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药物发现
药物发现是将机器学习技术应用于新候选药物的识别与开发过程的任务。其目标在于通过计算模型预测化合物活性,优化药物设计流程,提高发现潜在治疗药物的效率和成功率,从而加速药物研发周期,降低研发成本,提升医疗健康领域的创新能力和治疗水平。
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)