Peng Liu
University of Pittsburgh, PA·Chemistry
Chemists often build drug-like molecules by adding new groups to alkenes, which are simple molecules with carbon–carbon double bonds. Catalysts based on transition metals, and enzymes, can do this efficiently.
- Fields
Chemistry
Biochemistry
- Topics
computational chemistry
density functional theory
ab initio molecular dynamics
- Grants
- 1 known grant, through Nov 2028
What they work on
- Identify the rate- and selectivity-determining transition states of metal-catalyzed alkene functionalization reactions, using density functional theory and ab initio molecular dynamics simulations.
- Quantify which covalent and non-covalent catalyst–substrate interactions control reactivity and selectivity, using energy decomposition analysis to build predictive models.
- Model complex effects, including flexible catalysts, solvent cage effects in radical reactions and cooperative effects in multicomponent reactions, to establish general computational approaches.
- Reveal how engineered enzymes running new-to-nature reactions control product handedness, and which active-site residues set reactivity and selectivity, using computational modeling.
More about the research
Steering where the new group attaches is still hard, though, and so is controlling the molecule's 3D handedness, especially with common unactivated alkenes. Without a clear picture of how these reactions work, new catalysts are usually found by trial and error. This project uses computer simulations to explain these reactions and to predict better catalysts before chemists test them in the lab.
Why it matters
The lack of robust, selective methods for making diverse organic molecules is a major bottleneck in drug discovery. Predictive computational models of catalyst behavior could help chemists design metal and enzyme catalysts with less trial and error. This could improve the reactivity, selectivity and range of molecules available to medicinal chemists.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Srinivasan Iyengar
Indiana University, IN·Chemistry
Implement the graph-theory-based fragmentation method asynchronously on hybrid quantum/classical hardware, sending the steepest-scaling calculations to quantum processors and the molecular decomposition to classical computers.
X. Peter Zhang
Boston College, MA·Chemistry
Develop cobalt(II) complexes of D2-symmetric chiral amidoporphyrin ligands as chiral metalloradical catalysts, tuning their electronic, steric and chiral properties to control radical reactivity and stereoselectivity.
Jose Mendoza-Cortes
Michigan State University, MI·Chemistry
Determine whether oxygen–oxygen bonds form by nucleophilic attack or radical coupling in a manganese cubane with a dangling Mn(V)=O unit, combining kinetics, isotope labeling, EPR, X-ray spectroscopy and quantum-chemical calculations.
The school
University of Pittsburgh: 83 labs on PI Pages, $1.5B a year on research, 403 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.