QuantumCURE: Quantum-Enhanced Molecular Docking at Scale
- mansour ansari
- Aug 18
- 3 min read
Updated: Aug 19
QuantumCURE: Quantum-Enhanced Molecular Docking at Scale
By Mansour Ansari, Founder of QuantumLaso, LLC
After months of intense solo development, I now have a working system I call QuantumCURE — a real-time molecular docking platform enhanced by quantum entropy and distributed computing.
What is it? At its core, QuantumCURE enables large-scale docking of compounds from PubChem’s library against validated cancer protein targets, seeded with quantum entropy for deeper exploration of molecular space.
🎯 Goal
We live in the age of AI and Quantum Computing. Each discipline is transformative by itself — combined, they multiply possibilities exponentially.
My goal was simple but ambitious: learn quantum computing deeply, then apply it to real-world problems in drug discovery, meteorology, oil & gas, and beyond.
I began learning during COVID-19 isolation, building hybrid classical/quantum simulations seeded with entropy from quantum hardware. One result was my recent project, a tornado forecasting system that could detect collapse boundaries and vorticity earlier than radar — a project still under development.
But soon I pivoted to something even more impactful: drug discovery. After many trial runs and simulations of simulations, I built a production-level molecular docking system, designed as a scalable factory for quantum-enhanced docking.
🧪 Technical Implementation (V4.0.1)
Molecular Docking Engine
Backend: AutoDock Vina containerized on Google Cloud Platform. Vina is validated for drug discovery; swapping to a licensed, faster version is trivial when scaling up.
Preparation: RDKit for SMILES parsing and 3D conformer generation.
RDKit is a world-class open-source cheminformatics toolkit, used in pharma and academia for drug design, combinatorial chemistry, and structure-based prediction.
Targets: 6+ validated cancer proteins:
EGFR: Epidermal Growth Factor Receptor, critical in lung/breast cancers.
BCR-ABL: Fusion protein driving chronic myeloid leukemia.
HER2: Overexpressed in aggressive breast cancers.
BRAF: Mutated in melanoma, driving uncontrolled growth.
VEGFR2: Regulates angiogenesis, vital for tumor blood supply.
TP53: The “guardian of the genome,” mutated in >50% of cancers.
Throughput: 100–1000 compounds per session across distributed nodes. With GPU scaling (e.g., NVIDIA A100 clusters), this expands to tens of thousands per session, enabling industrial-scale screening.
⚛️ Quantum Entropy Sources — Unique to QuantumLaso
QuantumLaso Hardware: USB/PCIe QRNG units feed entropy into Google Cloud Storage buckets, powering the pipeline. Each run is seeded with live, irreducible randomness from quantum processes.
ANU QRNG: Australian National University’s public quantum RNG adds diversity of entropy streams.
D-Wave Annealer Integration: Entropy harvested from annealing pathways enriches conformer sampling, improving docking exploration.
Future Direction: Live integration with IonQ’s trapped-ion systems, streaming entangled entropy directly into simulations.
This pipeline is unique worldwide: not pseudo-random, but real quantum randomness powering molecular discovery.
🔒 Enterprise Security
Drug discovery at scale demands security:
Row-Level Security (RLS): User-specific table protection.
Data Isolation: V4 sessions fully separated from legacy data.
Location Privacy: GPS coordinates encrypted, owner-only access.
Rate Limiting: Built-in protection against endpoint abuse.
Security is not optional — it’s a requirement for pharma-grade adoption.
🛠 Technology Stack
Frontend:
React 18 + TypeScript + Vite → high-speed UI.
Shadcn/UI + Tailwind CSS → modern styling.
3Dmol.js → protein visualization in real time.
Supabase subscriptions → real-time interaction across devices.
Backend:
Supabase Edge Functions → scalable serverless logic.
Google Cloud Run → auto-scaling containers.
PostgreSQL with RLS → enterprise-grade database.
Real-time sync → cross-device collaboration.
🧬 Integrated AI Toxicity Prediction
Every docking run is followed by AI-powered toxicity screening:
Hepatotoxicity, cardiotoxicity, neurotoxicity detection.
Heavy metal screening, mutagenicity, and carcinogenicity tests.
Organ-specific predictions with molecular explanation layers.
This provides an immediate “red flag” system before advancing candidates.
🚀 Roadmap After Database Completion
Lead Optimization: Structure-based refinement.
ADMET Prediction: Pharmacokinetics, absorption, metabolism.
Synthetic Accessibility: Retrosynthetic analysis and cost modeling.
Experimental Validation: Biochemical assays via partnerships.
Clinical Pipeline: Progression toward therapeutic candidates.
🤝 Collaboration Opportunities
Academic:
Example: Collaboration with computational chemistry labs (e.g., MIT’s Chemical Engineering groups).
Structural biology labs for crystallography data.
Quantum computing centers for annealing access.
Industry:
Partnerships with pharmaceutical companies for lead validation.
CROs with screening capabilities.
Cloud & quantum hardware providers (Google, AWS, IonQ, D-Wave).
🌍 Closing
QuantumCURE is not just a docking engine — it’s a new paradigm. By injecting authentic quantum entropy into the molecular discovery process, we democratize access to discovery power once reserved for billion-dollar labs.
Join me in building the next frontier of quantum-enhanced medicine. email me at videomover@gmail.com
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