The University of California, Los Angeles (UCLA) received a $122,904 Project Grant from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training program (CFDA 93.859) to integrate machine learning (ML) with synthetic chemistry methods. The project aims to expedite the development and optimization of new synthetic methodologies by using ML workflows to reduce the number of experiments needed to obtain desired reaction outcomes. Key activities include:
A mentored phase (K99) to learn ML and data science tools, and develop ML workflows for reaction optimization across three case studies.
An independent phase (R00) to facilitate the design and discovery of new asymmetric synthetic methods through three additional case studies focused on improving stereoselectivity.
The award period is from July 1, 2024 to June 30, 2026. This project will enable the principal investigator to create a niche in catalytic research by integrating their expertise in asymmetric synthesis and computational chemistry with the host lab's capabilities in ML, data science, and photoredox catalysis. The grant will also facilitate close collaboration with industry partners Merck and Genentech to leverage their high-throughput experimentation technologies.