QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm.
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.
Jalali-Heravi
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Department of Chemistry, Sharif University of Technology, Azadi Avenue, P.O. Box 11365-9516, Tehran, Iran. jalali@sharif.edu
Asadollahi-Baboli
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Shahbazikhah
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D000465:Algorithms; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D005966:Glucuronidase / Q000037:antagonists & inhibitors* / Q000378:metabolism; D008954:Models, Biological; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012680:Sensitivity and Specificity; D000099175:Heparanase
D004791:Enzyme Inhibitors; D005966:Glucuronidase; D000099175:Heparanase