Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors - Jones, David E.; Ghandehari, Hamidreza; Facelli, Julio C. (Beilstein Journal of Nanotechnology, 2015)
The use of data mining techniques in the field of nanomedicine has been very limited. In this paper we demonstrate that data mining techniques can be used for the development of predictive models of the cytotoxicity of poly(amido amine) (PAMAM) dendrimers using their chemical and structural properties. We present predictive models developed using 103 PAMAM dendrimer cytotoxicity values that were extracted from twelve cancer nanomedicine journal articles. The results indicate that data mining and machine learning can be effectively used to predict the cytotoxicity of PAMAM dendrimers on Caco-2 cells.
Keywords: data mining; machine learning; molecular descriptors; poly(amido amine) dendrimers (PAMAM)