Molecular Property Prediction On

Metrics

RMSE

Results

Performance results of various models on this benchmark

		Paper Title
N-GramXGB	2.072	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
GROVER (large)	0.823	Self-Supervised Graph Transformer on Large-Scale Molecular Data
GROVER (base)	0.817	Self-Supervised Graph Transformer on Large-Scale Molecular Data
N-GramRF	0.812	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
ChemBERTa-2 (MTR-77M)	0.798	ChemBERTa-2: Towards Chemical Foundation Models
S-CGIB	0.762±0.042	Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
ChemBFN	0.746	A Bayesian Flow Network Framework for Chemistry Tasks
PretrainGNN	0.739	Strategies for Pre-training Graph Neural Networks
SPMM	0.706	Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
D-MPNN	0.683	Analyzing Learned Molecular Representations for Property Prediction
ChemRL-GEM	0.66	ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
SMA	0.609	Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Uni-Mol	0.603	Uni-Mol: A Universal 3D Molecular Representation Learning Framework

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Molecular Property Prediction On

Metrics

RMSE

Results

Performance results of various models on this benchmark

		Paper Title
N-GramXGB	2.072	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
GROVER (large)	0.823	Self-Supervised Graph Transformer on Large-Scale Molecular Data
GROVER (base)	0.817	Self-Supervised Graph Transformer on Large-Scale Molecular Data
N-GramRF	0.812	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
ChemBERTa-2 (MTR-77M)	0.798	ChemBERTa-2: Towards Chemical Foundation Models
S-CGIB	0.762±0.042	Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
ChemBFN	0.746	A Bayesian Flow Network Framework for Chemistry Tasks
PretrainGNN	0.739	Strategies for Pre-training Graph Neural Networks
SPMM	0.706	Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
D-MPNN	0.683	Analyzing Learned Molecular Representations for Property Prediction
ChemRL-GEM	0.66	ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
SMA	0.609	Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Uni-Mol	0.603	Uni-Mol: A Universal 3D Molecular Representation Learning Framework

0 of 13 row(s) selected.

Molecular Property Prediction On | SOTA | HyperAI