Using machine learning to improve synthetic chemistry methods
Official title: Data to Design: An Integrated Approach to Developing New Synthetic Methods
Funded at University of California Los Angeles · NIH-10887206 · Data updated 2026-11-12
This study is exploring how to use smart computer programs to make it easier and faster to create new medicines and chemicals, which could lead to better treatments for patients in the future.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | July 1, 2024 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $122,904 |
| NIH project number | 1K99GM151453-01A1 |
| Project ID | NIH-10887206 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for benefiting from this research include individuals seeking innovative therapies in areas such as oncology, neurology, and other fields reliant on new drug development.
✕Less likely to help
Patients with conditions that do not require new synthetic drugs or those not involved in the therapeutic areas targeted by this research may not receive direct benefits.
What this research studies
This research aims to integrate machine learning (ML) with synthetic chemistry to enhance the development of new medicines and chemicals. By utilizing ML algorithms, the project seeks to streamline the process of creating high-value chemical building blocks, which are essential for drug development and other applications. The approach focuses on reducing the number of experiments needed through predictive modeling, thereby saving time and resources while increasing the potential for innovative discoveries. Patients may benefit indirectly through the accelerated development of new therapies and medications resulting from improved synthetic methods.
Why it matters
Potential benefit: If successful, this research could lead to faster and more efficient development of new medications and treatments for various health conditions.
How similar studies have gone: Other research has shown success in applying machine learning to various fields, including drug discovery, indicating a promising potential for this integrated approach in synthetic chemistry.
Where this research is happening
-
University of California Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Maji, Rajat — University of California Los Angeles
- Study coordinator: Maji, Rajat
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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.