Tong Pan

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

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.

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

ACENet predicts enzyme pHmin using a graph neural network integrating structural and evolutionary features, achieving high accuracy and stability across conditions.

The paper develops a disease-specific histone modification prediction model using a large language model and MoE architecture, achieving high AUROC scores and identifying AD-associated variants.

KcatNet predicts enzyme catalytic efficiency using geometric deep learning, outperforming existing methods and providing insights into enzyme-substrate interactions.

The paper introduces Mimosa, a Transformer-based model for miRNA target prediction that incorporates non-canonical base-pairing patterns, achieving better performance than traditional methods.

SCREEN is a graph neural network that predicts catalytic residues and assesses mutation tolerance in enzymes by integrating structural and functional information.