Systematic study on applying machine learning methods - Elastic Net (ENET), Random Forest (RF), Gradient Boosting Machine (GBM), Multi-layer Perceptron (MLP), Gaussian Process Regressor (GPR) - to accelerate TYK2-inhibitor protein-ligand free energy calculations. Source code and dataset available are open-sourced on GitHub.

Cite: Optimizing active learning for free energy calculations,  J Thompson, W P Patricks, J A Feng, N A Pabon, H Xu, B B Goldman, ... & F York, Artificial Intelligence in the Life Sciences, 2022

Computational simulations to reveal how chiral cysteine attached to nanoparticle selects DOPA (related to dopamine) through matching hydrogen bonds.

Cite: Mesoporous encapsulated chiral nanogold for use in enantioselective reactions, Y Zhou, H Sun, H Xu, S Matysiak, J Ren, X Qu, Angewandte Chemie

Studies the effect of protonation on chitosan hydrogel network structure formation, and develops molecular model that fits well with experimentally-measured structural and mechanical properties.

Cite: Effect of pH on chitosan hydrogel polymer network structure, H Xu, S Matysiak, Chemical Communications

Computationally simulates the effect of ligand size on shaping clusters on the cell membrane surface.

Cite: Influence of Monovalent Cation Size on Nanodomain Formation in Anionic–Zwitterionic Mixed Bilayers, SJ Ganesan, H Xu, S Matysiak (co-first author)

Develops proxy models to simulate Alzheimer's pathological peptide with order of magnitude improvement in computational efficiency.

Cite: Effect of lipid head group interactions on membrane properties and membrane-induced cationic β-hairpin folding, SJ Ganesan, H Xu, S Matysiak, (co-first author)

Studies self-assembly processes in cell membrane, peptide folding, and hydrogel systems with Coarse-Grained molecular models