Cargando…

Explainable graph neural networks for organic cages

The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in materials science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encap...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yuan, Qi, Szczypiński, Filip T., Jelfs, Kim E.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	RSC 2022
Materias:	Chemistry
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996732/ https://www.ncbi.nlm.nih.gov/pubmed/35515082 http://dx.doi.org/10.1039/d1dd00039j

Ejemplares similares

Unlocking the computational design of metal–organic cages
por: Tarzia, Andrew, et al.
Publicado: (2022)

Can we predict materials that can be synthesised?
por: Szczypiński, Filip T., et al.
Publicado: (2020)

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors
por: Bennett, Steven, et al.
Publicado: (2021)

An evolutionary algorithm for the discovery of porous organic cages
por: Berardo, Enrico, et al.
Publicado: (2018)

Coarse-grained modelling to predict the packing of porous organic cages
por: Wolpert, Emma H., et al.
Publicado: (2022)

Deep generative design of porous organic cages via a variational autoencoder
por: Zhou, Jiajun, et al.
Publicado: (2023)

Inducing Social Self‐Sorting in Organic Cages To Tune The Shape of The Internal Cavity
por: Abet, Valentina, et al.
Publicado: (2020)

Systematic exploration of accessible topologies of cage molecules via minimalistic models
por: Tarzia, Andrew, et al.
Publicado: (2023)

Evaluating explainability for graph neural networks
por: Agarwal, Chirag, et al.
Publicado: (2023)

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

Competitive aminal formation during the synthesis of a highly soluble, isopropyl-decorated imine porous organic cage
por: Kearsey, Rachel J., et al.
Publicado: (2023)

Orientational self-sorting in cuboctahedral Pd cages
por: Li, Ru-Jin, et al.
Publicado: (2022)

Mapping binary copolymer property space with neural networks
por: Wilbraham, Liam, et al.
Publicado: (2019)

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 chemical shifts with graph neural networks
por: Yang, Ziyue, et al.
Publicado: (2021)

Transformation networks of metal–organic cages controlled by chemical stimuli
por: Benchimol, Elie, et al.
Publicado: (2022)

Structural Flexibility in Metal-Organic Cages
por: Martín Díaz, Andrés E., et al.
Publicado: (2021)

Explainable Multilayer Graph Neural Network for cancer gene prediction
por: Chatzianastasis, Michail, et al.
Publicado: (2023)

Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs
por: Kim, So Yeon
Publicado: (2023)

Building blocks for recognition-encoded oligoesters that form H-bonded duplexes
por: Szczypiński, Filip T., et al.
Publicado: (2019)

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

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

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

Water-stable hydrazone-linked porous organic cages
por: Yang, Miao, et al.
Publicado: (2021)

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

XGDAG: explainable gene–disease associations via graph neural networks
por: Mastropietro, Andrea, et al.
Publicado: (2023)

Chiral proline-substituted porous organic cages in asymmetric organocatalysis
por: Xu, Ning, et al.
Publicado: (2022)

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

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

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

Porous Organic Cages for Sulfur Hexafluoride Separation
por: Hasell, Tom, et al.
Publicado: (2016)

The effect of disorder in multi-component covalent organic frameworks
por: Wolpert, Emma H., et al.
Publicado: (2023)

Ionic organic cage-encapsulating phase-transferable metal clusters
por: Zhang, Su-Yun, et al.
Publicado: (2018)

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

Explainable drug side effect prediction via biologically informed graph neural network
por: Huang, Tongtong, et al.
Publicado: (2023)

Singlet oxygen stimulus for switchable functional organic cages
por: Mongin, Cédric, et al.
Publicado: (2020)

An organic cage controlling the dimension and stability of gold nanoparticles
por: Peters, Erich Henrik, et al.
Publicado: (2023)

Synthesis of a Large, Shape-Flexible, Solvatomorphic Porous Organic Cage
por: Teng, Baiyang, et al.
Publicado: (2019)

Photostable polymorphic organic cages for targeted live cell imaging
por: Al Kelabi, Dana, et al.
Publicado: (2022)

Inside information on xenon adsorption in porous organic cages by NMR
por: Komulainen, Sanna, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_aihlp9nkmr29q2rv4m9mvss1hi