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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Vydáno v:	Toxicology in vitro Ročník 81; s. 105347
Hlavní autoři:	Halabi, Andrés, Rincón, Elizabeth, Chamorro, Eduardo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 01.06.2022 Elsevier Science Ltd
Témata:	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
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Abstract	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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ+) or the chemical hardness (η) of the activated metabolites in aqueous solvent. •Activated metabolites and solvent information is needed for predicting carcinogenic activity of nitro and aromatic amines.•Our models predicts carcinogenic activity with over 90% accuracy accordingly with high Cohen's kappa statistic values•For activated metabolites in the aqueous solvent phase, the electron-accepting chemical potential and hardness are essential.
AbstractList	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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ ) or the chemical hardness (η) of the activated metabolites in aqueous solvent. 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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ+) or the chemical hardness (η) of the activated metabolites in aqueous solvent.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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ+) or the chemical hardness (η) of the activated metabolites in aqueous solvent. 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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ+) or the chemical hardness (η) of the activated metabolites in aqueous solvent. 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 calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models. We have obtained eight decision tree models that presented an accuracy of over 90% by using either Gázquez-Vela chemical potential (μ+) or the chemical hardness (η) of the activated metabolites in aqueous solvent. •Activated metabolites and solvent information is needed for predicting carcinogenic activity of nitro and aromatic amines.•Our models predicts carcinogenic activity with over 90% accuracy accordingly with high Cohen's kappa statistic values•For activated metabolites in the aqueous solvent phase, the electron-accepting chemical potential and hardness are essential.
ArticleNumber	105347
Author	Chamorro, Eduardo Rincón, Elizabeth Halabi, Andrés
Author_xml	– sequence: 1 givenname: Andrés surname: Halabi fullname: Halabi, Andrés email: andres.halabi@alumnos.uach.cl organization: Facultad de Ciencias, Instituto de Ciencias Químicas, Universidad Austral de Chile, Independencia 631, Valdivia 5090000, Chile – sequence: 2 givenname: Elizabeth surname: Rincón fullname: Rincón, Elizabeth email: elizabethrincon@uach.cl organization: Facultad de Ciencias, Instituto de Ciencias Químicas, Universidad Austral de Chile, Independencia 631, Valdivia 5090000, Chile – sequence: 3 givenname: Eduardo surname: Chamorro fullname: Chamorro, Eduardo email: echamorro@unab.cl organization: Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Universidad Andrés Bello, Avenida Republica 275, Santiago 8370146, Chile
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Cites_doi	10.1002/1097-0142(19810515)47:10<2327::AID-CNCR2820471003>3.0.CO;2-Z 10.1021/cr040109f 10.1021/cr9901079 10.1021/jp065459f 10.1002/jat.2550150305 10.3390/ijms12085098 10.1021/cr078014b 10.1080/1062936X.2018.1505778 10.1016/j.tiv.2019.03.017 10.1186/1758-2946-6-10 10.1021/cr990029p 10.1021/jo800572a 10.1021/cr100149p 10.1002/jat.2980 10.3390/ijerph13111141 10.1021/ja983494x 10.1093/carcin/18.5.1085 10.1128/MMBR.00006-10 10.1093/nar/gkt1109 10.1021/cr9904009 10.1007/s00214-020-2546-7 10.1021/cr100222q 10.1002/qua.20307 10.1093/carcin/16.9.2037 10.1023/B:JCAM.0000035186.90683.f2 10.1016/j.mrrev.2004.08.002 10.1021/jm00349a001
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Keywords	Aromatic amines SPAARC WEKA Carcinogenic activity DFT QSAR Nitroaromatics Machine learning RandomTree Solvent Effects JCHAIDStar Activated Metabolites Carcinogenic potency J48Consolidated
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Snippet	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...
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SubjectTerms	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
Title	Machine learning predictive classification models for the carcinogenic activity of activated metabolites derived from aromatic amines and nitroaromatics
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