Literature DB >> 19680771

QSAR modelling of the toxicity to Tetrahymena pyriformis by balance of correlations.

A A Toropov1, A P Toropova, E Benfenati, A Manganaro.   

Abstract

Balance of correlations is an approach to build up quantitative structure-property/activity relationships (QSPR/QSAR). This approach is based on a split into the subtraining, calibration and test sets instead of classic split into training and test sets. The function of the calibration set is the preliminary check up of the model. In other words, the calibration set is like a preliminary test set. Computational experiments (with the Monte Carlo method) have shown that the statistical characteristics of the prediction for the toxicity to Tetrahymena pyriformis (the 50% growth inhibition concentration, IGC(50)) based on the balance of correlations are better than the statistical characteristics of the prediction based on the classic scheme.

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Year:  2009        PMID: 19680771     DOI: 10.1007/s11030-009-9186-0

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  16 in total

1.  3D-QSAR illusions.

Authors:  Arthur M Doweyko
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

2.  LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities.

Authors:  David Vidal; Michael Thormann; Miquel Pons
Journal:  J Chem Inf Model       Date:  2005 Mar-Apr       Impact factor: 4.956

3.  A novel search engine for virtual screening of very large databases.

Authors:  David Vidal; Michael Thormann; Miquel Pons
Journal:  J Chem Inf Model       Date:  2006 Mar-Apr       Impact factor: 4.956

4.  SMILES in QSPR/QSAR Modeling: results and perspectives.

Authors:  Andrey A Toropov; Emilio Benfenati
Journal:  Curr Drug Discov Technol       Date:  2007-08

5.  The trouble with QSAR (or how I learned to stop worrying and embrace fallacy).

Authors:  Stephen R Johnson
Journal:  J Chem Inf Model       Date:  2007-12-28       Impact factor: 4.956

6.  QSAR modelling for mutagenic potency of heteroaromatic amines by optimal SMILES-based descriptors.

Authors:  Andrey A Toropov; Alla P Toropova; Emilio Benfenati
Journal:  Chem Biol Drug Des       Date:  2009-03       Impact factor: 2.817

7.  New QSPR study for the prediction of aqueous solubility of drug-like compounds.

Authors:  Pablo R Duchowicz; Alan Talevi; Luis E Bruno-Blanch; Eduardo A Castro
Journal:  Bioorg Med Chem       Date:  2008-07-29       Impact factor: 3.641

8.  Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

Authors:  Mark T D Cronin; Aynur O Aptula; Judith C Duffy; Tatiana I Netzeva; Philip H Rowe; Iva V Valkova; T Wayne Schultz
Journal:  Chemosphere       Date:  2002-12       Impact factor: 7.086

9.  A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas.

Authors:  Antreas Afantitis; Georgia Melagraki; Haralambos Sarimveis; Olga Igglessi-Markopoulou; George Kollias
Journal:  Eur J Med Chem       Date:  2008-07-10       Impact factor: 6.514

10.  QSAR: dead or alive?

Authors:  Arthur M Doweyko
Journal:  J Comput Aided Mol Des       Date:  2008-01-09       Impact factor: 4.179

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