Drug Discovery
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。
BACE
BACE (β-secretase enzyme)
BBBP
ProtoW-L2
BBBP (Blood-Brain Barrier Penetration)
BindingDB
AttentionSiteDTI
BindingDB IC50
DeepDTA
clintox
BiLSTM
DAVIS-DTA
DRD2
egfr-inh
Multi-input Neural network with Attention
ESOL (Estimated SOLubility)
FreeSolv (Free Solvation)
HIV dataset
GraphConv + dummy super node + focal loss
KIBA
SMT-DTA
Lipophilicity (logd74)
LIT-PCBA(ALDH1)
LIT-PCBA(ESR1_ant)
LIT-PCBA(KAT2A)
EGT+TGT-At-DP
LIT-PCBA(MAPK1)
MUV
TrimNet
PCBA
GraphConv + dummy super node
PDBbind
Ensemble locally constant networks
QED
HierG2G
QM9
PAMNet
SIDER
Ensemble locally constant networks
Tox21
elEmBERT-V1
ToxCast
ToxCast (Toxicity Forecaster)
GLAM