Graph Regression On Lipophilicity

RMSE@80%Train

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	RMSE@80%Train	Paper Title	Repository
Random Forests	1.16	CensNet: Convolution with Edge-Node Switching in Graph Neural Networks	-
CensNet	0.93	CensNet: Convolution with Edge-Node Switching in Graph Neural Networks	-
Weave	-	Molecular Graph Convolutions: Moving Beyond Fingerprints	-
GIN	-	How Powerful are Graph Neural Networks?	-
ESA (Edge set attention, no positional encodings)	-	An end-to-end attention-based approach for learning on graphs	-
XGBoost	-	Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective	-
SGC	-	Simplifying Graph Convolutional Networks	-
ProtoS-L2	-	Optimal Transport Graph Neural Networks	-
RF	-	Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective	-
GAT	-	Graph Attention Networks	-
AGNN	-	Attention-based Graph Neural Network for Semi-supervised Learning	-
DropGIN	-	DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks	-
ARMA	-	Graph Neural Networks with convolutional ARMA filters	-
GraphGPS	-	Recipe for a General, Powerful, Scalable Graph Transformer	-
C-SGEN+ Fingerprint	-	Molecule Property Prediction Based on Spatial Graph Embedding
PNA	-	Principal Neighbourhood Aggregation for Graph Nets	-
MPNN	-	Neural Message Passing for Quantum Chemistry	-
GCN	-	Semi-Supervised Classification with Graph Convolutional Networks	-
TokenGT	-	Pure Transformers are Powerful Graph Learners	-
GC	-	Convolutional Networks on Graphs for Learning Molecular Fingerprints	-

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