Zhanghan Ni — accepted AI-conference papers

2026

MutAtlas: A PDB-Wide Energy-Guided Atlas of Protein Mutation Effects

Ruihan Guo, Chaoran Cheng, Zhanghan Ni, Neil He, Bangji Yang, Ge Liu

ICML 2026poster

Predicting protein mutation effects is fundamental to protein engineering and disease variant interpretation, yet experimental mutation data remain accurate but extremely sparse. Large-scale computational augmentation offers scalability, but introduces heterogeneous and poorly calibrated supervision…

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2026

RigidSSL: Rigidity-based Geometric Pretraining for Protein Generation

Zhanghan Ni, Yanjing Li, Zeju Qiu, Bernhard Schölkopf, Hongyu Guo, Weiyang Liu +1

ICLR 2026poster

Protein design stands as one of biology’s most important frontiers, with the potential to transform medicine, advance human health, and drive sustainability. Protein generation, a central task in protein design, has been greatly accelerated by AI-driven models—such as FoldFlow, MultiFlow, and AlphaF…

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