# Selective Transition Metal-Catalyzed Alkene and Alkyne Functionalization Reactions

> **NIH NIH R35** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $367,002

## Abstract

Project Summary
After carbon and hydrogen; carbon, oxygen, nitrogen, and sulfur are the four of the most common elements
found in pharmaceuticals and drug candidates. Lead identification and optimization is typically the longest part
of the drug discovery process, taking about five out of the average fifteen years, to bring a drug to market. This
is partially due to the requirement to synthesize and test of a large number of potential drug candidates through
structure activity relationship (SAR) studies. As such, the development of new organic methodologies for the
rapid synthesis of organic molecules is of utmost importance to synthetic chemists. The research described
herein focuses on the development of novel approaches for the synthesis of C–N, C–S, C–O, and C–C bonds,
in a selective and expedient manner. Moreover, it seeks to allow for divergent synthesis; where from a single
common intermediate libraries of potential pharmaceuticals could be synthesized in a single synthetic
transformation simply by varying the reagents. More specifically, the proposal focuses on the development of
hydro- and oxidative functionalization of carbon-carbon double bonds using transition metal catalysts.

## Key facts

- **NIH application ID:** 10231192
- **Project number:** 5R35GM125029-05
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Kami Lee Hull
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $367,002
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10231192

## Citation

> US National Institutes of Health, RePORTER application 10231192, Selective Transition Metal-Catalyzed Alkene and Alkyne Functionalization Reactions (5R35GM125029-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10231192. Licensed CC0.

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