Home Heroes De Novo Discovery Gene Scan CDx Pathway ♻️ Repurposing 🚦 Gates Models Quantum 🧪 Wet Lab Schedules 🎛️ Control 🧬 CIFOG 📈 Milestones 🧬 BioBrain 🧠 NPEAD Architecture 🔬 RODE 🧠 Cortex-R Workspace 🧠 V2 🧠 V3 📦 Knowledge 💰 Revenue 🧪 Tests 📊 Costs 📚 Datasets

📋 Product & Inventory Catalog

Update: All 338 compounds now dual-indication (prostate + pancreatic). ATP1A4 promoted to HIGH priority.

Generated from product_inventory_joined.json — 338 compounds · 28 products

Platform Technology

ID	Product	Type	Price	Description
MNQP-001	MindNetQ Technology	GPU-Scalable AI Governance	$5K-$150K+/mo	Distributed GPU-scalable AI governance. Service sharding, authority management, evidence chains, quantum gate.
MNQP-002	E-DICE Core SDK	Governance SDK	$2.5K-$25K+/mo	Bounded decision-making for any AI. Authority contraction/expansion, PQM/QDM/QCM modules. Python/Rust/C.
MNQP-003	MindNetQ Cortex Engine	Drug Discovery Engine	$25K/mo or $50K-$150K/project	14-stage evidence-governed drug discovery pipeline. Multi-engine docking, GenChem AI, evidence certificates.

Platform Access Tiers

ID	Tier	Price	Description
MNQA-001	Explorer	$60K/year	2 discovery runs/year, dashboard access, quarterly reports.
MNQA-002	Professional	$180K/year	Unlimited runs, priority support, custom signal development, API access.
MNQA-003	Enterprise	$500K/year	Dedicated instance, multi-cancer scanning, data integration, named support team.
MNQA-004	On-Premise Deployment	$250K + $120K/year	MindNetQ Cortex inside your infrastructure. Data stays in your network.
MNQA-005	Federated Multi-Site	$500K + $200K/year	Governed federated learning across sites. No raw data shared.

Services

ID	Service	Timeline	Price	Description
MNQS-001	Feasibility Study	2 weeks	$15K	Quick signal check on your dataset. Yes/maybe/no answer.
MNQS-002	Discovery Pilot	6 weeks	$40K	Full governed pipeline run with novel candidates, mechanism analysis, drug links, evidence package.
MNQS-003	Multi-Cohort Evaluation	8 weeks	$150K	Signal applied across 2-3 patient cohorts with subgroup and outcome comparison.
MNQS-004	Validation Protocol Design	Project-based	$25K	Wet-lab validation protocol design for your biomarker.
MNQS-005	Analytical Support	Monthly	$10K/month	Computational support during lab validation, reanalysis and evidence updates.
MNQS-006	Regulatory Evidence Package	Project-based	$50K	FDA-ready evidence package with governed evidence chain and reproducibility support.
MNQS-007	Clinical Validation Engagement	4-6 weeks	$300K-$400K	Patient scores, outcome comparison, subgroup analysis, decision-ready recommendation.
MNQS-008	Discovery Vault Access	Annual	$50K/year	10,606 computationally characterized compounds with full evidence chains — 338 pipeline-validated candidates across 4 targets, plus thousands of drug-like molecules available for repurposing against new targets.
MNQS-009	Custom Signal Development	Project-based	$80K new / $40K validation	New signal discovery or validation of existing signal. Future license after validation.

Therapeutic Programs (with Compounds)

METAP1D Drug Discovery Program — 12 compounds · Priority: HIGH · $500K + milestones + royalties

Target: METAP1D · Best Vina: -8.51 · Avg QED: 0.806 · Stages: {'advanced_validated': 8, 'grade_a': 4}

#	ID	Vina	QED	MW	Stage	Priority	SMILES
1	EGEN-H1	-8.51	0.847	282.3	advanced_validated	HIGH	O=C(Nc1ccncc1)SC1=CC=CC2=NC=C=CN12
2	H7-METAP1D	-8.31	0.833	283.3	advanced_validated	HIGH	O=C(Nc1ccncc1)Sc1cccc2c1=NC=[N+]N=2
3	H3-METAP1D	-8.3	0.937	329.5	advanced_validated	HIGH	c1cc(SC2CCOCC2C2CCNCC2)c2nccnc2c1
4	H5-METAP1D	-8.0	0.897	285.3	advanced_validated	HIGH	O=C(O)C1CNCCC1CCc1cccc2nccnc12
5	H6-METAP1D	-7.76	0.727	270.3	advanced_validated	HIGH	O=C(Nc1ccncc1)SC1=CC=CC2=NC=CN12
6	H4-METAP1D	-7.76	0.73	282.3	advanced_validated	HIGH	O=C(Nc1ccncc1)Sc1cccc2c1N=CN=C2
7	EGEN-H9	-7.76	0.73	282.3	advanced_validated	HIGH	O=C(Nc1ccncc1)Sc1cc2ccnccnc1-2
8	EGEN-14	-7.61	0.922	293.4	advanced_validated	HIGH	O=S(=O)(NC1CCCC1)c1cccc(C2CCCC2)c1
9	METAP1D-A1	-8.4	0.73	282	grade_a	MEDIUM	O=C(Nc1ccncc1)Sc1cccc2nccnc12
10	METAP1D-A2	-8.2	0.762	212	grade_a	MEDIUM	c1cc(CC2CCCC2)c2nccnc2c1
11	METAP1D-A3	-7.7	0.799	301	grade_a	MEDIUM	NS(=O)(=O)c1ccccc1Oc1cccc2nccnc12
12	METAP1D-A4	-7.7	0.759	236	grade_a	MEDIUM	c1cc(NCc2ccncc2)c2nccnc2c1

CRYZ Drug Discovery Program — 100 compounds · Priority: HIGH · $500K + milestones + royalties

Target: CRYZ · Best Vina: -7.5 · Avg QED: 0.719 · Stages: {'wet_lab_ready': 2, 'grade_a': 3, 'candidate': 95}

#	ID	Vina	QED	MW	Stage	Priority	SMILES
1	CRYZ-021	-7.5	0.898	278.3	wet_lab_ready	HIGH	NS(=O)(=O)OCCc1cccc(-c2ccncc2)c1
2	CRYZ-002	-7.5	0.889	283.3	wet_lab_ready	HIGH	FC(F)(F)C1CNCCC1Cc1ccnc2[nH]ccc12
3	CRYZ-068	-7.4	0.772	316	grade_a	MEDIUM	O=S(=O)(Nc1ccncc1)OCc1cccc2ncncc12
4	CRYZ-062	-7.3	0.768	273	grade_a	MEDIUM	CNC(=O)NC(=O)CCNc1cccc2ncncc12
5	CRYZ-045	-7.3	0.74	213	grade_a	MEDIUM	FC(F)(F)Nc1cccc2ncncc12
6	CRYZ-029		0.9	252.2	candidate	UNRANKED	FC(F)(F)CNc1cccc(-c2ccncc2)c1
7	CRYZ-053		0.895	243.3	candidate	UNRANKED	c1cc(OCC2CCNCC2)c2cncnc2c1
8	CRYZ-051		0.89	250.3	candidate	UNRANKED	CS(=O)(=O)NCCc1cccc2ncccc12
9	CRYZ-040		0.883	246.3	candidate	UNRANKED	FC1CNCCC1Oc1cccc2ncccc12
10	CRYZ-085		0.882	296.2	candidate	UNRANKED	O=C(NC(F)(F)F)OCc1cccc(-c2ccncc2)c1
11	CRYZ-034		0.872	242.3	candidate	UNRANKED	NC(=O)CCOc1cccc(-c2ccncc2)c1
12	CRYZ-037		0.864	228.3	candidate	UNRANKED	NC(=O)COc1cccc(-c2ccncc2)c1
13	CRYZ-098		0.859	255.3	candidate	UNRANKED	O=C(NC1CCNCC1)c1cccc2ncccc12
14	CRYZ-035		0.857	231.3	candidate	UNRANKED	CNC(=O)CCOc1cccc2ncncc12
15	CRYZ-086		0.853	301.8	candidate	UNRANKED	ClC1CNCCC1NCc1cccc(-c2ccncc2)c1
16	CRYZ-097		0.841	256.3	candidate	UNRANKED	O=S(=O)(NF)OCc1cccc2ncccc12
17	CRYZ-049		0.839	215.3	candidate	UNRANKED	CNC(=O)CCc1cccc2ncncc12
18	CRYZ-069		0.838	291.3	candidate	UNRANKED	NC(=O)CNS(=O)(=O)c1cccc(-c2ccncc2)c1
19	CRYZ-046		0.836	245.2	candidate	UNRANKED	NC(=O)NC(=O)COc1cccc2ncccc12
20	CRYZ-025		0.834	227.3	candidate	UNRANKED	NC(=O)CNc1cccc(-c2ccncc2)c1
21	CRYZ-057		0.834	287.4	candidate	UNRANKED	O=C(NC1CCNCC1)Sc1cccc2ncccc12
22	CRYZ-077		0.831	267.3	candidate	UNRANKED	O=S(=O)(NF)Nc1cccc(-c2ccncc2)c1
23	CRYZ-087		0.828	257.3	candidate	UNRANKED	CCONC(=O)Nc1cccc(-c2ccncc2)c1
24	CRYZ-013		0.814	278.3	candidate	UNRANKED	O=S(=O)(CCc1cccc(-c2ccncc2)c1)NO
25	CRYZ-059		0.812	238.3	candidate	UNRANKED	CONS(=O)(=O)c1cccc2ncccc12
26	CRYZ-090		0.808	215.2	candidate	UNRANKED	FC(F)(F)CNc1ccnc2[nH]ccc12
27	CRYZ-026		0.803	253.3	candidate	UNRANKED	CS(=O)(=O)NCOc1cccc2ncncc12
28	CRYZ-050		0.796	216.2	candidate	UNRANKED	CNC(=O)CNc1cccc2ncncc12
29	CRYZ-012		0.795	202.2	candidate	UNRANKED	O=C(O)CNc1cccc2ncccc12
30	CRYZ-060		0.788	265.3	candidate	UNRANKED	CS(=O)(=O)NNC(=O)c1cccc2ncccc12
31	CRYZ-001		0.787	208.6	candidate	UNRANKED	ClONCc1cccc2ncccc12
32	CRYZ-065		0.787	203.2	candidate	UNRANKED	NC(=O)COc1cccc2ncncc12
33	CRYZ-067		0.787	239.3	candidate	UNRANKED	CS(=O)(=O)NOc1cccc2ncncc12
34	CRYZ-018		0.781	227.2	candidate	UNRANKED	NS(=O)(=O)COc1ccnc2[nH]ccc12
35	CRYZ-074		0.78	264.3	candidate	UNRANKED	NC(=O)Cc1cnccc1-c1cccc2ncncc12
36	CRYZ-095		0.778	305.4	candidate	UNRANKED	c1cc(Nc2ccncc2C2CCNCC2)c2cncnc2c1
37	CRYZ-079		0.773	203.2	candidate	UNRANKED	O=C(O)NCc1cccc2ncncc12
38	CRYZ-014		0.772	250.3	candidate	UNRANKED	c1cc(CNCc2ccncc2)c2cncnc2c1
39	CRYZ-071		0.772	232.2	candidate	UNRANKED	CNNC(=O)OCc1cccc2ncncc12
40	CRYZ-047		0.769	202.2	candidate	UNRANKED	CNC(=O)Oc1cccc2ncccc12
41	CRYZ-041		0.767	186.2	candidate	UNRANKED	NC(=O)Cc1cccc2ncccc12
42	CRYZ-011		0.765	241.3	candidate	UNRANKED	CS(=O)(=O)NOCc1ccnc2[nH]ccc12
43	CRYZ-023		0.761	192.2	candidate	UNRANKED	O=C(O)COc1ccnc2[nH]ccc12
44	CRYZ-061		0.758	258.7	candidate	UNRANKED	Clc1cnccc1CNc1ccnc2[nH]ccc12
45	CRYZ-088		0.757	235.3	candidate	UNRANKED	c1cc(NCc2ccncc2)c2cccnc2c1
46	CRYZ-072		0.753	230.3	candidate	UNRANKED	NCNC(=O)CCc1cccc2ncncc12
47	CRYZ-024		0.749	317.4	candidate	UNRANKED	O=S(=O)(Nc1ccncc1)Sc1cccc2ncccc12
48	CRYZ-010		0.747	188.2	candidate	UNRANKED	CNOCc1cccc2ncccc12
49	CRYZ-099		0.747	188.2	candidate	UNRANKED	CONCc1cccc2ncccc12
50	CRYZ-009		0.746	189.2	candidate	UNRANKED	COCNc1cccc2ncncc12
... and 50 more compounds (see full JSON)

ATP1A4 Drug Discovery Program — 3 compounds · Priority: MEDIUM · $250K co-development

Target: ATP1A4 · Best Vina: -7.4 · Avg QED: 0.757 · Stages: {'grade_a': 3}

#	ID	Vina	QED	MW	Stage	Priority	SMILES
1	ATP1A4-01	-7.4	0.593	384	grade_a	MEDIUM	Steroid-like scaffold
2	ATP1A4-02	-7.3	0.782	413	grade_a	MEDIUM	Ion pump (cardiac glycoside)
3	ATP1A4-03	-7.2	0.896	292	grade_a	MEDIUM	Decahydronaphthalene

BRD4 Drug Discovery Program — 223 compounds · Priority: LOW · $50K-$150K non-exclusive

Target: BRD4 · Best Vina: -11.24 · Avg QED: 0.764 · Stages: {'parked': 223}

#	ID	Vina	QED	MW	Stage	Priority	SMILES
1	C3	-11.24	0.695	441.5	parked	LOW	NS(=O)(=O)OC(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC
2	C158	-11.18	0.511	436.5	parked	LOW	NC(=O)OC[C@@H](O)CONc1ccoc1-c1csc(N2CCC[C@@H]2C2CC
3	C156	-11.09	0.555	449.5	parked	LOW	C[C@H](NOC(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)
4	C6	-11.03	0.732	424.5	parked	LOW	NS(=O)(=O)C(=O)CNc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2
5	C4	-11.0	0.719	427.5	parked	LOW	O=C(O)NS(=O)(=O)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2
6	C11	-11.0	0.719	399.5	parked	LOW	O=S(=O)(NO)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
7	C213	-10.9	0.599	412.5	parked	LOW	O=S(=O)(CNc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1)NO
8	C5	-10.89	0.756	425.5	parked	LOW	NS(=O)(=O)C(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2
9	C16	-10.89	0.677	440.5	parked	LOW	NC(=O)NS(=O)(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC
10	C7	-10.88	0.672	412.5	parked	LOW	NS(=O)(=O)OCNc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
11	C21	-10.88	0.673	412.5	parked	LOW	NS(=O)(=O)CNOc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
12	C84	-10.88	0.672	413.5	parked	LOW	O=S(=O)(COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1)NO
13	C78	-10.83	0.652	428.6	parked	LOW	NS(=O)(=O)CNSc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
14	C105	-10.77	0.627	426.6	parked	LOW	CNNS(=O)(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
15	C2	-10.76	0.704	439.6	parked	LOW	NS(=O)(=O)OC(=O)CCc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC
16	C9	-10.76	0.704	439.6	parked	LOW	NS(=O)(=O)C(=O)CCOc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC
17	C18	-10.74	0.696	413.5	parked	LOW	CONS(=O)(=O)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
18	C23	-10.74	0.695	413.5	parked	LOW	NS(=O)(=O)OCOc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
19	C32	-10.71	0.684	441.6	parked	LOW	NS(=O)(=O)C(=O)CSc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2
20	C36	-10.7	0.682	422.5	parked	LOW	N#CCNS(=O)(=O)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n
21	C64-phenyl-SO2	-8.61	0.593	487.7	parked	LOW	CS(=O)(=O)NS(=O)(=O)Sc1ccccc1-c1csc(N2CCC[C@@H]2C2
22	C64	-8.5	0.58	478.6	parked	LOW	NS(=O)(=O)NS(=O)(=O)Sc1ccoc1-c1csc(N2CCC[C@@H]2C2C
23	C237	-8.09	0.539	422.5	parked	LOW	N#CNS(=O)(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n
24	C31	-8.06	0.649	425.5	parked	LOW	O=CCNS(=O)(=O)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n
25	GC-0020	-7.87	0.883	377.5	parked	LOW	NS(=O)(=O)c1ccccc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
26	C69	-7.86	0.577	408.5	parked	LOW	N#CNS(=O)(=O)Oc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
27	C76	-7.83	0.604	411.6	parked	LOW	NS(=O)(=O)NCNc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
28	C64-phenyl-monoSO2	-7.8	0.747	409.6	parked	LOW	NS(=O)(=O)Sc1ccccc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
29	GC-0007	-7.72	0.737	362.5	parked	LOW	Fc1ccc(CCS)c(-c2csc(N3CCC[C@@H]3C3CCC3)n2)c1
30	C29	-7.7	0.677	412.5	parked	LOW	NS(=O)(=O)NCOc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
31	GC-0034	-7.63	0.866	347.5	parked	LOW	NCCOc1cc(F)ccc1-c1csc(N2CCC[C@@H]2C2CC2)n1
32	GC-0068	-7.57	0.771	375.5	parked	LOW	NOC(=O)CCc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
33	C92	-7.45	0.44	425.6	parked	LOW	NS(=O)(=O)CNCNc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
34	GC-0115	-7.44	0.739	364.5	parked	LOW	O=C(O)Sc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
35	GC-0132	-7.32	0.868	345.5	parked	LOW	NC(=O)CCc1ccoc1-c1csc(N2CCC[C@@H]2C2CCC2)n1
36	GC-0097	-7.31	0.757	357.4	parked	LOW	NS(=O)(=O)Nc1ccc(F)cc1-c1csc(N2CCNCC2)n1
37	GC-0105	-7.29	0.732	390.0	parked	LOW	NCCc1ccc(Cl)cc1-c1csc(N2CCC[C@@H]2C2CCCCC2)n1
38	GC-0134	-7.21	0.867	377.5	parked	LOW	NS(=O)(=O)Cc1ccccc1-c1csc(N2CCC[C@@H]2C2CCC2)n1
39	GC-0031	-7.12	0.715	352.4	parked	LOW	Cc1noc(C)c1-c1ccc2c(c1)C(=O)N2c1csc(N2CCC2)n1
40	GC-0033	-7.12	0.939	324.8	parked	LOW	OCCc1ccc(Cl)cc1-c1cnc(N2CCOCC2)s1
41	GC-0018	-7.11	0.859	348.4	parked	LOW	O=C(O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCC2)n1
42	GC-0094	-7.11	0.754	405.5	parked	LOW	O=C(O)NC(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
43	GC-0180	-7.1	0.713	371.5	parked	LOW	N#CC(=O)COc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
44	GC-0181	-7.02	0.93	339.8	parked	LOW	NC(=O)Oc1ccc(Cl)cc1-c1cnc(N2CCOCC2)s1
45	GC-0173	-6.99	0.717	358.5	parked	LOW	O=CCc1ccc(F)cc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
46	GC-0075	-6.97	0.783	353.4	parked	LOW	NC(=O)Cc1ccc2ccccc2c1-c1cnc(N2CCOCC2)s1
47	GC-0145	-6.97	0.827	382.5	parked	LOW	NS(=O)(=O)Nc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
48	GC-0076	-6.96	0.372	408.5	parked	LOW	COOC(=O)CSc1ccoc1-c1csc(N2CCC[C@@H]2C2CCCC2)n1
49	GC-0083	-6.94	0.87	369.5	parked	LOW	NS(=O)(=O)Oc1ccoc1-c1csc(N2CC[C@@H]2C2CCCC2)n1
50	GC-0099	-6.93	0.73	335.4	parked	LOW	N#CCc1cc2cccccc2c1-c1cnc(N2CCOCC2)s1
... and 173 more compounds (see full JSON)

Diagnostic & Prognostic Kits

ID	Kit	Type	Signal	Drug Class	Evaluation	License
MNQD-001	PRR36/DNAH5 CDx	Companion Diagnostic	3.7x prostate, 4.4x breast, 11.6x lung, 14.1x colon	BET inhibitors	$40K biotech / $250K mid pharma / $500K big pharma	Exclusive: $250K+4%. Non-exclusive: $100K+3%. Co-dev: $0+8%.
MNQD-002	STX19 CDx	Companion Diagnostic	2.35x prostate	EGFR inhibitors	$40K / $250K / $500K	Exclusive: $200K+4%. Non-exclusive: $80K+3%.
MNQD-003	ARSF Prognostic	Prognostic	High=687d vs 1302d survival	Sulfatase inhibitors	$40K / $250K / $500K	$150K+4% royalty.
MNQD-004	TM7SF2 Prognostic	Prognostic (protective)	High=1249d vs 653d survival	TBD	$40K / $250K / $500K	Protective prognostic package.
MNQD-005	Multi-Gene Panel	Panel	PRR36+STX19+TM7SF2	Multiple	Included in panel	Custom
MNQD-006	Immunotherapy Exclusion	Exclusion	Non-responder identification	Checkpoint inhibitors	$40K / $250K / $500K	Custom
MNQD-007	Subtype Classifier	Classifier	Group 1 vs Group 2	Treatment selection	$40K / $250K / $500K	Custom

Source: product_inventory_joined.json · Generated 2026-05-19 · MindNetQ Inc.

Two Entry Points: Cortex (agentic AI — reasons, learns, monitors) + MindNetQ Cortex (governed multi-omics — executes, audits, produces Research Objects). They enrich each other. Every interaction makes both smarter.