Designing inhibitors to target the Myc transcription factor in cancer treatment
Myc Transcription Factor Inhibitor Design: Integrating Atomic and Mesoscale with Semi-Supervised Generative Deep Learning Models
This study is working on finding new treatments for cancer by targeting a protein called Myc that helps tumors grow, using advanced computer techniques to discover new compounds that could stop Myc from doing its job, which might lead to better therapies for patients.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Wayne State University NIH-funded |
| Lab location | 1 site (Detroit, United States) |
| Project ID | NIH-10899643 on NIH RePORTER |
What this research studies
This research focuses on developing new inhibitors that can effectively target the Myc transcription factor, a key player in cancer progression. By utilizing advanced machine learning techniques, specifically semi-supervised generative deep learning models, the research aims to expand the library of known inhibitors and identify new compounds that can disrupt Myc's function. The approach combines atomic and mesoscale modeling to better understand the interactions between Myc and potential inhibitors, which could lead to more effective cancer therapies. Patients may benefit from the identification of novel treatments that could reverse oncogenic states associated with Myc.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients with cancers that are driven by the Myc transcription factor.
Not a fit: Patients with cancers not associated with Myc or those with non-cancerous conditions may not receive any benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to new cancer therapies that specifically target and inhibit the Myc transcription factor, potentially improving treatment outcomes for patients.
How similar studies have performed: Other research has shown promise in targeting transcription factors with novel inhibitors, but the specific approach of using generative deep learning for Myc is relatively novel.
Where this research is happening
Detroit, United States
- Wayne State University — Detroit, United States (Active)
Researchers
- Principal investigator: Schwing, Gregory John — Wayne State University
- Study coordinator: Schwing, Gregory John
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 below.