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QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm.

Abstract：

:A linear and non-linear quantitative structure-activity relationship (QSAR) study is presented for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 derivatives of 2,3-dihydro-1,3-dioxo-1H-isoindole-5-carboxylic acid, furanyl-1,3-thiazol-2-yl and benzoxazol-5-yl acetic acids is used in this study. Among a large number of descriptors, four parameters classified as physico-chemical, topological and electronic indices are chosen using stepwise multiple regression technique. The artificial neural networks (ANNs) model shows superiority over the multiple linear regressions (MLR) by accounting 87.9% of the variances of antiviral potency of the heparanase inhibitors. This paper focuses on investigating the role of weight update functions in developing ANNs. Levenberg-Marquardt (L-M) algorithm shows a better performance compared with basic back propagation (BBP) and conjugate gradient (CG) algorithms. The accuracy of 4-3-1 L-M ANN model was illustrated using leave-one-out (LOO), leave-multiple-out (LMO) cross-validations and Y-randomization. The mean effect of descriptors and sensitivity analysis show that log P is the most important parameter affecting the inhibitory behavior of the molecules.

journal_name

Eur J Med Chem

journal_title

European journal of medicinal chemistry

authors

Jalali-Heravi M,Asadollahi-Baboli M,Shahbazikhah P

doi

10.1016/j.ejmech.2007.04.014

subject

Has Abstract

pub_date

2008-03-01 00:00:00

pages

548-56

issue

3

eissn

0223-5234

issn

1768-3254

pii

S0223-5234(07)00200-0

journal_volume

43

pub_type

杂志文章

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