Using machine learning to find new treatments for acetylcholinesterase inhibition
Official title: Machine learning approaches to predict Acetylcholinesterase inhibition and model applications
Funded at Collaborations Pharmaceuticals, INC. · NIH-11004542 · Data updated 2026-11-12
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.
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 | December 10, 2021 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $727,312 |
| NIH project number | 2R44ES033855-02 |
| Project ID | NIH-11004542 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Alzheimer's disease or those at risk of developing it due to genetic or environmental factors.
✕Less likely to help
Patients with conditions unrelated to acetylcholinesterase inhibition or those not affected by Alzheimer's disease may not benefit from this research.
What this research studies
This research focuses on developing machine learning models to predict how certain compounds can inhibit the enzyme acetylcholinesterase (AChE), which is crucial for regulating neurotransmitters in the brain. By analyzing a vast amount of existing literature and data, the researchers aim to identify new potential inhibitors that could help treat conditions like Alzheimer's disease. The approach involves high-throughput screening of various compounds to find those that effectively target AChE, potentially leading to new therapeutic options for patients. This innovative methodology seeks to enhance the efficiency of drug discovery in this area.
Why it matters
Potential benefit: If successful, this research could lead to the development of new treatments for Alzheimer's disease and other conditions related to acetylcholine dysregulation.
How similar studies have gone: Previous research has shown promise in using machine learning for drug discovery, indicating that this approach could yield significant advancements in identifying effective treatments.
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
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.