Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network

S Sardari*¹ and M Dezfulian²

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

 

Tropical Journal of Pharmaceutical Research 2005; 4(2): 517-521

ISSN: 1596-5996

 

Abstract

 

Purpose: 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.

Key words: Amphotericin B, SAR, Artificial Neural Network.

To whom correspondence should be addressed: Email:   ssardari@hotmail.com