Lee‐Shin Chu

DFMDock is a diffusion model for protein docking that combines generative sampling and energy-based ranking, achieving higher success rates than existing methods.

This paper adapts co-folding models for structure-based protein-protein docking by replacing their template module with a docking module and training with a flow-matching objective, showing competitive performance on specific targets.

ProtAff predicts protein-protein binding affinity using sequence data, outperforming existing methods on an EGFR benchmark but underperforming structural methods in cross-target discrimination.

This paper examines whether diffusion models can extract physics-like energies for protein docking, finding they can recover energy funnels comparable to Rosetta in 9 out of 25 cases.

The paper develops a computational method to design diverse venom libraries and discovers peptides targeting the human itch receptor MRGPRX4.

GeoDock predicts protein-protein docked structures using a multi-track iterative transformer, achieving high success rates and fast inference speeds for rigid and flexible targets.

The paper introduces IgFold, a deep learning method for fast and accurate antibody structure prediction, achieving results comparable to AlphaFold in under a minute.