NutriTargetAi is revolutionizing drug discovery—powered by data, inspired by nature.
We harness AI and Transcriptomics integration to unlock the hidden potential of medicinal plants.
Bridging tradition and innovation, we turn ancient remedies into tomorrow’s breakthroughs.
The Problem
Only 5% of the world’s known plants have been pharmacologically analyzed.
AI tools today struggle to decode the complexity of natural compounds.
Wasted Potential
Limited Insight
Tool Limitations
Data Gaps
The Solution
NutriTargetAi analyzes omics data, chemical structures, and literature to generate ranked therapeutic hypotheses.
This enables faster, more accurate discovery of plant-based drug candidates.
Key Features:
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🔬 Predictive Modeling – Forecasts therapeutic potential of plant compounds
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🔗 Smart Compound Matching – Matches natural molecules to disease targets
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💡 Hypothesis Generation – Suggests testable drug leads efficiently
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🧬 Multi-Omics Integration – Combines genomics, proteomics, and metabolomics
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📚 AI Literature Mining – Extracts insights from vast biomedical research
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🧠 Deep Structural Analysis – Understands complex natural compound profiles
Impact
- Accelerates plant-based drug discovery, reducing time and cost.
- Empowers researchers to develop nature-inspired treatments for urgent global health needs.
Technologies Used
- Combines Deep Learning, NLP, Multi-Omics, and Graph AI to analyze and predict therapeutic effects.
- Integrates structured and unstructured data to generate clinically relevant insights.
Team
Master’s researcher specializing in artificial intelligence applications in healthcare
Nejood Bin Eshaq
PharmD Intern with a strong interest in bioinformatics and data-driven drug discovery.
Lena Alghamdi
Data scientist interested in AI applications and big data
Sara Al Amer
AI Engineer specialized in building intelligent nutrition solutions using machine learning and data analytics. Experienced in creating personalized health applications that optimize dietary recommendations.