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SOTA
Drug Discovery
Drug Discovery On Qm9
Drug Discovery On Qm9
Metrics
Error ratio
Results
Performance results of various models on this benchmark
Columns
Model Name
Error ratio
Paper Title
Molecular Graph Convolutions
2.59
Molecular Graph Convolutions: Moving Beyond Fingerprints
Gated Graph Sequence NN
1.36
Gated Graph Sequence Neural Networks
MPNNs
0.68
Neural Message Passing for Quantum Chemistry
DeepMoleNet
0.531
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
DimeNet
0.44
Directional Message Passing for Molecular Graphs
PaiNN
0.411
Equivariant message passing for the prediction of tensorial properties and molecular spectra
DimeNet++
0.410
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
ComENet
0.403
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
SphereNet
0.386
Spherical Message Passing for 3D Graph Networks
MXMNet
0.382
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
PAMNet
0.363
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
0 of 11 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Drug Discovery
Drug Discovery On Qm9
Drug Discovery On Qm9
Metrics
Error ratio
Results
Performance results of various models on this benchmark
Columns
Model Name
Error ratio
Paper Title
Molecular Graph Convolutions
2.59
Molecular Graph Convolutions: Moving Beyond Fingerprints
Gated Graph Sequence NN
1.36
Gated Graph Sequence Neural Networks
MPNNs
0.68
Neural Message Passing for Quantum Chemistry
DeepMoleNet
0.531
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
DimeNet
0.44
Directional Message Passing for Molecular Graphs
PaiNN
0.411
Equivariant message passing for the prediction of tensorial properties and molecular spectra
DimeNet++
0.410
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
ComENet
0.403
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
SphereNet
0.386
Spherical Message Passing for 3D Graph Networks
MXMNet
0.382
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
PAMNet
0.363
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
0 of 11 row(s) selected.
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Next