Using AI to identify toxic effects of chemicals on health
Official title: Discovering clinical endpoints of toxicity via graph machine learning and semantic data analysis
Funded at University of Pennsylvania · NIH-11000821 · Data updated 2026-11-12
This study is exploring how artificial intelligence can help us understand which chemicals might be harmful to our health, so we can better protect patients from toxic effects.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | January 1, 2023 |
| Project end | December 31, 2025 |
| Funding year | 2025 |
| Award (this year) | $212,568 |
| NIH project number | 5R00LM013646-05 |
| Project ID | NIH-11000821 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals who have been exposed to various chemicals and are experiencing unexplained health issues.
✕Less likely to help
Patients who have no history of chemical exposure or related health concerns may not benefit from this research.
What this research studies
This research aims to develop advanced methods that utilize artificial intelligence to predict how certain chemicals can cause harmful health effects. By analyzing vast amounts of publicly available chemical and biomedical data, the project seeks to uncover connections between toxic substances and specific clinical outcomes. The approach involves innovative techniques like graph machine learning and semantic data analysis, which can integrate diverse data sources to improve the accuracy of predictions. This could lead to better understanding and prevention of toxic effects in patients.
Why it matters
Potential benefit: If successful, this research could lead to improved safety assessments of chemicals, ultimately protecting patients from harmful exposures.
How similar studies have gone: Previous research has shown promise in using AI and machine learning for predictive toxicology, indicating that this approach could yield valuable insights.
Where this research is happening
-
University of Pennsylvania
Philadelphia, United States Active
Researchers
- Principal investigator: Romano, Joseph Daniel — University of Pennsylvania
- Study coordinator: Romano, Joseph Daniel
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.