Work

Machine Learning for Programmable Nanosystems: From Liposome Formulation to DNA Origami and Hybrid Vesicles

Year: 2026

Type: article

Abstract: Liposomes are lipid-based nanocarriers widely used for the delivery of a broad range of therapeutic cargos. DNA nanostructures are emerging programmable platforms being explored for sensing, biomolecular engineering, and delivery applications. However, the development of both systems is challenged by large design spaces, complex structure-function relationships, and labor-intensive characterization. This mini review discusses how machine learning (ML) can accelerate liposome formulation, DNA origami design, and emerging hybrid liposome-DNA systems. Key examples include predicting liposome quality attributes, guiding microfluidic manufacturing, automating origami characterization, enabling model-guided shape and stability prediction, and optimizing DNA-loaded lipid nanoparticles. This review examines how predictive modeling, interpretable ML, and closed-loop optimization can reduce experimental burden, reveal design rules, and support more robust programmable nanosystems. Further progress will require standardized datasets and better integration of ML with high-throughput experimentation.

Source: Small

DOI: 10.1002/smll.76157

Authors: Daniel Q. Zhang, Matthew A. B. Baker

Institution: UNSW Sydney

Language: English

Topic: Nanoparticle-Based Drug Delivery

Subfield: Biomaterials

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,790

Funder: University of New South Wales