DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING

DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING

Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR) has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Aus...

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Main Authors: Ihsanul Arief, Ria Armunanto, Bambang Setiaji, Muhammad Fachrie
Format: Article
Language:English
Published: Jenderal Soedirman University 2016-11-01
Series:Molekul
Online Access:http://ojs.jmolekul.com/ojs/index.php/jm/article/view/242
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spelling doaj-065fc5e6bf334cf7822aed30064c3b1e2020-11-24T21:55:50ZengJenderal Soedirman UniversityMolekul1907-97612503-03102016-11-0111215816710.20884/1.jm.2016.11.2.242198DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLINGIhsanul Arief0Ria Armunanto1Bambang Setiaji2Muhammad Fachrie3Akademi Farmasi Yarsi PontianakAustrian-Indonesian Centre (AIC) for Computational Chemistry, Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah MadaAustrian-Indonesian Centre (AIC) for Computational Chemistry, Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah MadaInformatics Engineering Major, Faculty of Business and Information Technology, Universitas Teknologi YogyakartaStudy on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR) has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1), Parameterized Model 3 (PM3), Hartree-Fock (HF), and density functional theory (DFT) methods.  Artificial neural networks (ANN) analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives.  The result shows that compound N1-(4′-Cyanophenyl)-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy)-4-dimethylether benzene-1,2-diamine) is the best-proposed compound with cytotoxicity value (CC50) of 93.037 μM.http://ojs.jmolekul.com/ojs/index.php/jm/article/view/242
collection DOAJ
language English
format Article
sources DOAJ
author Ihsanul Arief
Ria Armunanto
Bambang Setiaji
Muhammad Fachrie
spellingShingle Ihsanul Arief
Ria Armunanto
Bambang Setiaji
Muhammad Fachrie
DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
Molekul
author_facet Ihsanul Arief
Ria Armunanto
Bambang Setiaji
Muhammad Fachrie
author_sort Ihsanul Arief
title DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
title_short DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
title_full DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
title_fullStr DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
title_full_unstemmed DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING
title_sort design of low cytotoxicity diarylaniline derivatives based on qsar results: an application of artificial neural network modelling
publisher Jenderal Soedirman University
series Molekul
issn 1907-9761
2503-0310
publishDate 2016-11-01
description Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR) has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1), Parameterized Model 3 (PM3), Hartree-Fock (HF), and density functional theory (DFT) methods.  Artificial neural networks (ANN) analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives.  The result shows that compound N1-(4′-Cyanophenyl)-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy)-4-dimethylether benzene-1,2-diamine) is the best-proposed compound with cytotoxicity value (CC50) of 93.037 μM.
url http://ojs.jmolekul.com/ojs/index.php/jm/article/view/242
work_keys_str_mv AT ihsanularief designoflowcytotoxicitydiarylanilinederivativesbasedonqsarresultsanapplicationofartificialneuralnetworkmodelling
AT riaarmunanto designoflowcytotoxicitydiarylanilinederivativesbasedonqsarresultsanapplicationofartificialneuralnetworkmodelling
AT bambangsetiaji designoflowcytotoxicitydiarylanilinederivativesbasedonqsarresultsanapplicationofartificialneuralnetworkmodelling
AT muhammadfachrie designoflowcytotoxicitydiarylanilinederivativesbasedonqsarresultsanapplicationofartificialneuralnetworkmodelling
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