Yitao Ke

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 introduces IMUSE, a method combining machine learning and ultra-high-throughput screening to explore enzyme spaces, identifying mutations that significantly improve enzyme activity.

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