QuantumCURE V5 with Bemis-Murcko Scaffold Analysis
- mansour ansari
- Sep 11
- 2 min read
And yes — I am actively searching for a viable cancer drug lead, with a better chance than most traditional approaches, because my system explores chemical spaces that classical methods often overlook.
It’s been almost a year in the making. So far, oh, about 3,340 hours since November 2024. My journey started before that, learning and researching, and that goes back to the COVID-19 era.
Built not by a pharma giant, but by one man in Oklahoma City's closet/room, fueled by relentless passion, strong desire to make a difference in my retirement time, a stack of second-hand physics and chemistry books, twelve old PCs from my past software business, a few modern AI-powered machines, and plenty of coffee.
What emerged is a fully functioning quantum-enhanced drug discovery system — something most would expect only in multi-million-dollar pharmaceutical labs.
From My Desk to Quantum-Enhanced Drug Discovery
What you’re seeing in these screenshots is my molecular docking system — a platform I’ve built that integrates quantum entropy, AI-driven scaffolding (Bemis–Murcko analysis), and symbolic metrics (Betti numbers, Collapse Scores, Quantum Glyphs) into a live pipeline.
Here’s what happens under the hood:
·Molecules flow in: Each compound is processed through protein preparation (PDBFixer, PDB2PQR, grid generation).
·Docking engine fires: AutoDock Vina simulates binding to targets like EGFR (lung cancer).
·Quantum entropy injected: Instead of relying only on pseudo-random numbers, my system pulls entropy from real quantum sources (USB QRNG or GCS keys). This changes the search space — uncovering poses classical PRNG systems often miss.
·Symbolic layer: Results aren’t just numbers. Every compound is tagged with a Quantum Glyph, Betti numbers for topology, VAD scores for volume/area/dynamics, and scaffold novelty analysis.
·Throughput: 50 compounds per session run in minutes, with binding affinities as strong as –24 kcal/mol and quantum confidence metrics to rank plausibility.
💡 Comparison to Pharma’s Million-Dollar Platforms
Big pharma systems (like Schrödinger Glide, MOE, OpenEye) run on expensive clusters and license costs that easily reach seven figures per year. They offer robustness and integration into full R&D pipelines — but they’re locked behind corporate budgets.
My system, on the other hand, demonstrates that with ingenuity and quantum entropy integration:
·You can achieve real-time docking throughput (hundreds of compounds per hour).
·You can generate scaffold novelty insights that highlight unexplored chemical space.
·You can build a symbolic dictionary of compounds that adds an interpretive layer — something missing from conventional pharma stacks.
In short: pharma spends millions to rent speed and compliance. I’m showing how an independent lab can explore new frontiers in drug discovery using quantum randomness, symbolic analytics, and lean engineering.
🚀 This is not a toy. It’s a working QuantumCURE V5 Lab — and it’s proof that the future of drug discovery isn’t only locked in corporate towers. It can be built, tested, and scaled by citizen scientists and small labs who dare to rethink the foundations.
See my work here:
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