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Drug Discovery On Tox21

Metriken

AUC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	AUC	Paper Title	Repository
ContextPred	0.781	Strategies for Pre-training Graph Neural Networks	-
GraphConv + dummy super node	0.854	Learning Graph-Level Representation for Drug Discovery	-
TrimNet	0.860	TrimNet: learning molecular representation from triplet messages for biomedicine
RNN-DFS	0.748	Relational Pooling for Graph Representations	-
GraphConv	0.846	Convolutional Networks on Graphs for Learning Molecular Fingerprints	-
Ensemble predictor	0.862	ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors	-
elEmBERT-V1	0.961	Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties	-
GIT-Mol(G+S)	0.759	GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text	-
SNN (SELU Network)	0.845	Self-Normalizing Neural Networks	-
SSVAE with multiple SMILES	0.871	All SMILES Variational Autoencoder	-
TrimNet + Perforated Backpropagation	0.885	Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks	-

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Drug Discovery On Tox21 | SOTA | HyperAI