A reasoning architecture for drug discovery — from disease, through target identification, molecular design, and clinical-grade validation. Built where graph reasoning, diffusion generation, and quantum simulation each take their turn being the authority.
Six layers. Twenty-six knowledge bases. Thirty-seven layers of OMNIOME. One closed-loop discipline that always returns a classical baseline first — and reaches for quantum only when correlation collapses classical certainty.
What follows is what QDDA actually does, in the order it does it. Not a marketing diagram — the operational sequence by which a disease label becomes a clinical-grade candidate molecule.
A clinical phenotype enters the architecture — coronary artery disease, glioblastoma, septic shock, frontotemporal dementia.
L1–L37 OMNIOME indexQDDA traces the disease through 37 OMNIOME layers — genomic, transcriptomic, proteomic, metabolomic, pharmacoepi — surfacing the layers where causal signal lives.
causal evidence ≥ 0.85A druggable target emerges from the registry — PCSK9, KRAS-G12C, NLRP3, α-synuclein. Druggability score, binding pocket, and existing-drug landscape all attached.
KB-1 · 124 targetsRDTR scaffold reasoning + diffusion generates candidate molecules conditioned on pocket geometry. Boltz-2 confirms binding pose. AlphaFold 3 ratifies complex.
RDTR · TargetDiff · DiffSBDDClassical baseline runs first — always. When confidence is insufficient, the HAL invokes VQE-ADAPT on heavy-hex topology with full mitigation. Cross-platform verification on critical results.
classical-first · S3 mandatoryTrial design templates, RWE phenotype definitions, HEOR cost-effectiveness, regulatory dossiers — the candidate becomes a clinical-grade asset across CDSCO, FDA, EMA jurisdictions.
KB-18 · KB-19 · KB-20And the loop is closed — pharmacoepi signal from L37 returns to refine the next descent.
QDDA is not a pipeline. It is a closed-loop reasoning system in which graph neural networks, diffusion generators, and quantum simulators take turns being the authority — depending on what the molecule, the target, and the trial demand of them.
Every molecule is a graph. Every target is a graph. Every pathway is a graph. A twenty-architecture catalogue from GIN through MACE and Equiformer-v2 routes the right graph through the right reasoning step. Foundation models — TxGNN, Boltz-2, AlphaFold 3 — are first-class primitives, with licensing and deployability tracked per model.
Score-based diffusion models — TargetDiff, DiffSBDD, DecompDiff, DiffLinker, RFdiffusion — generate molecule-target pairs conditioned on pocket geometry, scaffold constraint, or pharmacophore. The RDTR loop reasons, diffuses, tests, and refines until the candidate survives both novelty and activity gates.
VQE-ADAPT, QAOA, hardware-efficient ansätze, with rigorous error mitigation (REM, ZNE, dynamical decoupling, symmetry verification) and twenty HAL routing rules that guarantee no single-vendor dependency. Quantum is invoked only when classical confidence is insufficient. Always.
The intellectual substrate of QDDA. Each knowledge base is a structured artifact — not a marketing artefact. Drug-gene interactions, severe AE signals, RWE phenotype definitions, ICER thresholds, foundation-model licensing matrices. Diligence-grade. Auditable. Composed.
The full architecture comprises twenty-six structured knowledge bases across science, substrate, clinical regulation, and infrastructure. Detailed scope shared with qualified partners under MNDA.
QDDA's hardware-abstraction layer routes quantum workloads across a federated portfolio of compute partners — superconducting, trapped-ion, and photonic. No single vendor becomes a critical dependency. Failover is guaranteed within four hours. Classical baseline always returns first.
QDDA's classical baseline is mandatory. The S3 reasoning layer always returns. Quantum is invoked only when classical confidence falls below threshold — and even then, only after symmetry verification, dynamical decoupling, and zero-noise extrapolation have been applied. The architecture is built around a discipline of restraint.
The hardware-abstraction layer enforces twenty routing rules across heterogeneous backends: IBM superconducting (heavy-hex), IonQ and Quantinuum trapped-ion, AWS Braket aggregator, academic-tier substrates, and on-premise simulators for confidential workloads. No single vendor becomes a critical dependency. Failover to alternate topology is guaranteed within four hours.
From GNN-ADMET in Q2 2026 through autonomous patient-trajectory closed loops by 2028. Every milestone has gates, fail criteria, and operating characteristics — never aspirational bullet points.
QDDA is composed, not closed. Research collaborators, pharma R&D teams, academic quantum centres, and regulators each find a different route in — and the architecture is built to honour all three.
For pharmaceutical R&D, biotech labs, and clinical research organisations who want QDDA's full reasoning loop in their own discovery pipeline. Federated, sovereignty-preserving, deployable into your environment.
For academic quantum computing centres, university research labs, and pre-clinical groups who want to contribute to the architecture itself. Joint IP. Shared milestone tracking. Publications under combined attribution.
For regulatory bodies, HTA agencies, and policy researchers who need to understand the architecture before it becomes evidence. Briefings. Whitepapers. Explainable provenance for every output.
QDDA is built around a single conviction — that discovery is reasoning under uncertainty, and that the institutions equipped to do this well are the ones that compose the right primitives, in the right order, with the right discipline.
Graph reasoning when the world is relational. Diffusion when generation must be guided. Quantum when correlation collapses classical certainty. Federation when sovereignty demands it. Foundation models when the prior should be other people's labour. And always — always — a classical baseline that returns first.
If you are reading this and recognising your own work in it — the same care for evidence, the same restraint about claims, the same patience for what real science requires — that recognition is the beginning of a partnership.