Analyzing and visualizing data for predicting toxic effects of new molecules
Official title: MegaTox for analyzing and visualizing data across different screening systems
Funded at Collaborations Pharmaceuticals, INC. · NIH-10674729 · Data updated 2026-11-12
This study is looking at how computer models can help predict if new chemicals or drugs might be harmful, making it easier and safer to test them before they reach patients like you.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Collaborations Pharmaceuticals, INC. NIH-funded |
| Lab location | 1 site (Fuquay Varina, United States) |
| Project start | September 1, 2019 |
| Project end | July 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $855,003 |
| NIH project number | 5R44ES031038-03 |
| Project ID | NIH-10674729 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals involved in drug development or those affected by conditions related to toxic exposures.
✕Less likely to help
Patients who are not involved in drug development or who do not have conditions related to toxic exposures may not benefit from this research.
What this research studies
This research focuses on computational toxicology, which uses algorithms and models to predict whether new chemical compounds may have harmful effects based on existing data. By integrating diverse datasets from various sources, the project aims to develop machine learning models that can accurately forecast toxicity outcomes. This approach could streamline the testing process for new drugs and chemicals, making it more efficient and potentially safer for patients. The research involves both in vitro and in vivo testing to verify predictions made by the models.
Why it matters
Potential benefit: If successful, this research could lead to safer drugs and chemicals by predicting toxic effects before they reach clinical trials.
How similar studies have gone: Other research in computational toxicology has shown promise in predicting drug safety, indicating that this approach has potential for success.
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
Related research
Other NIH-funded projects on the same topics.
-
Long-term formulation for a combination HIV treatment
A new antiviral drug combination for people living with HIV is being developed to stay effective against resistant virus strains.
-
Using machine learning to find new treatments for poisoning and Alzheimer's disease
This research uses computer intelligence to discover new medicines that can help people affected by certain types of poisoning and conditions like Alzheimer's disease.
-
Developing a treatment for CLN1 Batten Disease using enzyme replacement therapy
This study is testing a new treatment for young children with CLN1 Batten Disease by delivering a special enzyme directly into their brains to help improve their brain function and overall quality of life.
-
Using machine learning to find new treatments for acetylcholinesterase inhibition
This study is looking for new ways to find compounds that can help block a brain enzyme linked to Alzheimer's disease, with the hope of discovering better treatments for patients.
-
Developing a treatment for Nipah Virus infection
This study is looking at a potential new treatment for Nipah Virus using a medicine called pyronaridine, which has shown promise against other viruses, to see if it can help keep people safe and healthy if they get infected.
-
Using machine learning to predict how drugs interact with human transporters
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.
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.