Objectives: Present communication deals with two- and three-dimensional (2D and 3D) QSAR studies of twenty saponin analogue for antifungal activity against Candida albicans. Methods: The 2D-QSAR model for the prediction was obtained by applying Multiple Linear Regression (MLR) method, giving r2 = 0.8551 and q2 = 0.7717 and Partial Least Squares (PLS) method, giving r2 = 0.8551 and q2 = 0.7717. 3D-QSAR study was performed using the stepwise variable selection k-nearest neighbour molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Two different kNN-MFA methods (SA and GA) were used for the building of 3D-QSAR models. Results: The best model shows interesting result in terms of internal (q2 > 0.62) and external (predictive r2 > 0.52) predictivity for training and test set. Conclusion: Thus, QSAR models showed that hydrophobic and electrostatic effects dominantly determine the binding affinities. Hence the QSAR models proposed in this work would be further useful for development of new antifungal agents from medicinal plants and can help in the design of novel potent molecule.
Key words: Saponin, QSAR, MLR, PLS, Candida.