AI-designed dual-target cancer theranostics to overcome therapy resistance
Official title: SCH: Graph-String Transformer and Reinforcement Learning for Design of Cancer Theranostics
Funded at Baylor University · NIH-11415123 · Data updated 2026-11-12
A Baylor University team is using smart AI to create and test dual-target cancer drugs that can both treat tumors and help image them, with a focus on overcoming drug resistance.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Baylor University NIH-funded |
| Lab location | 1 site (Waco, United States) |
| Project start | September 4, 2025 |
| Project end | August 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $899,999 |
| NIH project number | 4R01CA309499-02 |
| Project ID | NIH-11415123 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with cancers where SCD-1 and FADS2 pathways are active and relevant to therapy resistance may be eligible for future translational or clinical testing.
✕Less likely to help
This project is primarily preclinical; directly eligible patients may be limited until later-stage trials.
What this research studies
This project combines advanced AI with lab experiments to design small molecules that inhibit two key enzymes in cancer cell metabolism. The AI, which uses graph-strings and reinforcement learning, will generate synthesizable compounds and BsSMICs that simultaneously target SCD-1 and FADS2. Researchers will validate these candidates through computer modeling, laboratory synthesis, radiolabeling, cell-based tests, animal studies, and ex vivo analyses to assess their effectiveness and safety. The work aims to produce molecules with strong target inhibition and favorable tumor-to-muscle imaging ratios, offering a path toward improved cancer theranostics.
Why it matters
Potential benefit: If successful, patients could benefit from more precise, dual-target therapies that both treat tumors and aid in imaging, potentially improving outcomes and monitoring.
How similar studies have gone: Similar AI-guided drug design approaches have shown promise in identifying novel inhibitors, with early evidence of meaningful in silico and in vitro success.
Where this research is happening
-
Baylor University
Waco, United States Active
Researchers
- Principal investigator: Dong, Liang — Baylor University
- Study coordinator: Dong, Liang
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.