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Plateforme

Molecular Property Prediction On Bace 1

Métriques

ROC-AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
MolXPT88.4MolXPT: Wrapping Molecules with Text for Generative Pre-training
S-CGIB86.46±0.81Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
Uni-Mol85.7Uni-Mol: A Universal 3D Molecular Representation Learning Framework
ChemRL-GEM85.6ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
PretrainGNN84.5Strategies for Pre-training Graph Neural Networks
SMA84.3Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Deep-CBN83.6Integrating convolutional layers and biformer network with forward-forward and backpropagation training
SPMM83.0Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
GROVER (base)82.6Self-Supervised Graph Transformer on Large-Scale Molecular Data
GROVER (large)81.0Self-Supervised Graph Transformer on Large-Scale Molecular Data
D-MPNN80.9Analyzing Learned Molecular Representations for Property Prediction
ChemBERTa-2 (MTR-77M)79.9ChemBERTa-2: Towards Chemical Foundation Models
N-GramXGB79.1N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
N-GramRF77.9N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
ChemBFN73.56A Bayesian Flow Network Framework for Chemistry Tasks
GAL 30B72.7Galactica: A Large Language Model for Science
GAL 120B61.7Galactica: A Large Language Model for Science
GAL 6.7B58.4Galactica: A Large Language Model for Science
GAL 1.3B57.6Galactica: A Large Language Model for Science
GAL 125M56.1Galactica: A Large Language Model for Science
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Molecular Property Prediction On Bace 1 | SOTA | HyperAI