Improving drug discovery using advanced computational methods
Official title: Teaching free energy calculations to learn
Funded at Sloan-Kettering Inst Can Research · NIH-11039919 · Data updated 2026-11-12
This study is working on new ways to find better medicines faster by using smart computer techniques to predict how well potential drugs will work, which could help patients get effective treatments sooner.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Sloan-Kettering Inst Can Research NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | March 1, 2024 |
| Project end | February 28, 2029 |
| Funding year | 2025 |
| Award (this year) | $484,704 |
| NIH project number | 5R35GM152017-02 |
| Project ID | NIH-11039919 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for benefiting from this research include individuals with conditions that require new drug therapies, particularly those related to small molecule treatments.
✕Less likely to help
Patients with conditions that are not addressed by small molecule drugs may not receive direct benefits from this research.
What this research studies
This research focuses on enhancing the process of small molecule drug discovery by utilizing advanced computational techniques, specifically alchemical free energy calculations and machine learning models. By leveraging structural biology data, the project aims to predict how well potential drug compounds will bind to their targets, which can significantly streamline the drug development process. The methodology combines rigorous statistical mechanics with innovative machine learning approaches to improve the accuracy of predictions and reduce the time and cost associated with drug discovery. Patients may benefit from faster access to effective new medications as a result of these advancements.
Why it matters
Potential benefit: If successful, this research could lead to quicker and more efficient development of new drugs, ultimately improving treatment options for patients.
How similar studies have gone: Previous research has shown success in using computational methods for drug discovery, indicating that this approach has the potential for significant advancements.
Where this research is happening
-
Sloan-Kettering Inst Can Research
New York, United States Active
Researchers
- Principal investigator: Chodera, John Damon — Sloan-Kettering Inst Can Research
- Study coordinator: Chodera, John Damon
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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.