Yi Hao

DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design

DesignMaster improves PROTAC design by incorporating linker length and physicochemical constraints through a diffusion framework, achieving better validity and recovery than existing methods.

MolX: A Geometric Foundation Model for Protein-Ligand Modelling

MolX is a geometric foundation model that jointly learns representations of protein pockets and ligands from 3D structural data, achieving state-of-the-art performance in predicting molecular interactions.

Structure-aware geometric graph learning for modeling protease-substrate specificity at scale

OmniCleave models protease-substrate specificity using a geometric graph framework, outperforming existing methods and identifying novel substrates.

Graph-based RNA structural representation reveals determinants of subcellular localization

GRASP uses a graph neural network to predict RNA subcellular localization by modeling RNA structure, outperforming existing methods across RNA types and transcript lengths.