CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring
This work provides a brand-new and effective pipeline for screening anti-HAPE agents by integrating artificial intelligence (AI) tools and Cell Painting, offering a novel perspective for AI-driven phenotypic drug discovery.
Authors
- Yue Gao (ORCID: https://orcid.org/0000-0003-1131-5326)
- Zhexin Ni (ORCID: https://orcid.org/0000-0002-9723-2398)
- Zhijie Bai (ORCID: https://orcid.org/0000-0003-2231-1320)
- Yang-yi Hu
- Wei Zhou (ORCID: https://orcid.org/0000-0003-0435-7261)
- Xiru Yang
- Chaoji Huangfu (ORCID: https://orcid.org/0000-0002-8650-7149)
- Congshu Huang
- Ningning Wang (ORCID: https://orcid.org/0000-0002-1363-3101)
- Yongfang Li (ORCID: https://orcid.org/0000-0002-2565-2748)
- Dezhi Sun (ORCID: https://orcid.org/0000-0002-0069-452X)
- Xianglin Tang
- Pan Shen
Institutions
- Qinghai University (CN)
- Henan University (CN)
- Chinese PLA General Hospital (CN)
- National Institutes of Pharmaceutical Research and Development (China) (CN)
Publication Details
- Journal
- Respiratory Research
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1186/s12931-025-03173-1
- Citations
- 4
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.54
Funders
- National Natural Science Foundation of China
- State Administration of Traditional Chinese Medicine of the People's Republic of China