Literature DB >> 16169734

Comparative QSAR study of phenol derivatives with the help of density functional theory.

F A Pasha1, H K Srivastava, P P Singh.   

Abstract

Quantum chemical reactivity descriptors based QSAR study of 50 phenol derivatives is presented in this paper. Four different methods have been employed to certify the reliability of QSAR study. The molecular weight, hardness, chemical potential, total energy, and electrophilicity index provide valuable information and have a significant role in the assessment of the toxicity of phenols. The first model has been drawn up with the help of AM1 calculations and in this model the correlation coefficient r2 is 0.88 and the cross-validation coefficient r(cv)2 is 0.78. Second and third models have been designed with the PM3 and PM5 calculations, respectively. The values of correlation coefficient r2 and cross-validation coefficient r(cv)2 in the second case are 0.85 and .070, while in the third case they are 0.85 and 0.71. Finally, the DFT calculations have been made for the same series of compounds by using a B88-PW91 GGA energy functional with the DZVP basis set. The DFT models have a higher predictive power than AM1, PM3, and PM5 methods, and the reliability of this model is clear from its correlation coefficient r2 0.91 and cross-validation coefficient r(cv)2 0.88. This study is also helpful in determining the effect of any particular phenol derivative of this series over Tetrahymena pyriformis.

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Year:  2005        PMID: 16169734     DOI: 10.1016/j.bmc.2005.07.064

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


  6 in total

1.  Hologram and 3D-quantitative structure toxicity relationship studies of azo dyes.

Authors:  F A Pasha; Muhammad Muddassar; Hwan Won Chung; Seung Joo Cho; Hoon Cho
Journal:  J Mol Model       Date:  2008-02-07       Impact factor: 1.810

2.  A model of atomic compressibility and its application in QSAR domain for toxicological property prediction.

Authors:  Hiteshi Tandon; Tanmoy Chakraborty; Vandana Suhag
Journal:  J Mol Model       Date:  2019-09-06       Impact factor: 1.810

3.  The use of quantum-chemical descriptors for predicting the photoinduced toxicity of PAHs.

Authors:  Jabir H Al-Fahemi
Journal:  J Mol Model       Date:  2012-04-17       Impact factor: 1.810

4.  Virtual screening of PEBP1 inhibitors by combining 2D/3D-QSAR analysis, hologram QSAR, homology modeling, molecular docking analysis, and molecular dynamic simulations.

Authors:  Mourad Stitou; Hamid Toufik; Taoufik Akabli; Fatima Lamchouri
Journal:  J Mol Model       Date:  2022-05-12       Impact factor: 1.810

5.  Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors.

Authors:  Changho Jhin; Keum Taek Hwang
Journal:  Int J Mol Sci       Date:  2014-08-22       Impact factor: 5.923

6.  Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment.

Authors:  Piers A Townsend; Elliot H E Farrar; Matthew N Grayson
Journal:  ACS Omega       Date:  2022-07-21
  6 in total

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