Cheng Zhang

This paper compares chemical descriptors and deep learning embeddings for scoring de novo peptide designs, finding classical descriptors are effective and efficient for developability prediction.

FrameDiPT is a diffusion model for protein structure inpainting that recovers masked sections using sequence and structural context, achieving comparable accuracy to existing models with limited data.