title: Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: David E
authors.initials: DE
authors.lastname: Jones
authors.affiliation: Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA ; Department of Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA, ; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Ghandehari
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA ; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
authors.forename: Julio C
authors.initials: JC
authors.lastname: Facelli
keywords: data mining; machine learning; molecular descriptors; poly(amido amine) dendrimers (PAMAM)