TY - JOUR
T1 - Protein-based nanomedicines for cancer theranostics
T2 - From supramolecular self-assembly to AI-driven design and applications
AU - Cheng, Hong Bo
AU - Cheng, Yang
AU - Wang, Junlan
AU - Liu, Hancong
AU - Zhang, Keyue
AU - Wu, Ruotong
AU - Li, Jiaxin
AU - Fang, Yanyan
AU - Hu, Runjing
AU - Jeong, Hyunsun
AU - Dyson, Paul J.
AU - Liang, Xing Jie
AU - Yoon, Juyoung
N1 - Publisher Copyright:
© 2025 Elsevier Inc.
PY - 2025/10/9
Y1 - 2025/10/9
N2 - Protein-based nanomedicines represent a paradigm shift in cancer theranostics, capitalizing on superior biocompatibility, molecular recognition, and multifunctional adaptability. This review delineates their evolution from supramolecular self-assembly to artificial intelligence (AI)-driven design, emphasizing their transformative role in cancer chemotherapy, phototherapy, chemodynamic therapy, and immunotherapy. Supramolecular strategies, including metal coordination, electrostatic interactions, host-guest chemistry, hydrogen bonding, π-π stacking, and hydrophobic interactions, not only enable precise control over protein assemblies but also facilitate drug delivery and performance. AI tools like AlphaFold 3 and RFdiffusion have accelerated de novo protein design and dynamic interaction prediction, overcoming limitations in structural prototyping. Despite breakthroughs, challenges persist in the mechanistic insights into assembly dynamics, experimental validation of AI-generated constructs, and scalable clinical translation. Future directions prioritize integrated theranostics platforms, multi-omics-guided precision medicine, and synthetic biology. By synergizing supramolecular chemistry, AI, and nanotechnology, this review envisions protein-based nanomedicines as intelligent, adaptive systems poised to redefine paradigms of cancer theranostics.
AB - Protein-based nanomedicines represent a paradigm shift in cancer theranostics, capitalizing on superior biocompatibility, molecular recognition, and multifunctional adaptability. This review delineates their evolution from supramolecular self-assembly to artificial intelligence (AI)-driven design, emphasizing their transformative role in cancer chemotherapy, phototherapy, chemodynamic therapy, and immunotherapy. Supramolecular strategies, including metal coordination, electrostatic interactions, host-guest chemistry, hydrogen bonding, π-π stacking, and hydrophobic interactions, not only enable precise control over protein assemblies but also facilitate drug delivery and performance. AI tools like AlphaFold 3 and RFdiffusion have accelerated de novo protein design and dynamic interaction prediction, overcoming limitations in structural prototyping. Despite breakthroughs, challenges persist in the mechanistic insights into assembly dynamics, experimental validation of AI-generated constructs, and scalable clinical translation. Future directions prioritize integrated theranostics platforms, multi-omics-guided precision medicine, and synthetic biology. By synergizing supramolecular chemistry, AI, and nanotechnology, this review envisions protein-based nanomedicines as intelligent, adaptive systems poised to redefine paradigms of cancer theranostics.
KW - SDG3: Good health and well-being
KW - artificial intelligence
KW - cancer
KW - protein-based nanomedicines
KW - supramolecular chemistry
KW - theranostics
UR - https://www.scopus.com/pages/publications/105008584827
U2 - 10.1016/j.chempr.2025.102624
DO - 10.1016/j.chempr.2025.102624
M3 - Review article
AN - SCOPUS:105008584827
SN - 2451-9308
VL - 11
JO - Chem
JF - Chem
IS - 10
M1 - 102624
ER -
