Literature DB >> 16782350

A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes.

Antreas Afantitis1, Georgia Melagraki, Haralambos Sarimveis, Panayiotis A Koutentis, John Markopoulos, Olga Igglessi-Markopoulou.   

Abstract

A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and predicting induction of apoptosis by 4-aryl-4H-chromenes. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 43 recently discovered 4-aryl-4H-chromenes. Among the 61 different physicochemical, topological, and structural descriptors that were considered as inputs to the model, seven variables were selected using the elimination selection-stepwise regression method (ES-SWR). The physical meaning of each descriptor is discussed. The accuracy of the proposed MLR model is illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.

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Year:  2006        PMID: 16782350     DOI: 10.1016/j.bmc.2006.05.061

Source DB:  PubMed          Journal:  Bioorg Med Chem        ISSN: 0968-0896            Impact factor:   3.641


  9 in total

1.  Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays.

Authors:  Rajarshi Guha; Stephan C Schürer
Journal:  J Comput Aided Mol Des       Date:  2008-02-19       Impact factor: 3.686

2.  Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors.

Authors:  Georgia Melagraki; Antreas Afantitis; Haralambos Sarimveis; Panayiotis A Koutentis; George Kollias; Olga Igglessi-Markopoulou
Journal:  Mol Divers       Date:  2009-02-10       Impact factor: 2.943

3.  Exploiting PubChem for Virtual Screening.

Authors:  Xiang-Qun Xie
Journal:  Expert Opin Drug Discov       Date:  2010-12       Impact factor: 6.098

4.  2D Quantitative structure-property relationship study of mycotoxins by multiple linear regression and support vector machine.

Authors:  Roya Khosrokhavar; Jahan Bakhsh Ghasemi; Fereshteh Shiri
Journal:  Int J Mol Sci       Date:  2010-08-31       Impact factor: 5.923

5.  4-[4-(Diethyl-amino)-phen-yl]-N-methyl-3-nitro-4H-chromen-2-amine.

Authors:  J Muthukumaran; A Parthiban; P Manivel; H Surya Prakash Rao; R Krishna
Journal:  Acta Crystallogr Sect E Struct Rep Online       Date:  2011-05-14

6.  rac-Ethyl 2-hy-droxy-2,7,7-trimethyl-4-(4-nitro-phen-yl)-5-oxo-3,4,5,6,7,8-hexa-hydro-2H-chromene-3-carboxyl-ate.

Authors:  Abel M Maharramov; Arif I Ismiev; Bahruz A Rashidov; Rizvan K Askerov; Konstantin A Potekhin
Journal:  Acta Crystallogr Sect E Struct Rep Online       Date:  2012-12-15

7.  In Silico Screening of IL-1β Production Inhibitors Using Chemometric Tools.

Authors:  Amirhossein Sakhteman; Najmeh Edraki; Bahram Hemmateenejad; Ramin Miri; Alireza Foroumadi; Abbas Shafiee; Mehdi Khoshneviszadeh
Journal:  Iran J Pharm Res       Date:  2017       Impact factor: 1.696

8.  In Silico SAR Studies of HIV-1 Inhibitors.

Authors:  Ismail Hdoufane; Imane Bjij; Mahmoud Soliman; Alia Tadjer; Didier Villemin; Jane Bogdanov; Driss Cherqaoui
Journal:  Pharmaceuticals (Basel)       Date:  2018-07-13

9.  Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method.

Authors:  Haixia Lu; Wanqiang Liu; Fan Yang; Hu Zhou; Fengping Liu; Hua Yuan; Guanfan Chen; Yinchun Jiao
Journal:  ACS Omega       Date:  2020-04-08
  9 in total

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