Yinuo Zhang

PeptiVerse provides a unified platform for predicting therapeutic peptide properties using both amino acid sequences and SMILES-based representations, supporting early-stage development and generative design.

This study identifies 62 Alzheimer's disease-associated genes using retinal transcriptome-wide association studies and validates 31 in an independent cohort, highlighting immune dysregulation and novel risk genes.

The paper develops a computational method to guide the cyclization of saxitoxin aptamers, showing that circular aptamers have higher affinity and improved survival in mice.

scPharm integrates pharmacogenomics profiles with scRNA-seq data to identify drug-sensitive subpopulations in cancer tissues and predict combination therapies.

moPPIt generates motif-specific and functionally active peptide binders using sequence data, validated in vitro and in disease-relevant assays.

MetaLATTE predicts metal binding in proteins using multi-task learning on ESM-2 embeddings, enabling classification of metal-binding properties without requiring structural input.