HyperAI

Molecular Property Prediction On Clintox 1

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ROC-AUC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
ROC-AUC
Paper TitleRepository
SPMM91.0Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
GAL 1.3B58.9Galactica: A Large Language Model for Science
GAL 125M51.8Galactica: A Large Language Model for Science
N-GramRF77.5N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
GAL 120B82.6Galactica: A Large Language Model for Science
Deep-CBN99.2Integrating convolutional layers and biformer network with forward-forward and backpropagation training
ChemBFN99.18A Bayesian Flow Network Framework for Chemistry Tasks
ChemBERTa-2 (MTR-77M)56.3ChemBERTa-2: Towards Chemical Foundation Models
GROVER (base)81.2Self-Supervised Graph Transformer on Large-Scale Molecular Data
Uni-Mol91.9Uni-Mol: A Universal 3D Molecular Representation Learning Framework
GAL 6.7B78.4Galactica: A Large Language Model for Science
S-CGIB78.58±2.01Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
D-MPNN90.6Analyzing Learned Molecular Representations for Property Prediction
MolXPT95.3±0.2MolXPT: Wrapping Molecules with Text for Generative Pre-training-
GAL 30B82.2Galactica: A Large Language Model for Science
ChemRL-GEM90.1ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction-
N-GramXGB87.5N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
GROVER (large)76.2Self-Supervised Graph Transformer on Large-Scale Molecular Data
SYN-FUSION94.7±0.2Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction-
PretrainGNN72.6Strategies for Pre-training Graph Neural Networks-
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