Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design
This review comprehensively examines the application of artificial intelligence (AI) to revolutionize precision oncology across all phases of drug development, including target discovery and molecular design, nanomedicine delivery, and resistance mitigation. Deep learning, systems biology, and multi-omics analytics enabled by AI accelerate target discovery, lead optimization, and personalized therapy. This study focuses on a new area of AI-driven nanocarrier design, adaptive therapy design, and digital twin clinical decision support. AI bridges the knowledge gap between molecular information and real-world data to enable forecasting, transparent, patient-centered cancer treatment, and the foundation for a data platform to counsel future generations of cancer patients, therapies, and resistance control.
Authors
- Zhenglun Yu (ORCID: https://orcid.org/0000-0001-6303-031X)
- Lei Zhang (ORCID: https://orcid.org/0000-0001-6481-2102)
- L liu
- Jing Zhang (ORCID: https://orcid.org/0000-0002-4530-6754)
- Li Mu
- Lanlan Guo
- Jun Li
Institutions
- Fourth Affiliated Hospital of China Medical University (CN)
- First Hospital of China Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2026-08-20
- DOI
- https://doi.org/10.1038/s41698-026-01513-y
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00