Engineering Polymeric Vectors for Nucleic Acid Delivery: Linking Molecular Architecture, Biological Function, and Computational Design

Abstract

Cationic polymers have been widely studied as non-viral vectors for therapeutic nucleic acid delivery. This is because their chemical structure can be modified across molecular weight, charge density, composition, and overall architecture. These features determine how polymers interact with nucleic acids and with the biological environment. Yet, despite extensive development of cationic polymer vectors, predicting biological performance from polymer structure remains difficult and provides insufficient data. This review examines how molecular weight, polymer composition, cationic group chemistry and density, pKa, and polymer topology influence complexation efficiency with nucleic acid, polymer to nucleic acid ratio (N/P), polyplex stability, cellular uptake, endosomal escape, cytotoxicity, and transfection efficiency. Particular attention is given to linear, branched, cyclic, star-shaped, and brush polymer architectures, with emphasis on the trade-offs that arise when structural features improve one stage of delivery while compromising another. Chemical modification strategies, including PEGylation, fluorination, amino acid functionalization, hydrophobic substitution, targeting ligand conjugation, and stimuli-responsive design, are discussed in relation to the biological barrier overcoming and transfection performance in complex biological systems. The review also considers the growing use of artificial intelligence, machine learning, and molecular dynamics simulations, which support polymer design, structure-function prediction, and polymer candidate selection. Progress toward clinical translation is discussed alongside persistent limitations in reproducibility, scalable synthesis, formulation consistency, safety, and the poor transfer of in vitro performance to in vivo systems. By bringing these relationships together, the review provides a structure-function basis for interpreting polymer performance and for designing more effective nucleic acid delivery systems.