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 closedloop discovery and personalized nanomedicine, and examines translational barriers involving datasets, interpretability, reproducibility, manufacturability, and regulation.
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