Fig. 2: Supervised learning descriptors derived from mass spectroscopy predict in vivo fate of PEGylated gold nanoparticles.
From: Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials
The half-life (a), spleen accumulation (b), and liver accumulation (c) of five nanoparticle sizes were applied as target labels to train an artificial neural network to map proteomic input descriptors to in vivo nanoparticle fate (d). The model generalized successfully in predicting the properties of two unknown (UK) nanoparticles for their half-life (e), spleen accumulation (f), and liver accumulation (g). n = 3, error bars indicate standard deviation. Adapted with permission from ref. 23 (copyright American Chemical Society, 2019).
