# Computational Models for Reactivity and Selectivity in Catalytic Olefin Functionalization > **NIH NIH R35** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $362,690 ## Abstract Abstract Transition metal- and enzyme-catalyzed reactions of alkenes are among the most powerful approaches to synthesize functionalized organic compounds for biomedical research. Recent experimental advancements have enabled promising catalytic methods for various alkene functionalization reactions with different mechanistic features and distinct types of bond formations. However, it remains a significant challenge to effectively control regio- and stereoselectivity in reactions with readily available, unactivated alkenes. Due to the lack of theoretical understanding of reaction mechanisms and origins of catalyst effects on reactivity and selectivity, experimental developments of new catalytic reactions often rely on trial-and-error screening. A general strategy to investigate catalytic reaction mechanisms and to utilize mechanistic information to guide catalyst discovery is warranted. The overall goal of this proposal is to develop and apply computational tools to obtain mechanistic insights and predictive models to guide experimental development of catalytic functionalizations of alkenes. We will use a series of computational tools, including density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations, to model reaction pathways and identify rate- and selectivity-determining transition states. We will utilize energy decomposition analysis (EDA) calculations to analyze covalent and non-covalent interactions between catalyst and substrate and provide quantitative and straightforward prediction of dominant catalyst– substrate interactions that control reactivity and selectivity. We will apply these computational tools to investigate mechanistically complex catalytic systems, including reactions involving conformationally flexible catalysts, solvent cage effects in radical-mediated reactions, and cooperative effects in multicomponent reactions. We will use computational tools to perform detailed studies of new-to-nature reactions catalyzed by engineered enzymes. These studies will reveal the mechanisms of enantioinduction and the roles of key active site residues on reactivity and selectivity. The proposed research program is significant and innovative because it aims to address general challenges in computational studies of a broad range of catalytic reactions, rather than to simply explain existing results for specific experimental systems. Our studies will establish computational approaches to investigate complex, yet important phenomena in catalysis, including the roles of non-covalent interactions, solvent, and catalyst flexibility. These mechanistic insights can provide new principles and strategies in rational catalyst design to improve reactivity, selectivity, and substrate scope. Our research is highly unique in collaborating with many prominent experimental groups. These fruitful collaborations allowed us to progress in not only the understanding of many specific examples of alkene functionalization reactions, ... ## Key facts - **NIH application ID:** 10765055 - **Project number:** 2R35GM128779-06 - **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH - **Principal Investigator:** Peng Liu - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $362,690 - **Award type:** 2 - **Project period:** 2018-09-01 → 2028-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10765055 ## Citation > US National Institutes of Health, RePORTER application 10765055, Computational Models for Reactivity and Selectivity in Catalytic Olefin Functionalization (2R35GM128779-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10765055. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*