Ruihan Dong

This paper introduces AMPainterV2, a model that designs membrane-lytic antimicrobial peptides using generative adversarial imitation learning, achieving active peptides with improved activity and miniaturization.

PepMCP predicts membrane contact probability for short peptides using graph-based modeling, achieving high correlation with experimental data.

This study screens antimicrobial peptides using a mechanism-driven approach, identifying seven peptides with membrane-targeting and pore-forming activity that show antimicrobial efficacy with low cytotoxicity.

This paper introduces AMPainter, a deep reinforcement learning model for designing antimicrobial peptides, which outperforms existing models and generates effective AMPs from signal peptides and random sequences.

This paper designs bifunctional antimicrobial peptides using deep learning, discovering 16 peptides with both antibacterial and antiviral activity, including two validated against drug-resistant bacteria and viruses.