Literature DB >> 20598401

Pharmacophore identification and bioactivity prediction for triaminotriazine derivatives by electron conformational-genetic algorithm QSAR method.

Emin Saripinar1, Nazmiye Geçen, Kader Sahin, Ersin Yanmaz.   

Abstract

The electron conformational-genetic algorithm (EC-GA) method has been employed as a 4D-QSAR approach to reveal the pharmacophore (Pha) and to predict anticancer activity in the N-morpholino triaminotriazine derivatives. The electron conformational matrices of congruity (ECMCs) identified by electronic and structural parameters are constructed from data of conformational analysis and electronic structure calculation of molecules. Comparing the matrices, electron conformational submatrix of activity (ECSA, Pha) are revealed that are common for these compounds within a minimum tolerance. To predict the theoretical activity of training and test set and to select important variables for describing the activities, genetic algorithm and non-linear least square regression methods were applied. Regression coefficients were found 0.9708 for training and 0.9520 for test set. 2010 Elsevier Masson SAS. All rights reserved.

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Year:  2010        PMID: 20598401     DOI: 10.1016/j.ejmech.2010.06.007

Source DB:  PubMed          Journal:  Eur J Med Chem        ISSN: 0223-5234            Impact factor:   6.514


  3 in total

1.  Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents.

Authors:  Hamzeh Karimi; Abbas Farmany; Hadi Noorizadeh
Journal:  Environ Monit Assess       Date:  2012-03-08       Impact factor: 2.513

2.  Application of electron conformational-genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction.

Authors:  Nazmiye Geçen; Emin Sarıpınar; Ersin Yanmaz; Kader Sahin
Journal:  J Mol Model       Date:  2011-03-31       Impact factor: 1.810

Review 3.  Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

Authors:  Andrzej Bak
Journal:  Int J Mol Sci       Date:  2021-05-14       Impact factor: 5.923

  3 in total

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