Kai Ling

Kihara Lab

Department of Computer Science

Purdue University

West Lafayette, IN

I am a CS PhD candidate at Purdue University under the supervision of Prof. Daisuke Kihara. My research focuses on computational biology, particularly in protein structure prediction and structure modeling using deep learning methods.

I received my B.S. in Computer Science from Huazhong University of Science and Technology (HUST) in 2019.

Beyond research, I am an amateur piano soloist. I served as the piano soloist in the Symphony Orchestra of HUST and as piano pedagogy for the Chorus of HUST.

news

Mar 17, 2026	Our review paper: Peptide–protein docking: from physics-based models to generative intelligence accepted by Chem Comm.

selected publications

Chem. Commun.
Peptide–protein docking: from physics-based models to generative intelligence

Kai Ling, Shu Li, Zicong Zhang, and 2 more authors

Chemical Communications, 2026

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A comprehensive review of peptide-protein docking methods, from traditional physics-based approaches to modern generative AI techniques.
@article{ling2026peptide, title = {Peptide--protein docking: from physics-based models to generative intelligence}, author = {Ling, Kai and Li, Shu and Zhang, Zicong and Shin, Woong-Hee and Kihara, Daisuke}, journal = {Chemical Communications}, year = {2026}, }
In Prep.
Distance-AF Multimer: A Multi-Step Approach for Protein Complex Structure Modeling with User-Defined Distance Constraints

Kai Ling and Daisuke Kihara

In preparation, 2026

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A distributed multi-GPU optimization pipeline for protein-complex structure prediction that adapts the Distance-AF method to complexes as a per-target, test-time optimization fitting user-supplied distance constraints. Benchmarked across three datasets with up to 60Å global-RMSD improvement over AlphaFold-Multimer on the hardest targets.
@article{ling2026distanceaf, title = {Distance-AF Multimer: A Multi-Step Approach for Protein Complex Structure Modeling with User-Defined Distance Constraints}, author = {Ling, Kai and Kihara, Daisuke}, journal = {In preparation}, year = {2026}, }