A hybrid classical/quantum ML database that tells you what natural and pharmaceutical drugs can fight a specific disease.
Built as part of Stanford CS229 (Machine Learning) by Dr. Elden Wayne Whalen III, ShD. https://quantum-ethno-pharma.onrender.com/
- Multi-Source Intelligence: Integrates Dr. Duke’s Phytochemical Database, COCONUT, DrugBank, ChEMBL, PubChem, and other ethnobotanical repositories.
- Hybrid ML Architecture:
- Supervised models for ranking and modality prediction
- Unsupervised embeddings and clustering for novel discovery
- Reinforcement Learning module (in development) for hybrid analog optimization
- Live Quantum Enhancement: Powered by a custom Variational Quantum Algorithm (VQA) oracle via HTTP.
- Rich Recommendations: Natural products + synthetic drugs with predicted modalities, mechanisms, evidence levels, toxicity flags, and quantum-enhanced confidence scores.
- Interactive Demo: Streamlit/Gradio web interface for instant queries.
Natural products continue to inspire a massive portion of modern pharmaceuticals. This project bridges traditional ethnobotanical knowledge with cutting-edge AI and quantum computing to accelerate hypothesis generation in drug discovery, repurposing, and global health research.
Important: This is a research tool only. All outputs are computational predictions and not medical advice.
- Backend: FastAPI + Python
- Classical ML: XGBoost, PyTorch, scikit-learn, RDKit
- Quantum: Custom VQA Oracle (Grok-Wayne Quantum Algorithm)
- Data: PostgreSQL + pgvector, Neo4j (Knowledge Graph), Redis
- Frontend: Streamlit / Gradio
- Deployment: Docker, Render
git clone https://github.com/wayneeffect/quantum-ethnobotany-pharma.git
cd quantum-ethnobotany-pharma
pip install -r requirements.txt
cp .env.example .env
streamlit run app.py