Finding new drugs to combat antibiotic resistance using machine learning
Official title: Machine Learning Approach for finding novel metallo-b-lactamase inhibitors
Funded at Miami University Oxford · NIH-10514544 · Data updated 2026-11-12
This study is looking for new ways to stop certain bacteria from resisting antibiotics, which can make infections harder to treat, especially in COVID-19 patients, by testing over a million different compounds to find the best ones that can help.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Miami University Oxford NIH-funded |
| Lab location | 1 site (Oxford, United States) |
| Project start | August 1, 2019 |
| Project end | August 31, 2025 |
| Funding year | 2022 |
| Award (this year) | $433,500 |
| NIH project number | 2R15GM134454-02A1 |
| Project ID | NIH-10514544 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals suffering from infections caused by antibiotic-resistant bacteria, particularly those with secondary infections related to COVID-19.
✕Less likely to help
Patients with infections that are not caused by antibiotic-resistant bacteria may not benefit from this research.
What this research studies
This research focuses on developing a machine learning model to identify potential inhibitors of metallo-β-lactamases (MBLs), which are enzymes that contribute to antibiotic resistance in bacteria. By analyzing large chemical libraries, the researchers aim to discover new compounds that can effectively inhibit these enzymes, which are increasingly responsible for difficult-to-treat infections, including those seen in COVID-19 patients. The project involves screening over 1.3 million compounds and validating the most promising candidates through microbiological and biochemical studies. The ultimate goal is to provide a validated model and data to the public to aid in the fight against antibiotic resistance.
Why it matters
Potential benefit: If successful, this research could lead to the development of new treatments for infections caused by antibiotic-resistant bacteria.
How similar studies have gone: Other research has shown promise in using machine learning to identify drug candidates, suggesting that this approach could yield significant results.
Where this research is happening
-
Miami University Oxford
Oxford, United States Active
Researchers
- Principal investigator: Crowder, Michael W — Miami University Oxford
- Study coordinator: Crowder, Michael W
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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.