Jianfeng Pei

DiffTernary is a diffusion model for predicting molecular glue ternary complex structures, achieving competitive performance on benchmark datasets.

CellChem learns cell-guided molecular representations that integrate structure and function, improving drug discovery by capturing cellular response similarities.

This paper evaluates co-folding methods for predicting ternary complexes involving molecular glues, finding AlphaFold3 performs best but relies on memorization rather than accurate modeling.

The paper reconstructs the spatiotemporal gene expression blueprint for C. elegans pharyngeal cuticle construction and identifies phase-separating proteins involved in cuticle remodeling.

CavitySpace identifies potential ligand binding sites in the human proteome using predicted structures, aiding in drug design and function prediction.

The paper develops a deep learning model to screen antiviral drugs against SARS-CoV-2, identifying a novel compound with promising inhibitory activity.

The paper develops a machine learning tool, PSPredictor, to predict phase separation proteins using sequence information, achieving high accuracy and outperforming existing methods.