Evaluation of SAR for Amphotericin B Derivatives by Artificial Neural Network

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.