Molecular Property Prediction On Freesolv

Metrics

RMSE

Results

Performance results of various models on this benchmark

			Paper Title	Repository
N-GramXGB	-	5.061	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
PretrainGNN	-	2.764	Strategies for Pre-training Graph Neural Networks
N-GramRF	-	2.688	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
GROVER (large)	-	2.272	Self-Supervised Graph Transformer on Large-Scale Molecular Data
GROVER (base)	-	2.176	Self-Supervised Graph Transformer on Large-Scale Molecular Data
D-MPNN	-	2.082	Analyzing Learned Molecular Representations for Property Prediction
ChemRL-GEM	-	1.877	ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction	-
SPMM	-	1.859	Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
S-CGIB	-	1.648±0.074	Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck	-
Uni-Mol	-	1.620	Uni-Mol: A Universal 3D Molecular Representation Learning Framework	-
GraphGPS	0.861±0.037	1.462±0.188	Recipe for a General, Powerful, Scalable Graph Transformer
ChemBFN	-	1.418	A Bayesian Flow Network Framework for Chemistry Tasks
SMA	-	1.09	Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Graphormer	0.927±0.005	1.065±0.039	Do Transformers Really Perform Bad for Graph Representation?
TokenGT	0.930±0.018	1.038±0.125	Pure Transformers are Powerful Graph Learners
PNA	0.951±0.009	0.870±0.081	Principal Neighbourhood Aggregation for Graph Nets
GCN	0.957±0.009	0.815±0.086	Semi-Supervised Classification with Graph Convolutional Networks
GAT	0.959±0.011	0.791±0.101	Graph Attention Networks
GIN	0.964±0.008	0.744±0.083	How Powerful are Graph Neural Networks?
GATv2	0.970±0.007	0.676±0.081	How Attentive are Graph Attention Networks?

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Molecular Property Prediction On Freesolv

Metrics

Results