Biomedical nanomaterials promise revolutionary drug delivery, yet only a few have reached clinics after 40 years’ trial-and-error, highlighting a critical gap between fundamental research and clinical application. Mining tens of thousands of published articles with machine learning could bridge this gap by revealing design rules, shortening R&D cycles and accelerating translation. This perspective reviews unsupervised and supervised strategies, assesses data reliability, and advocates interpretable, standardized frameworks that integrate biological complexity, and the ideal machine learning models for the future are prospected. Formulating such a guiding framework is crucial for enabling this field to break free from the limitations of relying on experience and to shift toward a paradigm of rational design.
Wang et al. (Thu,) studied this question.