Huiling Shao
Research Group
Research Focus
Catalysis
quantum chemistry | mechanism | machine learning
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Goal: To enable autonomous reaction discovery and the design of new organometallic catalysts.
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Challenge: Existing reaction-centered experimental databases are isolated, limiting the development of a universal predictive algorithm.
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Our Approach: We aim to bridge these isolated databases by constructing comprehensive reaction networks.
Cyclic Peptide
molecular dynamics | permeability|
AI structure prediction
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Goal: To establish structure-function relationships for the rational design of membrane-permeable cyclic peptides.
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Challenge: Simulation data across diverse environments is insufficient to support predictive modeling of cyclic peptide conformational "flipping."
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Our Approach: Training AI models on multi-solvent molecular dynamics datasets to predict and optimize environment-dependent cyclic peptide structures.
Collaboration
mechanism elucidation | catalyst validation | rational drug design
Our group collaborates closely with experimental research teams to provide mechanistic elucidation for complex transformations, validate and optimize designed organometallic catalysts, and accelerate the design of small-molecule drug candidates.
Dr. Huiling Shao
Huiling Shao joined Nanyang Technological University as an Assistant Professor in January 2026. Her research integrates quantum chemistry and machine learning to solve challenges at the interface of organometallic catalysis and peptide drug discovery. Prior to joining NTU, Dr. Shao conducted postdoctoral research at UCLA (2021-2025) and University of Minnesota (2019-2021), following the completion of her Ph.D. at the University of Pittsburgh (2019).
