PharmaGuard is an end-to-end pharmacogenomics decision-support platform built during a high-pressure hackathon to transform raw genomic data into fast, explainable, clinician-ready insights.
While many teams focused only on AI generation or UI dashboards, we engineered a clinically grounded system — combining genomics parsing, CPIC-aligned risk modeling, and safety-focused AI explanations into a single real-time workflow.
Upload a patient VCF file + select a drug → PharmaGuard instantly delivers:
- 🧬 Gene + Diplotype interpretation
⚠️ CPIC-aligned risk classification- 📊 Pharmacogenomic profile with detected variants
- 🤖 Grounded AI clinical explanation (mechanism-aware)
- 🔥 Risk Heatmap visualization
- 💬 “Ask PharmaGuard” clinician assistant
All powered by a privacy-first local LLM pipeline.
Many hackathon solutions stopped at “AI generates medical text.” PharmaGuard goes further — focusing on clinical reliability, explainability, and real workflow design.
- AI responses are constrained by structured pharmacogenomic data
- Variant citations and biological mechanisms included
- CPIC-aligned reasoning enforced
- Hallucination-resistant prompt architecture
- Risk engine responds instantly
- LLM explanation loads asynchronously
- Ultra-Turbo async pipeline reduces perceived latency
- Model warm-keep and token-limited inference for speed
Dynamic UI highlighting toxicity intensity using genomic risk signals — translating complex PGx data into an immediately interpretable clinical visual.
Unlike basic chatbots:
- Grounded in current patient genotype + drug
- Mechanism-focused answers
- Clinician-tone generation
- Safety disclaimers enforced
- Structured schema validation
- Fallback explanation logic
- No hallucinated genes or variants allowed
- Built for decision support, not autonomous prescribing
Backend
- FastAPI
- Pydantic
- Ollama (Llama3 local model)
- Async pipeline architecture
Frontend
- React + Vite
- Real-time state updates
- Clinical heatmap UI system
AI Layer
- Local LLM inference
- Structured prompt grounding
- Non-blocking async explanation pipeline
VCF Upload → Variant Parser → Risk Engine → Clinical Recommendation
↓
Async LLM Explanation
↓
Heatmap + Clinician UI
- Built full pharmacogenomic pipeline from scratch
- Integrated local LLM safely into clinical workflow
- Designed hallucination-resistant explanation system
- Implemented real-time UX despite heavy AI inference
- Delivered complete frontend + backend integration under time constraints
👉 Full walkthrough video included with this submission.
Massive thanks to RIFT by PWIOI for hosting an incredible hackathon environment that encouraged deep technical execution, clinical responsibility, and bold experimentation.
PharmaGuard provides AI-assisted pharmacogenomic insights for educational and clinical support purposes only. Final prescribing decisions must be made by licensed healthcare professionals.
PharmaGuard is not just a hackathon project — it’s a step toward making pharmacogenomics interpretable, actionable, and safe in real clinical workflows.
Because precision medicine shouldn’t feel like decoding a genome alone.