-data heterogeneous networks for microbe-disease prediction
por: Gong, Houwu, et al.
Publicado: (2022)

Introduction to graph neural networks
por: Liu, Zhiyuan, et al.
Publicado: (2020)

Quantitative evaluation of explainable graph neural networks for molecular property prediction
por: Rao, Jiahua, et al.
Publicado: (2022)

Heterogeneous graph inference based on similarity network fusion for predicting lncRNA–miRNA interaction
por: Fan, Yongxian, et al.
Publicado: (2020)

DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
por: You, Ronghui, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_kn7jbustpcn7tpcn9jmam8laob