Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Kong, Yue; Zhao, Xiaoman; Liu, Ruizi; Yang, Zhenwu; Yin, Hongyan; Zhao, Bowen; Wang, Jinling; Qin, Bingjie; Yan, Aixia

doi:10.1186/s13321-022-00634-3

Table 3 Model performance on kinase datasets

From: Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Dataset	Splitting	MPNN		AttentiveFP		RG-MPNN
Dataset	Splitting	MCC	AUC	MCC	AUC	MCC	AUC
EGFR	Random	0.699 ± 0.022	0.923 ± 0.009	0.729 ± 0.017	0.933 ± 0.010	0.738 ± 0.029	0.942 ± 0.008
EGFR	Scaffold	0.648 ± 0.03	0.901 ± 0.010	0.684 ± 0.029	0.908 ± 0.006	0.706 ± 0.020	0.925 ± 0.007
BRAF	Random	0.703 ± 0.052	0.916 ± 0.020	0.753 ± 0.035	0.938 ± 0.006	0.774 ± 0.020	0.915 ± 0.012
BRAF	Scaffold	0.614 ± 0.040	0.883 ± 0.006	0.685 ± 0.018	0.905 ± 0.008	0.677 ± 0.044	0.915 ± 0.006
PIM1	Random	0.691 ± 0.069	0.933 ± 0.029	0.741 ± 0.026	0.957 ± 0.009	0.758 ± 0.029	0.951 ± 0.013
PIM1	Scaffold	0.612 ± 0.050	0.881 ± 0.024	0.681 ± 0.021	0.921 ± 0.006	0.710 ± 0.042	0.928 ± 0.007
mTOR	Random	0.591 ± 0.037	0.888 ± 0.036	0.641 ± 0.033	0.927 ± 0.010	0.674 ± 0.038	0.921 ± 0.008
mTOR	Scaffold	0.408 ± 0.060	0.792 ± 0.033	0.588 ± 0.022	0.876 ± 0.011	0.574 ± 0.015	0.886 ± 0.009
AKT1	Random	0.669 ± 0.068	0.905 ± 0.035	0.751 ± 0.038	0.933 ± 0.011	0.771 ± 0.031	0.941 ± 0.014
AKT1	Scaffold	0.605 ± 0.041	0.883 ± 0.016	0.657 ± 0.029	0.914 ± 0.010	0.649 ± 0.023	0.910 ± 0.008
AURKA	Random	0.634 ± 0.061	0.892 ± 0.020	0.665 ± 0.021	0.909 ± 0.010	0.690 ± 0.028	0.917 ± 0.007
AURKA	Scaffold	0.471 ± 0.031	0.793 ± 0.012	0.475 ± 0.023	0.807 ± 0.008	0.522 ± 0.037	0.836 ± 0.006
BTK	Random	0.670 ± 0.065	0.915 ± 0.022	0.748 ± 0.083	0.947 ± 0.017	0.759 ± 0.042	0.954 ± 0.017
BTK	Scaffold	0.545 ± 0.044	0.849 ± 0.021	0.626 ± 0.044	0.902 ± 0.014	0.682 ± 0.031	0.893 ± 0.015
CDK2	Random	0.567 ± 0.041	0.865 ± 0.019	0.624 ± 0.074	0.886 ± 0.021	0.652 ± 0.050	0.902 ± 0.013
CDK2	Scaffold	0.376 ± 0.052	0.752 ± 0.024	0.412 ± 0.026	0.773 ± 0.022	0.495 ± 0.015	0.820 ± 0.008
MAP4K2	Random	0.457 ± 0.131	0.792 ± 0.059	0.484 ± 0.123	0.813 ± 0.041	0.540 ± 0.046	0.863 ± 0.025
MAP4K2	Scaffold	0.174 ± 0.039	0.578 ± 0.037	0.277 ± 0.03	0.652 ± 0.033	0.306 ± 0.045	0.706 ± 0.034
CK1	Random	0.156 ± 0.162	0.673 ± 0.111	0.313 ± 0.128	0.751 ± 0.047	0.433 ± 0.090	0.800 ± 0.065
CK1	Scaffold	− 0.020 ± 0.051	0.576 ± 0.029	0.159 ± 0.084	0.653 ± 0.011	0.333 ± 0.107	0.687 ± 0.033

Note that the best AUC and MCC for each kinase target are in bold

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