Integrating High-Density Wet-lab Data, AI Inference, and Quantum Optimization into a Single Loop.
Our closed-loop system improves accuracy with every cycle.
Using 200K Peptide Microarray to physically measure protein interactions. Generating ‘Ground Truth’ data.
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GNN/Transformer models learn interaction rules from physical data to predict binders from billions of libraries.
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Using VQE/QAOA algorithms to calculate exact binding affinity & stability at a sub-atomic level.
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Top candidates are synthesized and assayed the very next day. Results feed back to Phase 01.
Three layers of innovation defined.
Physical Asset
Proprietary Data Factory. We own the mine, while others buy the ore.
Format: High-Density Peptide Microarray
Capacity: 200,000+ features/chip
Throughput: 1,000+ assays daily
Inference Engine
Physics-Informed AI. Trained on specific, experimentally validated interaction data.
Architecture: GNN + NLP Transformer
Training Data: 100% In-house Wet-lab
Advantage: Zero Hallucination
Quantum Advantage
Quantum Hamiltonian Solver. Nature is quantum; we use quantum computers to understand it.
Algorithm: VQE (Quantum Eigensolver)
Target: Global Minimum Energy
Tech: Gate-based Quantum Circuits
Why GeneOn outperforms traditional methods.
| Metric | Traditional Pharma | AI-Only Biotech | GeneOn (QUAI) |
|---|---|---|---|
| Data Source | Public DB / Literature | Public DB (Noisy) | In-house Microarray (100% Valid) |
| Hit Rate | < 1% | 5 ~ 10% | > 40% (Verified) |
| Optimization | Trial & Error | Simulation | Quantum Energy Calculation |
| Cycle Time | Months | Weeks | 24 Hours (Daily Loop) |
| Cost | High ($$$$) | Medium ($$) | Low ($) |
1. Microarray Reliability: Validated via Surface Plasmon Resonance (SPR) correlation (R² > 0.95).
2. Quantum Utility: VQE algorithm application based on Peruzzo et al. (Nature Communications).
3. Data Integrity: Closed-loop active learning reduces false positives by 80%.
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