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

Table 1 Basic information of kinase datasets used in this work

Kinase family	Kinase full name^a	Short name	Total^b	Active	Inactive	Ratio^c
TK	Epidermal growth factor receptor erbB1	EGFR	8718	4514	4286	0.52
TKL	Serine/threonine-protein kinase B-raf	BRAF	4669	3629	1132	0.78
CAMK	Serine/threonine-protein kinase PIM1	PIM1	4437	3322	1185	0.75
Atypical	Serine/threonine-protein kinase mTOR	mTOR	3924	3390	745	0.86
AGC	Serine/threonine-protein kinase AKT	AKT1	3904	2175	1792	0.56
Other	Serine/threonine-protein kinase Aurora-A	AURKA	3854	2286	1596	0.59
TK	Tyrosine-protein kinase BTK	BTK	2560	1894	746	0.74
CMGC	Cyclin-dependent kinase 2	CDK2	2531	1302	1301	0.51
STE	Mitogen-activated protein kinase kinase kinase kinase 2	MAP4K2	891	280	617	0.31
CK1	Casein kinase I alpha	CK1	801	154	653	0.19

ISSN: 1758-2946