Table 3.

The comparison of the performance of Mol-GDL and SOTAs on synergistic drug combination dataset

Methods	Metrics
	AUC	AUPR	Recall	Precision	F1
XGBoost46	${0.92}_{\left(0.01\right)}$	${0.92}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$
RF47	${0.86}_{\left(0.01\right)}$	${0.85}_{\left(0.02\right)}$	${0.74}_{\left(0.01\right)}$	${0.78}_{\left(0.02\right)}$	${0.76}_{\left(0.01\right)}$
TranSynergy43	${0.90}_{\left(0.01\right)}$	${0.89}_{\left(0.01\right)}$	${0.80}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$	${0.82}_{\left(0.01\right)}$
DTF45	${0.89}_{\left(0.01\right)}$	${0.88}_{\left(0.01\right)}$	${0.77}_{\left(0.03\right)}$	${0.82}_{\left(0.01\right)}$	${0.80}_{\left(0.02\right)}$
DeepSynergy44	${0.88}_{\left(0.01\right)}$	${0.87}_{\left(0.01\right)}$	${0.75}_{\left(0.01\right)}$	${0.81}_{\left(0.01\right)}$	${0.78}_{\left(0.01\right)}$
${\text{DeepDDS}}_{GAT}$42	${0.93}_{\left(0.01\right)}$	${0.93}_{\left(0.01\right)}$	${0.84}_{\left(0.07\right)}$	${0.85}_{\left(0.07\right)}$	${0.85}_{\left(0.07\right)}$
${\text{DeepDDS}}_{GCN}$42	${0.93}_{\left(0.01\right)}$	${0.92}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$	${0.85}_{\left(0.01\right)}$	${0.84}_{\left(0.01\right)}$
Mol-GDL	${\mathbf{0.94}}_{\left(0.01\right)}$	${\mathbf{0.94}}_{\left(0.01\right)}$	${\mathbf{0.86}}_{\left(0.01\right)}$	${\mathbf{0.86}}_{\left(0.01\right)}$	${\mathbf{0.86}}_{\left(0.01\right)}$

Note that the subindex indicates standard deviation values. Bolding indicates best results.