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Molecular Graph Convolutions: Moving Beyond Fingerprints
v1v2v3 (latest)

Molecular Graph Convolutions: Moving Beyond Fingerprints

2 March 2016
S. Kearnes
Kevin McCloskey
Marc Berndl
Vijay S. Pande
Patrick F. Riley
    GNN
ArXiv (abs)PDFHTML

Papers citing "Molecular Graph Convolutions: Moving Beyond Fingerprints"

50 / 439 papers shown
Title
Step Change Improvement in ADMET Prediction with PotentialNet Deep
  Featurization
Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization
Evan N. Feinberg
Robert P. Sheridan
E. Joshi
Vijay S. Pande
A. Cheng
MedIm
104
17
0
28 Mar 2019
Batched Sparse Matrix Multiplication for Accelerating Graph
  Convolutional Networks
Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks
Yusuke Nagasaka
Akira Nukada
Ryosuke Kojima
Satoshi Matsuoka
GNN
56
10
0
27 Mar 2019
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Daniel T. Chang
AI4CEBDL
127
7
0
21 Mar 2019
Equivariant Entity-Relationship Networks
Equivariant Entity-Relationship Networks
Devon R. Graham
Junhao Wang
Siamak Ravanbakhsh
182
8
0
21 Mar 2019
Interpretable Deep Learning in Drug Discovery
Interpretable Deep Learning in Drug Discovery
Kristina Preuer
Günter Klambauer
F. Rippmann
Sepp Hochreiter
Thomas Unterthiner
161
100
0
07 Mar 2019
Relational Pooling for Graph Representations
Relational Pooling for Graph Representations
R. Murphy
Ninad Kulkarni
Vinayak A. Rao
Bruno Ribeiro
GNN
348
278
0
06 Mar 2019
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale
  Dynamics in Materials
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
T. Xie
A. France-Lanord
Yanming Wang
Y. Shao-horn
Jeffrey C. Grossman
AI4CE
140
119
0
18 Feb 2019
Probabilistic Generative Deep Learning for Molecular Design
Probabilistic Generative Deep Learning for Molecular Design
Daniel T. Chang
BDLAI4CE
126
7
0
11 Feb 2019
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph
  Neural Networks in Molecular Graph Analysis
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
Katsuhiko Ishiguro
S. Maeda
Masanori Koyama
GNN
171
33
0
04 Feb 2019
Deep Learning on Attributed Graphs: A Journey from Graphs to Their
  Embeddings and Back
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
M. Simonovsky
BDLGNN
133
1
0
24 Jan 2019
A Universal Density Matrix Functional from Molecular Orbital-Based
  Machine Learning: Transferability across Organic Molecules
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
Lixue Cheng
Matthew Welborn
Anders S. Christensen
Thomas F. Miller
222
104
0
10 Jan 2019
A Comprehensive Survey on Graph Neural Networks
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu
Shirui Pan
Fengwen Chen
Guodong Long
Chengqi Zhang
Philip S. Yu
FaMLGNNAI4TSAI4CE
1.4K
10,024
0
03 Jan 2019
Drug cell line interaction prediction
Drug cell line interaction prediction
Pengfei Liu
141
126
0
28 Dec 2018
Graph Neural Networks: A Review of Methods and Applications
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou
Ganqu Cui
Shengding Hu
Zhengyan Zhang
Cheng Yang
Zhiyuan Liu
Lifeng Wang
Changcheng Li
Maosong Sun
AI4CEGNN
1.6K
6,288
0
20 Dec 2018
Deep Learning on Graphs: A Survey
Deep Learning on Graphs: A Survey
Ziwei Zhang
Peng Cui
Wenwu Zhu
GNN
416
1,470
0
11 Dec 2018
Learning Multimodal Graph-to-Graph Translation for Molecular
  Optimization
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
Wengong Jin
Kevin Kaichuang Yang
Regina Barzilay
Tommi Jaakkola
358
251
0
03 Dec 2018
Discovering Molecular Functional Groups Using Graph Convolutional Neural
  Networks
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Phillip E. Pope
Soheil Kolouri
Mohammad Rostami
Charles E. Martin
Heiko Hoffmann
GNN
330
16
0
01 Dec 2018
Using Attribution to Decode Dataset Bias in Neural Network Models for
  Chemistry
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Kevin McCloskey
Ankur Taly
Federico Monti
M. Brenner
Lucy J. Colwell
155
91
0
27 Nov 2018
Grammars and reinforcement learning for molecule optimization
Grammars and reinforcement learning for molecule optimization
E. Kraev
98
7
0
27 Nov 2018
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for
  Molecule Interpretation
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule InterpretationChemMedChem (ChemMedChem), 2018
Hyeoncheol Cho
I. Choi
297
60
0
24 Nov 2018
KekuleScope: prediction of cancer cell line sensitivity and compound
  potency using convolutional neural networks trained on compound images
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound imagesJournal of Cheminformatics (J Cheminform), 2018
I. Cortés-Ciriano
A. Bender
MedIm
192
53
0
22 Nov 2018
Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural
  Networks
Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural NetworksACS Central Science (ACS Cent. Sci.), 2018
Jennifer N. Wei
David Belanger
Ryan P. Adams
D. Sculley
172
162
0
21 Nov 2018
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with
  Multitask Learning for Material Property Prediction
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Soumya Sanyal
J. Balachandran
N. Yadati
Abhishek Kumar
Padmini Rajagopalan
Suchismita Sanyal
Partha P. Talukdar
179
54
0
14 Nov 2018
A simple yet effective baseline for non-attributed graph classification
A simple yet effective baseline for non-attributed graph classification
Chen Cai
Yusu Wang
231
35
0
08 Nov 2018
Deep learning long-range information in undirected graphs with wave
  networks
Deep learning long-range information in undirected graphs with wave networks
Matthew K. Matlock
Arghya Datta
N. Dang
Kevin Jiang
S. Joshua Swamidass
GNN
86
15
0
29 Oct 2018
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera
Aleksandar Bojchevski
Stephan Günnemann
GNN
832
1,902
0
14 Oct 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
790
8,888
0
01 Oct 2018
Powerful, transferable representations for molecules through intelligent
  task selection in deep multitask networks
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Clyde Fare
Lukas Turcani
Edward O. Pyzer-Knapp
80
14
0
17 Sep 2018
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural
  Networks
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Khushmeen Sakloth
Wesley Beckner
J. Pfaendtner
Garrett B. Goh
73
2
0
13 Sep 2018
Discriminative Learning of Similarity and Group Equivariant
  Representations
Discriminative Learning of Similarity and Group Equivariant Representations
Shubhendu Trivedi
112
1
0
30 Aug 2018
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Yunsheng Bai
Haoyang Ding
Song Bian
Ting-Li Chen
Luke Huan
Wei Wang
GNN
224
372
0
16 Aug 2018
Multimodal Deep Neural Networks using Both Engineered and Learned
  Representations for Biodegradability Prediction
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Garrett B. Goh
Khushmeen Sakloth
Charles Siegel
Abhinav Vishnu
J. Pfaendtner
HAI
127
11
0
13 Aug 2018
Relational dynamic memory networks
Relational dynamic memory networks
Trang Pham
T. Tran
Svetha Venkatesh
GNN
177
3
0
10 Aug 2018
PADME: A Deep Learning-based Framework for Drug-Target Interaction
  Prediction
PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction
Qingyuan Feng
Evgenia V. Dueva
Artem Cherkasov
Martin Ester
206
96
0
25 Jul 2018
Hierarchical Visualization of Materials Space with Graph Convolutional
  Neural Networks
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
T. Xie
Jeffrey C. Grossman
AI4CE
151
68
0
09 Jul 2018
Jointly learning relevant subgraph patterns and nonlinear models of
  their indicators
Jointly learning relevant subgraph patterns and nonlinear models of their indicators
Ryoji Shirakawa
Y. Yokoyama
F. Okazaki
Ichigaku Takigawa
44
0
0
09 Jul 2018
BayesGrad: Explaining Predictions of Graph Convolutional Networks
BayesGrad: Explaining Predictions of Graph Convolutional NetworksInternational Conference on Neural Information Processing (ICONIP), 2018
Hirotaka Akita
Kosuke Nakago
Tomoki Komatsu
Yohei Sugawara
S. Maeda
Yukino Baba
H. Kashima
FAttOODBDL
100
9
0
04 Jul 2018
End-to-End Learning on 3D Protein Structure for Interface Prediction
End-to-End Learning on 3D Protein Structure for Interface PredictionNeural Information Processing Systems (NeurIPS), 2018
Raphael J. L. Townshend
Rishi Bedi
Patricia Suriana
R. Dror
3DV
303
109
0
03 Jul 2018
Quantum-chemical insights from interpretable atomistic neural networks
Quantum-chemical insights from interpretable atomistic neural networks
Kristof T. Schütt
M. Gastegger
A. Tkatchenko
K. Müller
AI4CE
125
34
0
27 Jun 2018
Graph-to-Sequence Learning using Gated Graph Neural Networks
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck
Gholamreza Haffari
Trevor Cohn
GNN
141
336
0
26 Jun 2018
Improving Chemical Autoencoder Latent Space and Molecular De novo
  Generation Diversity with Heteroencoders
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders
E. Bjerrum
Boris Sattarov
BDL
150
155
0
25 Jun 2018
N-Gram Graph: Simple Unsupervised Representation for Graphs, with
  Applications to Molecules
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu
M. F. Demirel
Yingyu Liang
GNNNAI
171
215
0
24 Jun 2018
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble
  Predictors
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
M. Zaslavskiy
S. Jégou
Eric W. Tramel
G. Wainrib
116
25
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
752
2,219
0
09 Jun 2018
Neural Message Passing with Edge Updates for Predicting Properties of
  Molecules and Materials
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Peter Bjørn Jørgensen
K. Jacobsen
Mikkel N. Schmidt
133
85
0
08 Jun 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
665
960
0
07 Jun 2018
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying
Ruining He
Kaifeng Chen
Pong Eksombatchai
William L. Hamilton
J. Leskovec
GNNBDL
797
3,869
0
06 Jun 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CENAI
1.2K
3,360
0
04 Jun 2018
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular
  Structure Information
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Masaki Asada
Makoto Miwa
Yutaka Sasaki
95
49
0
15 May 2018
Conditional molecular design with deep generative models
Conditional molecular design with deep generative models
Seokho Kang
Dong Wang
BDL
483
197
0
30 Apr 2018
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