AI-powered platform to identify and discover new psychoactive substances
Official title: A chemical AI and data science platform for the identification and discovery of novel psychoactive substances
Funded at Princeton University · NIH-11384903 · Data updated 2026-11-12
A data-driven AI platform helps labs quickly find known and unknown psychoactive substances to improve toxicology testing and public health responses.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Princeton University NIH-funded |
| Lab location | 1 site (Princeton, UNITED STATES) |
| Project start | August 15, 2025 |
| Project end | July 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $164,000 |
| NIH project number | 5R03DA063752-02 |
| Project ID | NIH-11384903 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable to a specific patient cohort; this grant centers on tool development for toxicology and public health testing.
✕Less likely to help
Patients may not directly benefit during development, but could benefit once the tools are used in labs and clinical settings.
What this research studies
This project aims to build a comprehensive database of known novel psychoactive substances and develop AI tools that predict the structures of new substances likely to appear on the market. By adapting neural network approaches from language models, the platform will enable faster, more accurate identification in clinical and forensic samples. The work is designed to support toxicology labs in diagnosing intoxications, guiding treatment, and informing public health strategies. The focus is on substances like synthetic cannabinoids, cathinones, benzodiazepines, and opioids, with an emphasis on staying ahead of emerging compounds.
Why it matters
Potential benefit: If successful, the platform could speed up detection of dangerous substances, improve patient care during intoxications, and strengthen public health surveillance.
How similar studies have gone: Prior AI and data-science efforts have shown promise in predicting chemical properties and structures, suggesting potential for this approach.
Where this research is happening
-
Princeton University
Princeton, United States Active
Researchers
- Principal investigator: Skinnider, Michael Alexander — Princeton University
- Study coordinator: Skinnider, Michael Alexander
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.