Tropical Journal of Pharmaceutical Research

Original Research Article | OPEN ACCESS

Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network

S . Sardari¹ , M . Dezfulian²

¹Dept of Biotechnology, Pasteur Institute, Tehran, Iran; ²Dept of Biology, Ahvaz University, Ahvaz, Iran.

For correspondence:- S Sardari Email: ssardari@hotmail.com

Published: 17 December 2005

Citation: Sardari S., Dezfulian M.. Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network. Trop J Pharm Res 2005; 4(2):517-521 doi: 10.4314/tjpr.v4i2.7

© 2005 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..

Abstract

Aim: 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.
Method: 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.
Results: 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.
Conclusion: 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.

Keywords: Amphotericin B, SAR, Artificial Neural Network.