Jin Sub Lee

This paper introduces NCFlow, a generative model for designing peptides with non-canonical amino acids, showing improved binding affinity over existing methods.

The paper uses language modeling to create a comprehensive representation of protein biology, enabling structure prediction and discovery of high-affinity protein interactions.

The paper combines NMR data with AlphaFlow to identify excited state conformers of pro-IL-18, revealing structural insights from transient states.

The paper extends the AlphaFold3 framework to predict structures of cyclic peptides with non-canonical amino acids, outperforming existing models on a test set.

FlowPacker predicts protein side-chain conformations using torsional flow matching and equivariant graph attention, outperforming existing methods and handling missing coordinates and multimeric targets.