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SOTA
药物发现
Drug Discovery On Lit Pcba Aldh1
Drug Discovery On Lit Pcba Aldh1
Metrics
AUC
Results
Performance results of various models on this benchmark
Columns
Model Name
AUC
Paper Title
Code
EGT+TGT-At-DP
0.806
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
GLAM
0.761
An adaptive graph learning method for automated molecular interactions and properties predictions
TransformerCPI
0.694
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
DGraphDTA
0.679
Drug–target affinity prediction using graph neural network and contact maps
0 of 4 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
Console
Sign In
首页
SOTA
药物发现
Drug Discovery On Lit Pcba Aldh1
Drug Discovery On Lit Pcba Aldh1
Metrics
AUC
Results
Performance results of various models on this benchmark
Columns
Model Name
AUC
Paper Title
Code
EGT+TGT-At-DP
0.806
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
GLAM
0.761
An adaptive graph learning method for automated molecular interactions and properties predictions
TransformerCPI
0.694
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
DGraphDTA
0.679
Drug–target affinity prediction using graph neural network and contact maps
0 of 4 row(s) selected.
Previous
Next
Console
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