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Utilising Graph Machine Learning within Drug Discovery and Development

Utilising Graph Machine Learning within Drug Discovery and Development

9 December 2020
Thomas Gaudelet
Ben Day
Arian R. Jamasb
Jyothish Soman
Cristian Regep
Gertrude Liu
Jeremy B. R. Hayter
R. Vickers
Charlie Roberts
Jian Tang
D. Roblin
Tom L. Blundell
M. Bronstein
J. Taylor-King
    AI4CE
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Papers citing "Utilising Graph Machine Learning within Drug Discovery and Development"

10 / 10 papers shown
Title
Improving Link Prediction in Social Networks Using Local and Global
  Features: A Clustering-based Approach
Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach
S. Ghasemi
Ahmad Zarei
9
6
0
17 May 2023
GraphGANFed: A Federated Generative Framework for Graph-Structured
  Molecules Towards Efficient Drug Discovery
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery
Daniel Manu
Jingjing Yao
Wuji Liu
Xiang Sun
FedML
10
6
0
11 Apr 2023
Diffusing Graph Attention
Diffusing Graph Attention
Daniel Glickman
Eran Yahav
GNN
28
3
0
01 Mar 2023
A Generalization of ViT/MLP-Mixer to Graphs
A Generalization of ViT/MLP-Mixer to Graphs
Xiaoxin He
Bryan Hooi
T. Laurent
Adam Perold
Yann LeCun
Xavier Bresson
22
88
0
27 Dec 2022
Attentive cross-modal paratope prediction
Attentive cross-modal paratope prediction
Andreea Deac
Petar Velickovic
Pietro Sormanni
31
58
0
12 Jun 2018
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
229
1,935
0
09 Jun 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
219
1,332
0
12 Feb 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
234
1,809
0
25 Nov 2016
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
231
3,230
0
24 Nov 2016
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
228
31,150
0
16 Jan 2013
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