Building a Quantum-AI Pipeline That’s Already Discovering Novel Compounds
I’ve been quietly working on something that feels like the future of early-stage drug discovery.
From my command line, I built a fully autonomous quantum-AI pipeline that integrates quantum simulation, intelligent evolutionary design, and high-precision molecular validation. No fancy graphical interface — just raw, efficient terminal-based workflows that let me iterate fast and explore deep.
It’s still in active testing and optimization phase, but the early results have already surprised me. The system has generated multiple novel molecular scaffolds for challenging targets like HIV-1 integrase. Some of these leads show predicted binding profiles that outperform existing approved drugs in computational assays.
This isn’t just another theoretical model. It’s a working end-to-end pipeline running real discovery cycles and producing concrete candidates with strong docking scores and drug-like properties.
I’m keeping the full technical implementation private for now. The complete production version is still under development, and I want to make sure it’s rock-solid before wider release. But the progress so far has me genuinely excited about where quantum + AI can take compound discovery.
The goal is simple: accelerate the hunt for novel cures by combining the best of quantum computing power with smart autonomous agents — all while staying lean and focused.
Right now the pipeline remains under my direct control. I’m using it to push boundaries on difficult targets and expand into new disease areas.
If you're working in pharma, biotech, or quantum technologies and are serious about next-generation compound discovery, feel free to reach out via DM. I’m open to discussing licensing, collaborations, or targeted pilot runs once the system reaches full production readiness.
This is only the beginning.
Quantum-AI drug discovery is no longer science fiction — it’s running on my machine today.
#QuantumAI #DrugDiscovery #Biotech #ComputationalChemistry #Ai
Built an AI research agent that runs autonomous physics experiments 🔬
AEGIS = Casimir force simulator + fine-tuned Qwen2.5 + Claude agent
Demo → https://t.co/OGDGMiSaL5
#AI#Physics#AMD#Hackathon#OpenSource
https://t.co/LovK1ovgbb
Just submitted AEGIS to the AMD Developer Hackathon on @lablab_ai 🚀
AEGIS is an anti-gravity research platform that combines real Casimir force physics with an autonomous AI research agent powered by Claude + a fine-tuned Qwen2.5 7B model.
#AI#Physics
https://t.co/LovK1ovgbb
🔬 AEGIS Platform — autonomous AI physics research agent
4 tabs, 1 physics engine:
→ Theory Sandbox
→ Anomaly Detector
→ Unified View
→ AI Agent (Qwen2 on AMD)
Dr. AEGIS narrates results with voice 🎙️
Built for @AIatAMD Developer Hackathon on @lablabai#AMDDeveloperHackathon
Nobody has ever measured Casimir forces inside a negative-index metamaterial. I built the computational framework that shows exactly how to do it — and what to look for. Validated physics. Novel experiment. Browser-based. Open for collaboration. 🧪
#CasimirEffect#QuantumVacuum
🌱 FloraBot — My Autonomous AI Greenhouse Digital Twin
Built with:
4-stage multi-agent AI pipeline (Sensor → Diagnostic → Strategy → Coordinator)
• Real-time 3D simulation with Three.js + React Three Fiber
#FloraBot#AIRobotics#DigitalTwin#Hackathon#GreenhouseAutomation
🌱 FloraBot — Autonomous AI Greenhouse Digital Twin
4 AI Agents • Real-time 3D Simulation • Gantry Arm + Tall Rover
Closed loop: Sensors → AI → Robots → Plants
Built with React Three Fiber + Multi-model AI (Gemini included)
#FloraBot#AIRobotics#DigitalTwin#Hackathon
Training an AI to design drugs that don't exist yet 🧬
Fine-tuned on my EGFR & HIV-1 Protease docking campaigns using @AIatAMD MI300X + ROCm. Model reasons about binding affinity, ADMET, fragment growing.
#AMDHackathon@lablab#DrugDiscovery
Training an AI to design drugs that don't exist yet 🧬
Fine-tuned on my EGFR & HIV-1 Protease docking campaigns using @AIatAMD MI300X + ROCm. Model reasons about binding affinity, ADMET, fragment growing.
#AMDHackathon@lablab#DrugDiscovery
GET READY FAM $PHNIX IS ABOUT TO BREAK OUT 🔥🐦🔥
Phoenix rising on the XRPL.
XRPL meme season is just waking up and $PHNIX is the official phoenix mascot leading the charge.
If ripple:native keeps running this one flies.
Send $PHNIX higherrrr! 🐦🔥
"AI just found a molecule that computationally outperforms an approved HIV drug.
Our autonomous platform designed it from scratch — no dataset, no known drugs as templates, just physics-based docking and particle physics statistics.
-9.708 kcal/mol vs Dolutegravir's -9.43. 5.87σ confidence.
Next step: lab validation. Open source. 🔬
https://t.co/RYBrS5y1ty
#AIDrugDiscovery #ComputationalChemistry #DrugDiscovery #OpenSourceScience #AIPowered #PhysicsBased #HIVResearch #Cheminformatics #ArtificialIntelligence #ScientificBreakthrough"
"AI just found a molecule that computationally outperforms an approved HIV drug.
Our autonomous platform designed it from scratch — no dataset, no known drugs as templates, just physics-based docking and particle physics statistics.
-9.708 kcal/mol vs Dolutegravir's -9.43. 5.87σ confidence.
Next step: lab validation. Open source. 🔬
https://t.co/RYBrS5y1ty
#AIDrugDiscovery #ComputationalChemistry #DrugDiscovery #OpenSourceScience #AIPowered #PhysicsBased #HIVResearch #Cheminformatics #ArtificialIntelligence #ScientificBreakthrough"
"AI just found a molecule that computationally outperforms an approved HIV drug.
Our autonomous platform designed it from scratch — no dataset, no known drugs as templates, just physics-based docking and particle physics statistics.
-9.708 kcal/mol vs Dolutegravir's -9.43. 5.87σ confidence.
Next step: lab validation. Open source. 🔬
https://t.co/RYBrS5y1ty
#AIDrugDiscovery #ComputationalChemistry #DrugDiscovery #OpenSourceScience #AIPowered #PhysicsBased #HIVResearch #Cheminformatics #ArtificialIntelligence #ScientificBreakthrough"