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Drug Discovery On Tox21

评估指标

AUC

评测结果

各个模型在此基准测试上的表现结果

模型名称
AUC
Paper TitleRepository
ContextPred0.781Strategies for Pre-training Graph Neural Networks-
GraphConv + dummy super node0.854Learning Graph-Level Representation for Drug Discovery-
TrimNet0.860TrimNet: learning molecular representation from triplet messages for biomedicine
RNN-DFS0.748Relational Pooling for Graph Representations-
GraphConv0.846Convolutional Networks on Graphs for Learning Molecular Fingerprints-
Ensemble predictor0.862ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors-
elEmBERT-V10.961Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties-
GIT-Mol(G+S)0.759GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text-
SNN (SELU Network)0.845Self-Normalizing Neural Networks-
SSVAE with multiple SMILES0.871All SMILES Variational Autoencoder-
TrimNet + Perforated Backpropagation0.885Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks-
0 of 11 row(s) selected.
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