NISCAIR Online Periodicals Repository

NISCAIR ONLINE PERIODICALS REPOSITORY (NOPR)  >
NISCAIR PUBLICATIONS >
Research Journals >
Indian Journal of Chemistry -Section B (IJC-B) >
IJC-B Vol.46B [2007] >
IJC-B Vol.46B(03) [March 2007] >


Title: Prediction of acidity constant for substituted acetic acids in water using artificial neural networks
Authors: Habibi-Yangjeh, Aziz
Danandeh-Jenagharad, Mohammad
Keywords: Quantitative Structure-Activity Relationship
Artificial neural network
Acidity constant
Theoretical descriptors
Substituted acetic acids
Issue Date: Mar-2007
Publisher: CSIR
IPC CodeInt.Cl.⁸ C07C
Abstract: Linear and non-linear quantitative structure-activity relationships have been successfully developed for the modelling and prediction of acidity constant (pKa) of 87 substituted acetic acids with diverse chemical structures. The descriptors appearing in the multi-parameter linear regression (MLR) model are considered as inputs for developing the back-propagation artificial neural network (BP-ANN). ANN model is constructed using two molecular descriptors; the most positive charge of acidic hydrogen atom (q⁺) and most negative charge of the carboxylic oxygen atom (q⁻) as inputs and its output is pKa. It has been found that properly selected and trained neural network with 53 substituted acetic acids could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network has been applied for prediction pKa values of 17 compounds in the prediction set. Mean percentage deviation (MPD) for prediction set using the MLR and ANN models are 9.135 and 1.362, respectively. These improvements are due to the fact that the pKa of substituted acetic acids demonstrates non-linear correlations with the molecular descriptors.
Page(s): 478-487
ISSN: 0376-4699
Source:IJC-B Vol.46B(03) [March 2007]

Files in This Item:

File Description SizeFormat
IJCB 46B(3) (2007) 478-487.pdf134.99 kBAdobe PDFView/Open
 Current Page Visits: 630 
Recommend this item

 

National Knowledge Resources Consortium |  NISCAIR Website |  Contact us |  Feedback

Disclaimer: NISCAIR assumes no responsibility for the statements and opinions advanced by contributors. The editorial staff in its work of examining papers received for publication is helped, in an honorary capacity, by many distinguished engineers and scientists.

CC License Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India

Copyright © 2012 The Council of Scientific and Industrial Research, New Delhi. All rights reserved.

Powered by DSpace Copyright © 2002-2007 MIT and Hewlett-Packard | Compliant to OAI-PMH V 2.0

Home Page Total Visits: 509878 since 06-Feb-2009  Last updated on 11-Apr-2014Webmaster: nopr@niscair.res.in