T. I. Hung

This study introduces a deep learning model to analyze conformational transitions in cyclic peptides, revealing key residues and nonlinear torsional motions that govern structural changes.

This study quantifies molecular entropy and its changes during ligand binding using MD simulations and machine learning, revealing key motions and their thermodynamic impacts.

This paper introduces a deep learning model to sample conformational ensembles of dynamic proteins, demonstrating its ability to generate novel conformations and rationalize experimental findings.

This study computationally models PROTAC-induced protein dynamics to explain variation in degradation potency despite identical warheads.