TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Node Classification	CiteSeer (0.5%)	GCN-FP	Accuracy	43.9%	# 12
Node Classification	CiteSeer (1%)	GCN-FP	Accuracy	54.3%	# 12
Node Classification	CiteSeer with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	61.5%	# 40
Node Classification	Cora (0.5%)	GCN-FP	Accuracy	50.5%	# 11
Node Classification	Cora (1%)	GCN-FP	Accuracy	59.6%	# 12
Node Classification	Cora (3%)	GCN-FP	Accuracy	71.7%	# 12
Node Classification	Cora with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	74.6%	# 35
Drug Discovery	HIV dataset	GraphConv	AUC	0.822	# 2
Graph Regression	Lipophilicity	GC	RMSE	0.655	# 11
Drug Discovery	MUV	GraphConv	AUC	0.836	# 3
Drug Discovery	PCBA	GraphConv	AUC	0.855	# 2
Node Classification	PubMed (0.03%)	GCN-FP	Accuracy	56.2%	# 10
Node Classification	PubMed (0.05%)	GCN-FP	Accuracy	63.2%	# 11
Node Classification	PubMed (0.1%)	GCN-FP	Accuracy	70.3%	# 11
Node Classification	PubMed with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	76.0%	# 31
Drug Discovery	Tox21	GraphConv	AUC	0.846	# 7
Drug Discovery	ToxCast	GraphConv	AUC	0.754	# 3

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Convolutional Networks on Graphs for Learning Molecular Fingerprints

NeurIPS 2015 · David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams ·

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.

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HIPS/neural-fingerprint official

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debbiemarkslab/neural-fingerprint-t…

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pgniewko/solubility

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Drug Discovery

Graph Regression

Node Classification

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Pubmed

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Tox21 HIV (Human Immunodeficiency Virus) ToxCast (Toxicity Forecaster) MUV PCBA

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Ranked #2 on Drug Discovery on HIV dataset

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Node Classification	CiteSeer (0.5%)	GCN-FP	Accuracy	43.9%	# 12	Compare
Node Classification	CiteSeer (1%)	GCN-FP	Accuracy	54.3%	# 12	Compare
Node Classification	CiteSeer with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	61.5%	# 40	Compare
Node Classification	Cora (0.5%)	GCN-FP	Accuracy	50.5%	# 11	Compare
Node Classification	Cora (1%)	GCN-FP	Accuracy	59.6%	# 12	Compare
Node Classification	Cora (3%)	GCN-FP	Accuracy	71.7%	# 12	Compare
Node Classification	Cora with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	74.6%	# 35	Compare
Drug Discovery	HIV dataset	GraphConv	AUC	0.822	# 2	Compare
Graph Regression	Lipophilicity	GC	RMSE	0.655	# 11	Compare
Drug Discovery	MUV	GraphConv	AUC	0.836	# 3	Compare
Drug Discovery	PCBA	GraphConv	AUC	0.855	# 2	Compare
Node Classification	PubMed (0.03%)	GCN-FP	Accuracy	56.2%	# 10	Compare
Node Classification	PubMed (0.05%)	GCN-FP	Accuracy	63.2%	# 11	Compare
Node Classification	PubMed (0.1%)	GCN-FP	Accuracy	70.3%	# 11	Compare
Node Classification	PubMed with Public Split: fixed 20 nodes per class	GCN-FP	Accuracy	76.0%	# 31	Compare
Drug Discovery	Tox21	GraphConv	AUC	0.846	# 7	Compare
Drug Discovery	ToxCast	GraphConv	AUC	0.754	# 3	Compare

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