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Drug Discovery
Drug discovery is the task of applying machine learning techniques to the identification and development of new drug candidates. Its goal is to predict compound activity through computational models, optimize the drug design process, enhance the efficiency and success rate of discovering potential therapeutic drugs, thereby accelerating the drug development cycle, reducing R&D costs, and improving innovation capabilities and treatment standards in the healthcare sector.
Tox21
elEmBERT-V1
QM9
PAMNet
BACE
ToxCast
HIV dataset
GraphConv + dummy super node + focal loss
MUV
GraphConv + dummy super node
LIT-PCBA(MAPK1)
clintox
BiLSTM
KIBA
SMT-DTA
BindingDB
AttentionSiteDTI
BBBP
ProtoW-L2
SIDER
Ensemble locally constant networks
LIT-PCBA(KAT2A)
EGT+TGT-At-DP
LIT-PCBA(ALDH1)
DAVIS-DTA
LIT-PCBA(ESR1_ant)
PCBA
GraphConv + dummy super node
BindingDB IC50
DeepDTA
FreeSolv (Free Solvation)
BACE (β-secretase enzyme)
egfr-inh
Multi-input Neural network with Attention
DRD2
ToxCast (Toxicity Forecaster)
GLAM
Lipophilicity (logd74)
PDBbind
Ensemble locally constant networks
BBBP (Blood-Brain Barrier Penetration)
QED
HierG2G
ESOL (Estimated SOLubility)