I am a first-year PhD student in Bioinformatics at Shanghai Jiao Tong University. My research focuses on applying artificial intelligence to the design and optimization of proteins, enzymes, and antibodies.
I used to be a dedicated distance runner, with a personal best of 19 minutes for the 5K. I completed my first half-marathon in Shangyu, Shaoxing. More recently, I have developed a strong interest in strength training—especially deadlifts.
Outside of research and training, I enjoy spending time outdoors and connecting with people. I also have a cat named Panghu (胖虎), who brings plenty of fun to my everyday life.
Education
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Shanghai Jiao Tong UniversityPh.D. in BioinformaticsSep. 2026 - Present -
Shanghai Jiao Tong UniversityM.S. in BioinformaticsSep. 2023 - Jul. 2026
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Huazhong Agricultural UniversityB.S. in BioinformaticsSep. 2019 - Jun. 2023
Honors & Awards
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Outstanding Graduate, Shanghai Jiao Tong University2026
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National Scholarship2025
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Shanghai Jiao Tong University Excellence Scholarship2024
Blog (view all )
Selected Publications (view all )
A unified predictor of protein stability changes across all mutation types via implicit structure learning
Hong Tan, Shenggeng Lin, Yi Xiong
Chemical Science 2026
UniStab predicts protein stability changes across single-point mutations, multi-point mutations, and insertions or deletions by learning implicit structural representations from a pretrained protein folding model.
A unified predictor of protein stability changes across all mutation types via implicit structure learning
Hong Tan, Shenggeng Lin, Yi Xiong
Chemical Science 2026
UniStab predicts protein stability changes across single-point mutations, multi-point mutations, and insertions or deletions by learning implicit structural representations from a pretrained protein folding model.
RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design
Shenggeng Lin, Hong Tan, Keyao Wang, Ruixuan Wang, Hongxia Wang, Tong Zhu, Yi Xiong
Genome Biology 2026
RNARL unifies RNA sequence generation and multi-objective optimization in a reinforcement learning framework for efficient, generalizable codon design.
RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design
Shenggeng Lin, Hong Tan, Keyao Wang, Ruixuan Wang, Hongxia Wang, Tong Zhu, Yi Xiong
Genome Biology 2026
RNARL unifies RNA sequence generation and multi-objective optimization in a reinforcement learning framework for efficient, generalizable codon design.
Design of permeability-optimized target-binding macrocycles via direct preference optimization
Heqi Sun, Hong Tan, Yanyi Chu, Jiayi Li, Ruixuan Wang, Dong-Qing Wei
Chemical Science 2026, 17(20), 10223–10236.
CycDiff-DPO is a preference-aligned diffusion framework for designing target-specific macrocyclic peptide binders with improved membrane permeability while preserving binding competence.
Design of permeability-optimized target-binding macrocycles via direct preference optimization
Heqi Sun, Hong Tan, Yanyi Chu, Jiayi Li, Ruixuan Wang, Dong-Qing Wei
Chemical Science 2026, 17(20), 10223–10236.
CycDiff-DPO is a preference-aligned diffusion framework for designing target-specific macrocyclic peptide binders with improved membrane permeability while preserving binding competence.
ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding models
Hong Tan, Xiaowei Wei, Shenggeng Lin, Xueying Mao, Junwei Chen, Heqi Sun, Yufang Zhang, Zhenghong Zhou, Dong-Qing Wei, Shuangjun Lin, Yi Xiong
bioRxiv 2025
ProStab integrates mutation-aware protein language model embeddings with inverse-folding-derived structural features to predict protein stability changes caused by single-point mutations.
ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding models
Hong Tan, Xiaowei Wei, Shenggeng Lin, Xueying Mao, Junwei Chen, Heqi Sun, Yufang Zhang, Zhenghong Zhou, Dong-Qing Wei, Shuangjun Lin, Yi Xiong
bioRxiv 2025
ProStab integrates mutation-aware protein language model embeddings with inverse-folding-derived structural features to predict protein stability changes caused by single-point mutations.
