Chentong Wang

This paper introduces Pinal, a foundation model that designs proteins from natural language descriptions, achieving functional alignment and experimental success in four design tasks.

This paper introduces a physics-based generative method for designing ligand-binding proteins and demonstrates its application in creating biosensors for small molecules and metal ions.

This paper designs metalloproteases that specifically cleave targeted peptide bonds in amyloid-β using a generative model, achieving high specificity and catalytic efficiency.

HalluDesign optimizes and designs proteins using iterative structure hallucination and sequence design, achieving high success rates and structural accuracy in various tasks.

The paper introduces the Dayhoff Atlas, a resource with large protein sequence datasets and generative models to improve protein design and prediction.

This paper introduces PTM-Mamba, a protein language model that incorporates PTM tokens and Mamba blocks to improve performance on PTM-specific tasks.

Proteus generates protein backbones efficiently without pre-training, showing high designability in in silico and experimental tests.