Author

Kang Lin

Zhejiang University of TechnologyPrimary ORCID profile (0000-0002-0632-1441 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Kang LiniD1 paper · China National Biotechnology (China)

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:protein design· 2 Assay:protein structure· 2 Approach:diffusion model· 1 Approach:statistical model· 1

Frequent Co-Authors

Chengyun ZhangiD· 2 shared Hongliang DuaniD· 2 shared Ning Zhu· 2 shared An SuiD· 1 shared Cheng ZhuiD· 1 shared Chengxi LiiD· 1 shared Chenhao ZhangiD· 1 shared Huitian LiniD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersBioinformatics

Chengyun ZhangORCID

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2 papers

Recent·Most cited

Cycledesigner Leveraging RFdiffusion and HighFold to Design Cyclic Peptide Binders for Specific Targets

Chenhao Zhang, Zhenyu Xu, Kang Lin, Ning Zhu, Chengyun Zhang, Wen Xu, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan

This study introduces cycledesigner, a method combining RFdiffusion, ProteinMPNN, and HighFold to design cyclic peptide binders for specific targets, followed by scoring functions for screening.

Journal of Chemical Information and Modeling · Jun 10, 2025Peer-reviewedBioinformatics

HighPlay：Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction

Huitian Lin, Cheng Zhu, Tianfeng Shang, Ning Zhu, Kang Lin, Chengyun Zhang, Xiang Shao, Xudong Wang, Hongliang Duan

HighPlay uses reinforcement learning and protein structure prediction to design cyclic peptides with good binding affinity, validated through molecular dynamics simulation and experimental testing.

Journal of Medicinal Chemistry · May 23, 2025Peer-reviewedBioinformatics