Machine Learning for the Design and Optimization of Bio-Nanomaterials

Machine learning (ML) supports the design and optimization of bio-nanomaterials, but comparisons across chemically distinct material classes remain limited. This review addresses this gap through a common framework linking material representations and design tasks to validation strategies and material or biomedical endpoints. We compare four classes comprising polymeric, carbon-based, metal and metal oxide, and metal-organic framework systems, including larger assemblies with functional nanoscale features. The comparison identifies a shared requirement for representations that connect adjustable material and processing variables to the target property and its measurement conditions. For bio-nanocomposites, these representations also capture the composition and interfaces of two or more constituent components or phases. Across material classes, ML supports property prediction and candidate selection. Many studies rely on internal model evaluation or material-level confirmation, with limited independent testing or biological validation. The strength of design conclusions depends on whether validation reflects the intended application and experimental conditions. Small, heterogeneous datasets and inconsistent biological endpoints further restrict model comparison and transfer to new experimental settings. Priorities include standardized multimodal datasets, interpretable models, calibrated uncertainty estimates and independent validation. Future closed-loop workflows should connect synthesis and characterization with biological testing. Experimental feedback can then refine predictions and guide subsequent material design.