Protein-peptide interactions are central to many biological processes, but the scarcity of experimental data makes their prediction challenging. In our latest work, Xinke Zhan led the development of PepInter, a deep learning framework that leverages pretrained protein language models and large-scale structural pretraining to learn interaction-aware representations, achieving strong results across multiple benchmarks. We’re excited about the potential of this approach to support next-generation peptide therapeutics and AI-driven drug discovery.

Congratulations to Xinke and all collaborators on this achievement.

Read more: https://www.nature.com/articles/s42004-026-02168-3

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