Author

Qiushi Cao

Peking UniversityORCID 0000-0002-2768-0814Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Research Focus:software / tool· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Chen SongiD· 2 shared Ruihan DongiD· 2 shared Kai KangiD· 1 shared Lei WangiD· 1 shared Tadsanee AwangiD· 1 shared Zefeng ZhuiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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2 shared papersBioinformatics

Chen SongORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJun 25, 2026

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides

Ruihan Dong, Tadsanee Awang, Qiushi Cao…·Chen Song

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

1 citation

protein sequencegraph neural network

𝕏 PostBluesky

BioinformaticsAdvanced SciencePeer-reviewedSep 12, 2025

Painting Peptides with Antimicrobial Potency through Deep Reinforcement Learning

Ruihan Dong, Qiushi Cao, Chen Song

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.

11 citationstop 3%

protein sequencedeep learning

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