Nanosafety-data-reusability-34-datasets

Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors (2015)

• Authors: David E Jones, Hamidreza Ghandehari, Julio C Facelli
• Journal: Beilstein Journal of Nanotechnology
• Article Type: journal-article
• Dataset Type: Toxicological Datasets
• Subject: Electrical and Electronic Engineering,General Physics and Astronomy,General Materials Science
• Date: 2015-9-11
• License: http://creativecommons.org/licenses/by/2.0
• URL: http://dx.doi.org/10.3762/bjnano.6.192
• DOI: 10.3762/bjnano.6.192

Original Study 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.

Data Sample

Cytotoxicity_Concentration (mM)	Cell_Viability (%)	Molecular_Weight	Exact_Mass	Atom_Count	pI	logP	logD	Molecular_Polarizability	Refractivity	Toxic
1	100	516,6811	516,3860001	84	12,63	-6,45	-18,95	55,28	139,82	No
2	100	516,6811	516,3860001	84	12,63	-6,45	-18,95	55,28	139,82	No
5	100	516,6811	516,3860001	84	12,63	-6,45	-18,95	55,28	139,82	No
10	96	516,6811	516,3860001	84	12,63	-6,45	-18,95	55,28	139,82	No
0,1	98	1429,8467	1429,020504	228	12,47	-16,51	-36,84	150,96	383,72	No

Data Summary

Variable	Count (unique values)
Cytotoxicity_Concentration (mM)	34
Cell_Viability (%)	50
Molecular_Weight	10
Exact_Mass	10
Atom_Count	10
pI	10
logP	10
logD	10
Molecular_Polarizability	10
Refractivity	10
Toxic	2

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