Shuo Zhang

GEM-GPT generates personalized therapeutic molecules to shift cell-type-specific disease states toward healthy phenotypes, outperforming baselines in chemical perturbation and CRISPR experiments.

ProMoNet improves drug discovery by predicting protein-ligand binding affinity and kinetics using sequence-based pre-training without 3D structural data.

This paper introduces a signed multi-omics knowledge graph foundation model for drug action and clinical response predictions, achieving high accuracy and efficiency.

The paper introduces eMOSAIC, a method for uncertainty quantification in multi-target ligand binding affinity predictions, showing it outperforms existing methods in out-of-distribution settings.

MolGene-E generates high-quality drug molecules from single-cell transcriptomics data, outperforming baselines in zero-shot molecule generation.

PortalCG predicts chemical-protein interactions for dark proteins using a deep learning framework that integrates binding site information and meta-learning, outperforming existing methods in out-of-distribution scenarios.