QuantumCURE Pro – Citizen-Driven, Quantum-Enhanced Drug Discovery

Release: January 2026

From my back office in Oklahoma City, I’ve built a full-stack docking factory that combines classical molecular modeling with quantum entropy, D-Wave annealing seeds, and real QRNG hardware. QuantumCURE Pro is designed so that small labs, independent researchers, and citizen scientists can run serious early-stage drug discovery without a $100K/year license.

 Early-stage drug discovery is still slow, expensive, and out of reach for most people who actually have ideas. Traditional platforms like Schrödinger’s suite are powerful—but they’re optimized for large pharma licensing and classical, PRNG-only workflows.

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QuantumCURE Pro takes a different path. It’s a quantum-enhanced docking and scoring system that:

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• Prepares proteins at scale

• Runs docking pipelines across hundreds of thousands of ligands

• Injects true quantum entropy (QRNG + D-Wave annealing seeds) into the exploration process

• Scores, filters, ranks, and helps you build a wet list you can defend and eventually test in a lab

 

citizenscientist.org and quantumcurepro.com are the front door into this system:  where you can learn how it works, see the tiers, and eventually join me in running massive simulations for real-world targets. The system is due to release in January 2026, but you can use the beta system now as a guest at no charge.

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HOW IT WORKS

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End-to-End Docking Factory – From Protein Prep to Wet List

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1. Protein Preparation

QuantumCURE Pro starts with protein prep at scale:

• Fetches or imports protein structures (e.g., PDB, curated sets)

• Cleans structures: removes crystallographic artifacts, water where appropriate, alternate conformations

• Adds hydrogens, assigns charges, fills missing side chains when needed

• Defines and stores docking grids / binding sites as reusable “prepared proteins”

By preparing proteins once and storing them as JSON + grid bundles, I can decouple the heavy prep step from the ligand runs. This is where a local GPU workstation (e.g., SPARK-class box) shines: you prep everything locally, then upload prepared proteins to the cloud so docking runs at “highway speed.”

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2. Ligand Ingestion & Normalization

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• Take ligands from PubChem, ChEMBL, or custom libraries

• Normalize SMILES, sanitize structures (RDKit-style cleaning)

• Generate 3D conformers

• Apply filters (basic drug-likeness, logP ranges, MW windows, flags, etc.)

The platform keeps track of which ligands were already prepared and docked, so I’m not wasting compute by repeatedly processing the same molecules.

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3. Docking Engine (PRNG & Quantum-Seeded)

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Docking is run in two primary modes:

1. Classical Mode (PRNG)

   • Uses high-quality pseudo-random seeds for reproducibility

   • Typical Vina-style search over poses and conformers

   • Good as a baseline and for cross-checking runs

2. Quantum-Enhanced Mode (QRNG + Annealing Seeds)

   • Seeds the search with true entropy from a USB QRNG (photon-based randomness)

   • Optionally injects D-Wave annealing seeds harvested from QUBO runs

   • Modulates exploration of pose space, torsions, and local minima in a way PRNG simply can’t replicate

 

Every docking job logs:

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• Binding affinity (kcal/mol)

• Pose/clash metrics

• Predicted IC₅₀ (via post-docking models)

• Symbolic tags (Zaban glyphs, “entropy-aware” signals)

• Entropy source: PRNG vs QRNG vs Annealing

 

4. Scoring, IC₅₀ & Structural Forensics

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After docking, QuantumCURE Pro layers additional analysis:

• Binding Score: Energy-based scores (e.g., –5 kcal/mol vs –15 kcal/mol range)

• IC₅₀ Estimation: Predictive models to estimate potency and classify compounds (extremely potent, moderate, weak, etc.)

• Van der Waals Clash Analysis: Detect and score steric clashes, buried unsatisfied polar atoms, etc.

• Bemis–Murcko Scaffolding: Identify core scaffolds, group families, and track structure-activity relationships

• Dose–Response Curves (Prism-lite): Data export and visualization of sigmoidal curves for shortlisted compounds

This isn’t just “dock and forget.” The system is built to explain why a compound looks promising, not just spit out a ranking.

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5. Wet List & Golden List Pipeline

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The ultimate goal is not pretty scores—it’s a defensible wet list:

1. Run millions of dockings across multiple entropy modes

2. Filter via IC₅₀, clash analysis, scaffolds, and symbolic glyph patterns

3. Build a shortlist

4. Pass that shortlist to GANN AI (generative / adversarial models) for pattern discovery

5. Run electron distribution validation (e.g., SKALA-style workflows) on the survivors

6. The compounds that pass all filters become a wet list worthy of lab synthesis

 

Over time, the best of the best converge into what I call a Golden List—compounds that have been through docking, entropy-aware screening, AI pattern checks, and electron distribution validation.

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TECH SPECS

​Under the Hood – Architecture & Capabilities

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• Core Pipeline:

  • Protein prep engine (local GPU / SPARK-ready option comping soon!)

  • Ligand prep and conformer generation

  • Cloud-ready docking workers

  • JSONL-based result storage and replay

• Entropy Sources:

  • PRNG: Classical, reproducible seeds for baselines and debug

  • QRNG: USB quantum random number generator (photon-based) at ~0.5 Mbps, and optional PCIe high-speed entropy harvesting

  • Quantum Annealer Seeds: D-Wave QUBO runs converted into entropy packets

  • Future: Ion trap / other hardware entropy integration (2026)

• QuantumCURE Pro core Features:

  • High-throughput docking (hundreds of thousands to millions of ligands per campaign)

  • Entropy-aware lead discovery (track which compounds emerge under which entropy source)

  • Symbolic glyph tagging of collapse patterns (Zaban-Quantum, Quantum Linguistic Framework integration)

  • IC₅₀ prediction and sigmoidal curve exports

  • Van der Waals clash maps & “forensics cards” for each promising hit

  • Full JSON export for further AI / ML analysis

• Data Outputs:

  • Docking results with pose info, scores, IC₅₀, entropy metadata

  • Ligand → scaffold clusters

  • Wet-list exports for lab partners

  • Optional citizen scientist metadata for distributed runs

ENTROPY ENGINE (QRNG + ANNEALING) & PRNG COMPARISON

Why Entropy Matters

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Typical docking platforms (Schrödinger, etc.) rely on high-quality PRNGs to explore conformational space. That’s fine, but it means:

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• The search is ultimately driven by algorithmic randomness

• Multiple runs often revisit the same basins of attraction

• Redundant poses and similar solutions are common

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uantumCURE Pro adds a second dimension:

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1. QRNG Mode – entropy from real quantum events (photon detections, collapse events). This introduces:

   • Non-algorithmic randomness

   • Different sampling of pose space

   • The ability to tag hits as QRNG-derived vs PRNG-derived

2. Annealing Mode – seeds derived from D-Wave QUBO runs, where the system samples from a physical energy landscape. Those samples are converted into:

   • Entropy packets

   • Seed sequences for docking exploration

   • Symbolic glyphs for pattern tracking

 

Over time, you can ask questions like:

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• “Which scaffolds only appear as hits under QRNG or annealing seeds?”

• “Do certain targets respond better to certain entropy profiles?”

 

This is what I call Entropy-Aware Lead Discovery: not just running more simulations, but understanding which entropy source brought which compound to the surface.

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COMPARISON TO SCHRÖDINGER

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Platforms like Schrödinger offer:

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• Mature, enterprise-grade suites

• Deep integration with pharma workflows

• Classical, PRNG-driven docking and simulation pipelines

• Primarily licensed to big biopharma and large institutions

 

QuantumCURE Pro is different in both philosophy and architecture:

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• Built for small labs, startups, and citizen scientists

• Designed from day one to integrate quantum entropy (QRNG + D-Wave)

• Focuses on building my own wet list and Golden List, not just providing scores to someone else’s pipeline

• Offers a path for individuals to participate in distributed simulations through the Citizen Scientist Portal

 

In short: Schrödinger builds the tools for big pharma; I’m building a factory that anyone can afford and use use to hunt for new cancer-killing compounds.

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