Using machine learning to predict how drugs interact with human transporters
Official title: MegaTrans – human transporter machine learning models
Funded at Collaborations Pharmaceuticals, INC. · NIH-10696051 · Data updated 2026-11-12
This study is looking at how different medications work with proteins in our bodies that help move substances in and out of cells, with the goal of creating safer drugs that have fewer side effects and work better for specific organs.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Collaborations Pharmaceuticals, INC. NIH-funded |
| Lab location | 1 site (Fuquay Varina, United States) |
| Project start | April 1, 2019 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $864,767 |
| NIH project number | 5R42GM131433-03 |
| Project ID | NIH-10696051 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who are prescribed medications that may interact with human transporters, such as those taking ACE inhibitors or statins.
✕Less likely to help
Patients who are not on medications that interact with human transporters or those who do not require drug therapy may not benefit from this research.
What this research studies
This research focuses on developing machine learning models to predict how various drugs interact with human transporters, which are proteins that help move substances in and out of cells. By understanding these interactions, researchers aim to design safer drugs that minimize side effects and target specific organs effectively. The project utilizes computational methods and in vitro data to create reliable predictions, which can significantly aid in drug discovery and regulatory assessments. The initial phase has already explored the interactions of numerous drugs with a key transporter involved in processing important medications.
Why it matters
Potential benefit: If successful, this research could lead to the development of safer and more effective medications with fewer side effects for patients.
How similar studies have gone: Previous research has shown promise in using machine learning approaches to model drug interactions, indicating that this method could be effective in advancing drug discovery.
Where this research is happening
-
Collaborations Pharmaceuticals, INC.
Fuquay Varina, United States Active
Researchers
- Principal investigator: Ekins, Sean — Collaborations Pharmaceuticals, INC.
- Study coordinator: Ekins, Sean
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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.