Machine learning predictive classification models for the carcinogenic activity of activated metabolites derived from aromatic amines and nitroaromatics

A 3D-QSAR study based on DFT descriptors and machine learning calculations is presented in this work. Our goal has been to build predictive models for classifying the carcinogenic activity of a set of aromatic amines (AA) and nitroaromatic (NA) compounds. As the main result, we stress that calculati...

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Bibliographic Details
Published in:	Toxicology in vitro Vol. 81; p. 105347
Main Authors:	Halabi, Andrés, Rincón, Elizabeth, Chamorro, Eduardo
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01.06.2022 Elsevier Science Ltd
Subjects:	Activated Metabolites Amines Aromatic amines Carcinogenic activity Carcinogenic potency Carcinogens Chemical potential Classification Decision trees DFT J48Consolidated JCHAIDStar Learning algorithms Machine learning Metabolites Nitroaromatics Prediction models QSAR RandomTree Solvent Effects Solvents SPAARC Structure-activity relationships WEKA Aromatic amines SPAARC WEKA Carcinogenic activity DFT QSAR Nitroaromatics Machine learning RandomTree Solvent Effects JCHAIDStar Activated Metabolites Carcinogenic potency J48Consolidated
ISSN:	0887-2333, 1879-3177, 1879-3177
Online Access:	Get full text
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