Application of machine learning in the design of biomedical nanomaterials

As artificial intelligence technology advances rapidly, machine learning shows great potential in materials science. It can efficiently process and analyze large-scale data, uncover complex patterns, and offer new approaches for material prediction and design. This review focuses on the application progress of machine-learning models in biomedical nanomaterial design. Firstly, it presents the basic principles, development history, and advantages of machine learning in this field. Then, it provides a deep analysis of the technical breakthroughs in machine-learning models for predicting physicochemical properties, optimizing synthesis routes, and analyzing biological interactions of nanomaterials. Finally, it addresses the challenges of insufficient data quality and limited model interpretability in machine learning, and proposes solutions like developing more explainable machine-learning methods, standardizing data-reporting formats, combining machine learning with traditional molecular simulations, and enhancing result verification. Overall, this review aims to promote a shift from experience-driven to data-driven approaches in the field of biomedical nanomaterial design and to facilitate innovation and clinical translation in this area.

Duke Scholars

Author Sharon Gerecht Biomedical Engineering