Molecular Property Prediction On Bace 1

المقاييس

ROC-AUC

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	ROC-AUC	Paper Title	Repository
SMA	84.3	Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning	-
N-GramRF	77.9	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules	-
S-CGIB	86.46±0.81	Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
ChemRL-GEM	85.6	ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction	-
GROVER (base)	82.6	Self-Supervised Graph Transformer on Large-Scale Molecular Data	-
ChemBERTa-2 (MTR-77M)	79.9	ChemBERTa-2: Towards Chemical Foundation Models	-
Uni-Mol	85.7	Uni-Mol: A Universal 3D Molecular Representation Learning Framework
GROVER (large)	81.0	Self-Supervised Graph Transformer on Large-Scale Molecular Data	-
GAL 1.3B	57.6	Galactica: A Large Language Model for Science	-
GAL 30B	72.7	Galactica: A Large Language Model for Science	-
D-MPNN	80.9	Analyzing Learned Molecular Representations for Property Prediction	-
GAL 125M	56.1	Galactica: A Large Language Model for Science	-
MolXPT	88.4	MolXPT: Wrapping Molecules with Text for Generative Pre-training	-
GAL 120B	61.7	Galactica: A Large Language Model for Science	-
ChemBFN	73.56	A Bayesian Flow Network Framework for Chemistry Tasks	-
SPMM	83.0	Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model	-
Deep-CBN	83.6	Integrating convolutional layers and biformer network with forward-forward and backpropagation training
GAL 6.7B	58.4	Galactica: A Large Language Model for Science	-
N-GramXGB	79.1	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules	-
PretrainGNN	84.5	Strategies for Pre-training Graph Neural Networks	-

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