PepMSND: integrating multi-level feature engineering and comprehensive databases to enhance <i>in vitro</i>/<i>in vivo</i> peptide blood stability prediction
Deep learning has emerged as a transformative tool for peptide drug discovery, yet predicting peptide blood stability—a critical determinant of bioavailability and therapeutic efficacy—remains a major challenge.
Authors
- Hongliang Duan (ORCID: https://orcid.org/0000-0002-9194-0115)
- An Su (ORCID: https://orcid.org/0000-0002-6544-3959)
- Haitao Hu (ORCID: https://orcid.org/0000-0003-0708-6233)
- Jingjing Guo (ORCID: https://orcid.org/0009-0003-9645-2246)
- Zhenyu Xu (ORCID: https://orcid.org/0000-0002-3142-1161)
- Chengxi Li (ORCID: https://orcid.org/0000-0003-1102-0181)
- Chengyun Zhang (ORCID: https://orcid.org/0000-0002-8499-5273)
Institutions
- Shenzhen Baoan High School Group (CN)
- Macao Polytechnic University (MO)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00118h
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundo para o Desenvolvimento das Ciências e da Tecnologia
- Natural Science Foundation of Zhejiang Province