Junwei Chen

RNASTOP predicts and optimizes mRNA chemical stability using deep learning, improving accuracy by 13% and reducing free energy in a vaccine sequence.

This paper introduces AnewOmni, a generative model that designs biomolecules across scales by learning interaction patterns and physical constraints from 5 million complexes.

The paper introduces Delta PSA, a metric for predicting macrocyclic peptide permeability using MD simulations, achieving lower MSE than existing methods.

ProStab predicts protein stability changes upon mutations using a deep learning framework combining sequence and structure information, showing strong performance and experimental validation.

GRATCR generates epitope-specific TCR sequences with fewer training data than state-of-the-art models, showing higher specificity and biological functionality.

This paper introduces Hot2Mol, a deep learning framework for designing drug-like PPI inhibitors by generating molecules that mimic hot-spot residues, showing improved performance over baselines.

This paper introduces MultiPPIMI, a deep learning framework that predicts PPI-modulator interactions without requiring target structure or reference modulators, achieving high AUROC scores in benchmark tests.