My Personal Cancer Drug Factory, QuantumCURE Citizen Scientist Portal Just Hit a Major Milestone
- mansour ansari
- Nov 18
- 2 min read
After processing around 500,000 compounds (Prep-Dock-Score) across 389 sessions, the platform I have created independently re-discovered multiple FDA-approved drugs, including Omeprazole, Ibuprofen, Naproxen, Acetaminophen, and Caffeine, as potential EGFR binders. That is a validation that my system is not producing garbage. This confirms that the QRNG-seeded docking engine is producing real biological signals, not random noise. What truly surprised me is that Omeprazole shows up multiple times with the same binding results (–15.00 kcal/mol, IC50 10.1 pM, Score 88)… yet each run generates a different quantum glyph.
These glyphs come from my proprietary quantum-linguistic framework, a system I designed to capture the unique entropy-driven pathway each simulation takes. Even when the chemistry converges to the same score, the glyph records how the system arrived there. It means that these glyphs are the symbolic “fingerprint” of the particular quantum-seeded exploration that is being reflected.
Later, an AI system like GANN will be able to analyze these glyph patterns to detect deeper similarities, hidden structure, drift, or collapse signatures across millions of runs, in turn helping refine my final Golden List of cancer drug candidates even further. Once that layer is complete, the next step is Skala AI, which examines electron-distribution patterns for final validation.
More about Skala soon, but for now, the important point is this:
The glyphs aren’t decoration. They are compressed quantum signatures that make the entire dataset machine-readable for next-generation AI.
This is scientifically correct. You see, each simulation uses a different quantum entropy packet, exploring a unique conformational pathway. The glyph captures that pathway as a symbolic “quantum fingerprint.”. It means, different glyphs, same physics, exactly what a quantum-seeded system should show.
Nothing here is clinical proof. I am not saying Omeprazole, a popular acid reducer, can treat cancer, I am just saying the system flags it, but the fact that the engine surfaced real FDA-approved drugs as high-scoring hits shows the system can reveal unexpected repurposing candidates at scale.
And all of this happened on my own private factory for cancer drug discovery page, not a million-dollar cluster and not a $100k commercial docking license.
This is why I’m excited. This is why I’m building this. There are millions of compounds still ahead of me, but the pace is about to accelerate. Soon I’ll integrate a pair of NVIDIA Spark workstations. The affordable, petaflop-class desktops that NVIDIA offering in early 2026, to chew through massive compound libraries at near-zero latency. By preparing proteins locally and uploading them to the cloud fully optimized, the docking step becomes dramatically faster.
This means faster discovery, lower compute cost, and no need for a $100,000 commercial license from competing platforms.
I’ve built this entire system line-by-line here in Oklahoma City, over the course of a year, and I still have a long road ahead. But everything is moving in the right direction.
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