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.
An overview of the applications of AI in drug delivery is highlighted, focusing on AI-designed drug formulations, AI-driven prediction of ADMET properties, and AI-assisted drug delivery devices, which support the development of precision medicine.
Xinmin Yu, Xinyun Jiang, Tao Sheng et al.· ACS Nano· 1 citation
This paper examines and views the new innovations in combining the use of AI into harmonized medication design and targeted delivery systems, and analyzes AI-driven molecular design with synthetic level reports, emphasizing explainable AI, digital twin platforms, and translations that characterize the next generation o...
Artificial Intelligence (AI) has become a fundamental driver of scientific progress, particularly in disease diagnosis, drug development, and drug delivery optimization. The intersection of AI, drug design, and nanosystems for delivery is accelerating the advancement of personalized nanomedicines and innovative dianano...
It is argued that clinical value will depend on closed-loop workflows in which multimodal predictions are experimentally validated, externally tested, and longitudinally updated to guide the next therapeutic decision.
K. Papavassiliou, A. Sofianidi, Angeliki Margoni et al.· International Journal of Mol...· 0 citations
This review discusses AI‐guided strategies for material design, targeting, payload optimization, and in vivo delivery, with particular attention to protein corona‐mediated biological identity, microenvironment‐responsive activation, and biodistribution modeling.
Sheng-Bin Liu, Zhi Liu, Haixing Shi et al.· MedComm· 0 citations
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
