Turki M. Alturaifi

Turki M. Alturaifi

Ph.D. in Computational Chemistry

University of Pittsburgh

Biography

I am a computational chemist who builds predictive tools and studies reaction mechanisms using DFT, molecular dynamics, QM/MM, cheminformatics, and graph neural networks. I recently (February 2026) completed my Ph.D. with Prof. Peng Liu at the University of Pittsburgh. My work focuses on three main areas:

  1. Transition-Metal and Enzyme Catalysis

    • Mechanistic Insights and Reaction Design Led computational studies on Ni/Pd/Cu/Rh-catalyzed reactions in collaboration with groups at Scripps (Keary Engle), Emory (Huw Davies), UC Davis (Annaliese Franz), UT Dallas (Vladimir Gevorgyan), and more, identifying catalyst and ligand features governing reactivity and selectivity.
    • Enzyme Design DFT/MD work with the DeGrado (UCSF) and Yang (UCSB) labs on de novo designed metalloproteins. Predicted conformer-controlled selectivity in Si-H insertion and guided directed evolution to >99:1 er for Ge-H insertion (Science 2025, JACS 2025).
  2. Cheminformatics and Machine Learning

    • HArD (HeteroAryl Descriptors) Built a database of >31,500 monosubstituted heteroarenes with 65 descriptors (heteroaryl Hammett parameters, aromaticity metrics, HOMO-LUMO gaps, buried volumes). Published in Sci. Data 2025. You can try the tool here: hard.pengliugroup.com
    • RAPID (Radical Polarity Predictor) Trained a graph neural network on >1 million DFT calculations to predict radical polarity for any organic radical from a drawn structure. Collaboration with the Nagib and Hutchison groups. Manuscript in preparation. You can try the tool here: radicalpolarity.pengliugroup.com
  3. High-throughput MD simulations (on-going)

    • Enzyme Selectivity Prediction Exploring whether physics-based descriptors from short classical MD simulations can enhance selectivity prediction for ~900 enzyme variants, building on the MODIFY framework for ML-guided directed evolution.
Interests
  • Computational Chemistry (DFT, MD, QM/MM)
  • Cheminformatics & Molecular Descriptors
  • Machine Learning (GNNs, Property Prediction)
  • Transition Metal & Enzyme Catalysis
Education
  • Ph.D. in Chemistry, 2026

    University of Pittsburgh

  • B.S. in Chemistry, 2021

    Colorado State University

Publications

Alkyl sulfonyl fluorides as ambiphiles in the stereoselective palladium(II)-catalysed cyclopropanation of unactivated alkenes
Alkyl sulfonyl fluorides as ambiphiles in the stereoselective palladium(II)-catalysed cyclopropanation of unactivated alkenes

Alkyl sulfonyl fluorides act as ambiphilic coupling partners in Pd(II)-catalyzed stereoselective cyclopropanation of unactivated alkenes, with the SN2-type C–SO2F oxidative addition serving as the turnover-limiting and diastereoselectivity-determining step.

Hexafluoroisopropanol Solvent Effects on Enantioselectivity of Dirhodium Tetracarboxylate-Catalyzed Cyclopropanation
Hexafluoroisopropanol Solvent Effects on Enantioselectivity of Dirhodium Tetracarboxylate-Catalyzed Cyclopropanation

A combined experimental and computational study revealing how HFIP solvent induces flexibility in dirhodium catalysts to modulate enantioselectivity in cyclopropanation reactions.

Catalytic Addition of Nitroalkanes to Unactivated Alkenes via Directed Carbopalladation
Catalytic Addition of Nitroalkanes to Unactivated Alkenes via Directed Carbopalladation

We report a redox-neutral catalytic coupling of nitroalkanes and unactivated alkenes that proceeds by a directed carbopalladation mechanism.

Three-Component Asymmetric Ni-Catalyzed 1,2-Dicarbofunctionalization of Unactivated Alkenes via Stereoselective Migratory Insertion
Three-Component Asymmetric Ni-Catalyzed 1,2-Dicarbofunctionalization of Unactivated Alkenes via Stereoselective Migratory Insertion

An asymmetric 1,2-dicarbofunctionalization of unactivated alkenes with aryl iodides and aryl/alkenylboronic esters under nickel/bioxazoline catalysis is disclosed.