Author

Gaoxing Guo

Fudan University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:protein design· 2 Research Focus:benchmarking· 1 Assay:electronic health records· 1 Approach:graph neural network· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Qiang HuangiD· 3 shared Jiaqiang QianiD· 2 shared Xiaoming HeiD· 2 shared Xinzhou Qian· 2 shared Fuyan Liu· 1 shared Gary Liu· 1 shared Lei Wang· 1 shared Lei WangiD· 1 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersBioinformatics

Qiang HuangORCID

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Author profile →

4 papers

Recent·Most cited

ParSeek: Accurate cryo-EM particle picking with a deep learning model trained on synthetic data

Jiaqiang Qian, 龚有盛, Fuyan Liu, Yimiao Huang, Gaoxing Guo, Ya Zhu, Qiang Huang

ParSeek is a deep learning model for cryo-EM particle picking trained on synthetic data, achieving high accuracy and outperforming existing methods on multiple datasets.

bioRxiv · May 11, 2026 · PreprintMolecular Biology

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling

Lei Wang, Xiaoming He, Xinzhou Qian, Gaoxing Guo, Qiang Huang

This paper develops a computational method using graph neural networks and Metropolis Monte Carlo sampling to design high-affinity nanobodies, achieving a Pearson correlation of 0.62 on benchmark.

Briefings in Bioinformatics · Mar 1, 2026Peer-reviewedBioinformatics

IntelliFold-2: Surpassing AlphaFold 3 via Architectural Refinement and Structural Consistency

Lifeng Qiao, Yan He, Gary Liu, Gaoxing Guo, Siqi Sun

IntelliFold-2 improves protein structure prediction accuracy and robustness over AlphaFold 3 using architectural refinements and structural consistency, with strong gains in antibody-antigen and protein-ligand interactions.

bioRxiv · Feb 14, 2026 · PreprintBioinformatics

De novo design of protein-binding aptamers through deep reinforcement learning assembly of nucleic acid fragments

Gaoxing Guo, Liangwei Guo, Jiaqiang Qian, Xiaoming He, Xinzhou Qian, Lei Wang, Qiang Huang

This paper introduces AiDTA, a deep reinforcement learning method for de novo design of protein-binding aptamers, achieving high-affinity DNA aptamers for disease-related proteins.

bioRxiv · Jun 1, 2025 · PreprintBioinformatics