Citation: Yanchao Zhang, Man Zhang, Wantong Zhang, Shuangshuang Ji, Xufang Hu, Nianrong Sun, Chunhui Deng. Smart peptidomic diagnostics: Hydrophilic molecular traps with machine learning-driven pattern recognition[J]. Chinese Chemical Letters, ;2026, 37(12): 112765. doi: 10.1016/j.cclet.2026.112765 shu

Smart peptidomic diagnostics: Hydrophilic molecular traps with machine learning-driven pattern recognition

Yanchao Zhang ^a ,
Man Zhang ^a ,
Wantong Zhang ^a ,
Shuangshuang Ji ^{b, *,} ,
Xufang Hu ^c ,
Nianrong Sun ^{d, *,} ,
Chunhui Deng ^{e, *,}

a.
Department of Chemistry, Fudan University, Shanghai 200433, China
b.
Maiyuan Biological Technology Co., Ltd., Yangzhou 225000, China
c.
School of Chemical Science and Technology, National Demonstration Center for Experimental Chemistry and Chemical Engineering Education, Yunnan University, Kunming 650091, China
d.
Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
e.
Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 201399, China

jsmy202011@163.com

sunnianrong@fudan.edu.cn

chdeng@fudan.edu.cn

Received Date: 30 August 2025
Revised Date: 31 March 2026
Accepted Date: 9 April 2026
Available Online: 10 April 2026

Figures(5)

With the rapid advancement of artificial intelligence, smart diagnosis is emerging as a transformative force in modern medicine. Herein, we developed a hydrophilic molecular trap to combine with machine learning to collect and analyze hydrophilic peptide patterns fully from human serum samples, enabling effective smart peptidomic diagnostics. Utilizing a hydrophilic molecular trap combined with high-throughput mass spectrometry, high-quality hydrophilic peptide profiles were extracted from serum samples of gastric cancer (GC) patients, pancreatic ductal adenocarcinoma (PDAC) patients, and healthy controls (HC). Machine learning clearly distinguished GC, PDAC, and HC, with the area under the receiver operating characteristic curve exceeding 0.949 for all paired group comparisons. Furthermore, 4 (HC vs. PDAC), 5 (HC vs. GC) and 3 (GC vs. PDAC) key peptides features were selected out, which could be used to achieve precise diagnosis in paired groups. This work demonstrates the strong clinical potential of our smart peptidomic diagnostic approach for accurate disease diagnosis and real-time monitoring.
- Diagnosis,
- Machine learning,
- Peptidomics,
- Magnetic materials,
- Hydrophilic mesopores
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沈阳化工大学材料科学与工程学院沈阳 110142