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Comparison of Atom Representations in Graph Neural Networks for
  Molecular Property Prediction
v1v2 (latest)

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction

IEEE International Joint Conference on Neural Network (IJCNN), 2020
23 November 2020
Agnieszka Pocha
Tomasz Danel
Lukasz Maziarka
    GNN
ArXiv (abs)PDFHTML

Papers citing "Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction"

4 / 4 papers shown
ProGReST: Prototypical Graph Regression Soft Trees for Molecular
  Property Prediction
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionSDM (SDM), 2022
Dawid Rymarczyk
D. Dobrowolski
Tomasz Danel
300
7
0
07 Oct 2022
Graph neural networks for materials science and chemistry
Graph neural networks for materials science and chemistryCommunications Materials (Commun. Mater.), 2022
Patrick Reiser
Marlen Neubert
André Eberhard
Luca Torresi
Chen Zhou
...
Houssam Metni
Clint van Hoesel
Henrik Schopmans
T. Sommer
Pascal Friederich
GNNAI4CE
405
657
0
05 Aug 2022
Relative Molecule Self-Attention Transformer
Relative Molecule Self-Attention TransformerJournal of Cheminformatics (J Cheminform), 2021
Lukasz Maziarka
Dawid Majchrowski
Tomasz Danel
Piotr Gaiñski
Jacek Tabor
Igor T. Podolak
Pawel M. Morkisz
Stanislaw Jastrzebski
MedIm
298
45
0
12 Oct 2021
Learning to Extend Molecular Scaffolds with Structural Motifs
Learning to Extend Molecular Scaffolds with Structural MotifsInternational Conference on Learning Representations (ICLR), 2021
Krzysztof Maziarz
Henry Jackson-Flux
Pashmina Cameron
F. Sirockin
Nadine Schneider
N. Stiefl
Marwin H. S. Segler
Marc Brockschmidt
AI4CE
533
94
0
05 Mar 2021
1
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