Artificial intelligence and machine learning-guided bio-orthogonal engineering of smart soft polymeric nanocarriers for precision drug delivery and translational nanomedicine
- 1Department of Chemistry, Technical University of Denmark
- 2Department of Health Technology, DTU Health Tech, Technical University of Denmark
Abstract
Soft polymeric nanocarriers provide programmable platforms for precision drug delivery, yet their rational design remains constrained by complex relationships among polymer chemistry, soft-matter properties, formulation, and biological performance. Bioorthogonal chemistry offers a modular route to functionalize these systems under physiologically compatible conditions, enabling selective ligand installation, responsive crosslinking, and therapeutic activation. Artificial intelligence and machine learning can complement these capabilities by predicting polymer behavior, optimizing reaction and formulation parameters, and linking physicochemical descriptors with biological outcomes. This Mini Review discusses the convergence of AI/ML and bioorthogonal chemistry for engineering smart soft polymeric nanocarriers, highlights opportunities in closed-loop discovery and personalized nanomedicine, and examines translational barriers involving datasets, interpretability, reproducibility, manufacturability, and regulation.
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BibTeX
@article{basak2026artificial,
title = {{Artificial intelligence and machine learning-guided bio-orthogonal engineering of smart soft polymeric nanocarriers for precision drug delivery and translational nanomedicine}},
author = {Suman Basak},
journal = {Frontiers in Soft Matter},
year = {2026},
month = jul,
doi = {10.3389/frsfm.2026.1883618},
url = {https://doi.org/10.3389/frsfm.2026.1883618},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence and machine learning-guided bio-orthogonal engineering of smart soft polymeric nanocarriers for precision drug delivery and translational nanomedicine
AU - Suman Basak
PY - 2026
DA - 2026/07/03
JO - Frontiers in Soft Matter
DO - 10.3389/frsfm.2026.1883618
UR - https://doi.org/10.3389/frsfm.2026.1883618
AB - Soft polymeric nanocarriers provide programmable platforms for precision drug delivery, yet their rational design remains constrained by complex relationships among polymer chemistry, soft-matter properties, formulation, and biological performance. Bioorthogonal chemistry offers a modular route to functionalize these systems under physiologically compatible conditions, enabling selective ligand installation, responsive crosslinking, and therapeutic activation. Artificial intelligence and machine learning can complement these capabilities by predicting polymer behavior, optimizing reaction and formulation parameters, and linking physicochemical descriptors with biological outcomes. This Mini Review discusses the convergence of AI/ML and bioorthogonal chemistry for engineering smart soft polymeric nanocarriers, highlights opportunities in closed-loop discovery and personalized nanomedicine, and examines translational barriers involving datasets, interpretability, reproducibility, manufacturability, and regulation.
SN - 2813-0499
ER -