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المنصة
الرئيسية
SOTA
الرسوم البيانية للانحدار
Graph Regression On Lipophilicity
Graph Regression On Lipophilicity
المقاييس
RMSE@80%Train
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
RMSE@80%Train
Paper Title
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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