A research acceleration platform for identifying overlooked drug repurposing opportunities
Ouki is a research acceleration platform designed to help medical researchers identify potential drug repurposing opportunities that may have been overlooked in existing literature.
The challenge: Over 4,000 new medical research papers are published daily. Researchers working on specific conditions often cannot review findings from other disease areas that might hold relevant insights. Valuable connections between existing drugs and new treatment applications can remain hidden in siloed research.
Our approach: Ouki uses AI to analyze recent medical literature across multiple disease conditions, identifying novel connections between FDA-approved drugs and treatment opportunities. By examining research at scale and across disciplinary boundaries, Ouki flags patterns that warrant further investigation by qualified researchers.
AI regularly analyzes recent medical research across 10 major disease conditions, focusing on peer-reviewed publications and clinical findings.
Advanced algorithms identify connections between FDA-approved drugs and disease mechanisms that may not have been explicitly explored in existing literature.
Potential findings are scored across five dimensions:
High-scoring hypotheses (typically 35+/50) are published for review by the research community. All findings require external validation through proper scientific investigation.
Ouki currently monitors 20 major disease conditions:
Coverage expansion is planned based on researcher demand and community feedback.
Drug repurposing—finding new uses for existing FDA-approved medications—offers several advantages over developing new drugs from scratch:
By focusing exclusively on FDA-approved drugs, Ouki ensures all findings are immediately actionable through clinical investigation.
Ouki is a computational hypothesis generation tool with important limitations:
Ouki is designed to accelerate research by flagging potentially interesting connections for human experts to investigate—not to replace the rigorous scientific process.
All breakthrough findings are cryptographically timestamped using distributed version control before publication, providing immutable proof of discovery date.
This timestamping system ensures:
This approach ensures intellectual honesty and protects both the platform and the research community from potential IP conflicts.
Ouki generates research hypotheses, not medical advice.
All findings published on this platform are computational predictions that require validation by qualified medical researchers through proper clinical studies. Nothing on this site should be interpreted as medical advice, treatment recommendations, or established scientific fact.
This platform is designed as a research acceleration tool for the scientific community. If you are a patient or caregiver, please consult qualified healthcare professionals for medical guidance.
Ouki was developed by a cancer survivor and mechanical & electrical engineer who transitioned into software engineering with expertise in AI systems and automation. Having navigated the healthcare system firsthand and witnessed the challenges of cross-disciplinary medical research, the developer combined engineering problem-solving with AI capabilities to create a tool that identifies overlooked connections across disease boundaries. The name "Ouki" (大喜) means "great joy" in Japanese, reflecting the hope that this tool might help accelerate discoveries that bring relief to those suffering from disease.
The platform represents a fusion of personal experience with technical expertise—applying systematic engineering thinking and modern AI to address a gap in medical research: valuable connections between existing drugs and new treatment applications often remain hidden because researchers working on specific conditions cannot review findings from other disease areas at the scale required.
For questions, collaboration inquiries, or to report issues, please contact:
Email: ouki.research@proton.me
Development is ongoing and community feedback is welcomed.