MindNetQ provides governed AI for multi-omics discovery and distributed scientific workflows
MindNetQ provides a governance layer for AI systems operating across E-DICE Core substrates. It controls how multi-step AI workflows execute, tying decisions to evidence, reproducibility, and policy constraints across distributed environments.
What MindNetQ is
A governance layer that controls how AI-driven discovery workflows execute — from raw data to a ranked, auditable candidate hypothesis.
Governed molecular discovery
We take governance to the molecular level. MindNetQ screens thousands of genes across six molecular data types, applies strict quality gates that eliminate over 95% of candidates, evaluates structural druggability using AlphaFold and binding pocket detection, and ranks molecular intervention targets.
Reproducible and auditable execution
Every step is governed. From raw data to molecular hypothesis, each decision is traceable, auditable, and reproducible, producing controlled discovery workflows rather than isolated analysis.
End-to-end discovery system
This is governed target discovery, druggability assessment, and compound ranking in a single system designed for distributed research environments with built-in validation and evidence tracking.
How governance works
Three interlocking mechanisms — evidence authority, deterministic control, and defensive oversight — enforce correctness across every step.
Evidence and authority
- Trust computation derived from execution history
- Authority decay applied to stale or insufficient evidence
- Authority-based routing that ties compute allocation to evidence strength
- Delegation leases binding purpose, scope, and expiry to each operation
Deterministic control
- Four-layer deterministic governance cache
- Identical outputs for identical inputs
- Learned substrate selection based on execution conditions
- Replayable, audit-ready execution traces
Defensive oversight
- Detection across ten violation types
- Six defensive actions with parallel triggered responses
- SHA-256 hash-chained logging for every governance decision
- Active patent mapping across nine provisional filings
System architecture
One governance layer. Multiple domain-scoped substrates. Unified policy enforcement on secure cloud infrastructure.
MindNetQ instance
One governance layer operates across multiple domain-scoped substrates under unified policy enforcement.
E-DICE-CORE-R
Supports governed multi-omics cancer discovery workflows and evidence-aware substrate execution.
E-DICE-CORE-CSH
Supports healthcare workflow execution under domain-scoped policies and bounded delegation control.
Cloud deployment
Active secure cloud deployment with full passing status across 48 defined safety tests.
Governed discovery workflow
Every stage of the pipeline runs under governance — from raw multi-omics inputs through ranked, auditable molecular targets.
First governed discovery result
Surfaced autonomously from public TCGA data — no human guidance. The first signal from a governed platform.
A governed multi-omics workflow screened 19,010 genes and identified PRR36 as a prostate cancer candidate with no prior indexed publications at the time of discovery. The signal was produced autonomously through governed multi-evidence screening and is under validation for reproducibility, biological consistency, and clinical relevance.
Genes screened in the governed workflow
Technical contribution
Novel formal contributions to authority-governed control and reproducible scientific computation.
Authority-governed control framework
- Authority computed from evidence sufficiency and execution state
- Deterministic behavior under uncertainty
- Evidence-weighted routing across compute substrates
- Governance-aware optimization through EGAO
Reproducible scientific computation
- Replayable execution with cryptographic audit trails
- Deterministic caching for identical input paths
- Policy-scoped execution across heterogeneous substrates
- Formal grounding for reviewable multi-step AI systems
Federated and quantum direction
Expanding governance to cross-institution collaboration and hybrid classical-quantum execution.
Federated learning
Enables cross-institution analysis without moving raw data, supporting privacy-aware collaboration and governed model improvement across distributed sites.
Quantum collaboration
Explores hybrid classical and quantum-inspired workflows, including VQE admissibility controls and qubit sufficiency validation to prevent invalid or misleading outputs.
System roadmap
What comes next — optimization, continuous learning, and performance at scale.
Optimization and molecular workflows
- Full EGAC(x) multi-term governed objective function
- VQE multi-metric admissibility gates
- Jordan-Wigner qubit sufficiency validation
Learning and monitoring
- Governance LLM trained on execution data
- Sentinel Agent for continuous trust monitoring
- Learning pipelines driven by execution outcomes
Scale and performance
- Distributed optimization with policy propagation and sharding
- Multi-node governance enforcement
- Rust rewrite targeting sub-5 ms enforcement latency
Team
An interdisciplinary team spanning governed AI architecture, multi-omics biology, clinical research, applied mathematics, and full-stack delivery.
Leads architecture and research across authority-governed AI, deterministic execution control, and E-DICE orchestration. Lead author, SPIE 2026.
Multi-omics analysis, genomics, and biological data integration supporting validation and interpretation.
Formal modeling, numerical methods, and stability analysis supporting correctness and system validation.
Aligns computational outputs with clinical relevance and evidence-based healthcare outcomes. DNP, FNP-BC.
Supports transcriptomics, molecular modeling, and candidate validation workflows.
Builds AI pipelines, backend systems, and cloud deployment for governed execution.
Positioning
Tailored value propositions for research and enterprise partners.
Supports biomarker and target discovery workflows through controlled execution, distributed data collaboration, and structured validation paths.
Establishes a novel authority-governed control framework for multi-step AI workflows with deterministic behavior, formal constraints, and reproducible scientific computation.