Tropical Journal of Pharmaceutical Research
Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, Nigeria
Vol. 4, No. 2, 2005, pp. 517-521
Bioline Code: pr05014
Full paper language: English
Document type: Research Article
Document available free of charge
Tropical Journal of Pharmaceutical Research, Vol. 4, No. 2, 2005, pp. 517-521
© Copyright 2005. Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, Nigeria.
Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network|
S Sardari and M Dezfulian
This study was designed to investigate the role of several descriptive structure-activity features in the antifungal drug, amphotericin B and analyze them by artificial neural networks.
Artificial neural networks (ANN) based on the back-propagation algorithm were applied to a structure-activity relationship (SAR) study for 17 amphotericin B derivatives with antifungal and membrane directed activity. A series of modified ANN architectures was made and the best result provided the ANN model for prediction of antifungal activity using the structural and biologic property descriptors.
The best architecture, in terms of cycles of calculation was 12-15-2. Among the most important factors were biological descriptors that correlated best with the model produced by ANN. Among the chemical and structural descriptors, positive charge on Y substitution was found to be the most important, followed by lack of availability of free carboxyl and parachor.
This model is found to be useful to elucidate the structural requirements for the antifungal activity and can be applied in the design and activity prediction of the new amphotericin B derivatives.
Amphotericin B, SAR, Artificial Neural Network.
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