Table 1 Top 30 ranked compounds for leukaemia: the table shows the highest ranked compounds along with their predicted targets and the scores for the predicted targets (Target score).

From: Transcriptional drug repositioning and cheminformatics approach for differentiation therapy of leukaemia cells

Rank	Bioinformatics score	Cheminformatics score	Compound name	Retrospective validation for leukaemia	Retrospective validation for other cancers	Selected for experimental validation	Target 1 name	Target 1 score	Target 1 disease score	Target 2 name	Target 2 score	Target 2 disease score	Target 3 name	Target 3 score	Target 3 disease score	Target 4 name	Target 4 score	Target 4 disease score	Target 5 name	Target 5 score	Target 5 disease score	Target 6 name	Target 6 score	Target 6 disease score	Target 7 name	Target 7 score	Target 7 disease score
1	− 0.81	23	Podophyllotoxin	*⁷⁶			EDNRB	26	30	EDNRA	21	11	F3	20	40	PDE11A	13	3	FKBP1A	13	12	CYP3A4	10	44	NR3C1	9	22
2	− 0.79	15	Leflunomide	*⁷⁷			MTTP	21	2	APOB	19	20	DHODH	16	0	ATF1	14	2	TRPV1	13	12	NFKB1	13	46	RAF1	12	22
3	− 0.79	16	Colchicine	*⁷⁸			TUBB1	37	3	F3	8	40	STS	5	24	BDKRB1	2	11	ALK	2	2
4	− 0.79	5	Terazosin			*	EHMT2	20	18	CCR4	18	3	ADRA1B	18	4	ADRA1A	15	3	EHMT1	14	0	UBE2N	14	0	PDPK1	8	9
5	− 0.78	7	Prenylamine		*⁷⁹		ADRB2	18	9	CASR	16	2	ADRB3	13	3	ADRB1	12	6	C3AR1	9	7	CAPN2	8	17	SSTR2	8	2
6	− 0.78	21	Trimethylcolchicinic acid	*⁸⁰			TUBB1	23	3	STS	14	24	DRD1	8	0	F3	7	40	ALK	6	2	ABCC1	6	57	ACHE	5	18
7	− 0.78	29	Etoposide	*⁸¹			NCOA3	30	100	F3	23	40	NCOA1	20	16	RORC	15	0	TOP1	13	17	SLC5A1	12	0
8	− 0.78	14	Mebendazole	*²¹			TEK	30	14	RAF1	20	22	KDR	17	25	F9	10	0	GRB7	6	14	CHEK2	6	22	ITK	5	3
9	− 0.78	5	Adenosine phosphate	*⁸²			RSEL	99	3	IMPDH1	91	4	P2RY2	87	12	P2RY1	70	0	P2RX1	69	0	AHCY	68	12
10	− 0.78	5	Cefoperazone				SLC22A8	72	9	SLC22A6	66	7	CMA1	44	3	ELANE	35	0	PGF	15	5
11	− 0.77	2	Thioridazine	*¹⁸			HRH1	34	0	DRD1	24	0	DRD2	23	3	CHRM5	20	6	DRD3	18	0	CHRM4	18	3	HRH2	18	0
12	− 0.77	14	Nocodazole	*⁸³			TEK	36	14	KDR	21	25	STK33	17	3	ITK	15	3	F9	13	0	RAF1	12	22	ABL1	9	32
13	− 0.77	4	Tetryzoline				ADRA2A	27	4	ADRA2B	24	7	NISCH	23	0	ADRA2C	23	3	BDKRB1	21	11	DRD1	12	0	ADRA1A	12	3
14	− 0.77	20	Wortmannin	*²⁰			PIK3CA	72	11	MTOR	58	23	MYLK	18	17	ABCB1	10	63	SOAT2	10	3	CYP19A1	9	23	ADCY1	9	4
15	− 0.77	15	Tretinoin	*⁸⁴			RARG	65	11	RARB	65	28	RXRA	64	12	RARA	59	20	RXRG	53	11	RXRB	53	8	RBP4	49	11
16	− 0.77	46	Genistein	*⁸⁵			ALDH2	40	15	ESR2	25	16	ESR1	23	100	TOP2A	20	100	XDH	15	17	CYP1A1	15	38	CYP1B1	15	36
17	− 0.76	15	Ly-294002	*⁸⁶			PRKDC	68	18	PIK3CA	38	11	PIK3CB	32	6	PIK3CD	26	24	PIK3CG	25	18	PIK3C2B	14	3	MTOR	11	23
18	− 0.76	4	Proxymetacaine			*	UBE2N	11	0	HTR4	10	0	P2RY12	6	0	PDGFRA	1	4	BCHE	1	18	CHRM4	1	3	MBTPS1	1	3
19	− 0.76	9	Sulfapyridine	*⁸⁷			NTRK1	7	5	CYP2C18	6	10	GRM4	6	0	EDNRA	6	11	HTR6	4	0	EDNRB	4	30	PIK3C3	3	7
20	− 0.75	22	Fenbendazole		*³¹	*	TEK	27	14	AURKB	11	21	RAF1	11	22	KDR	10	25	AURKA	8	13	ITK	7	3	MCL1	5	56
21	− 0.75	4	Mephentermine				CASR	12	2	GHSR	10	4	ADRB2	8	9	ADRB1	6	6	ADRB3	5	3	TACR3	2	3	CCKBR	2	3
22	− 0.75	5	Dobutamine				ADRB2	32	9	ADRB1	31	6	ADRB3	30	3	PGF	5	5	OPRD1	5	0	OPRM1	4	4	KISS1R	4	11
23	− 0.75	11	Clenbuterol				ADRB2	27	9	ADRB1	27	6	ADRB3	18	3	BACE1	3	8	CTSD	1	25	IGF1R	0	16	CYP2D6	-1	11
24	− 0.75	2	Thioridazine (rep)	*¹⁸			HRH1	34	0	DRD1	24	0	DRD2	23	3	CHRM5	20	6	DRD3	18	0	CHRM4	18	3	HRH2	18	0
25	− 0.75	15	Ly-294002 (rep)	*⁸⁶			PRKDC	68	18	PIK3CA	38	11	PIK3CB	32	6	PIK3CD	26	24	PIK3CG	25	18	PIK3C2B	14	3	MTOR	11	23
26	− 0.75	8	Trichostatin a	*⁸⁸			HDAC6	28	3	HDAC10	25	3	HDAC1	24	17	HDAC8	23	3	HDAC2	23	9	HDAC9	23	11	HDAC11	22	7
27	− 0.74	2	Remoxipride	*⁸⁹			DRD2	28	3	DRD3	10	0	UTS2R	9	0	HCRTR1	9	7	HCRTR2	9	0	HTR4	7	0
28	− 0.74	5	Nadide				IMPDH1	105	4	P2RY2	90	12	RSEL	86	3	GAPDH	77	18	P2RX1	74	0	P2RY1	70	0	P2RY4	66	0
29	− 0.74	4	Terfedine	*²⁶			TACR2	28	3	NPY2R	22	7	HRH1	18	0	CHRM3	11	2	KCNH2	11	10	NPC1L1	10	0	CCR5	10	5
30	− 0.74	27	Ouabain	*y²⁶			KLF5	39	0	NR3C1	37	22	ATP12A	30	0	SHBG	28	7	STAT3	24	42	FGF2	23	16	FGF1	23	100

High target scores show high probability of the compound to bind to that protein target based on our in-silico prediction approach. The “Target disease score” shows how much the target is related to the disease according to Comparative Toxicogenomic Database (CTD). Compounds that have retrospective validation and literature support for leukaemia or other cancer types and the ones selected for experimental validation in this work are marked with a * for each relevant column. The Bioinformatics score is the score calculated based on anti-correlation of the compound gene signature and leukaemia differentiation signature. The Cheminformatics score is the rounded average of Target Disease Score for the top seven predicted targets of each compound.

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Table 1 Top 30 ranked compounds for leukaemia: the table shows the highest ranked compounds along with their predicted targets and the scores for the predicted targets (Target score).

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