Computational screening and qsar study of bastadins as acat1 inhibitors

In the search for new and effective anticancer agents, we performed a QSAR study on a series of sixteen bastadins to evaluate their potential as ACAT1 inhibitors and predict their antiangiogenic activity. Our goal was to establish a clear correlation between their biological responses and a set of molecular descriptors, applying principal component analysis (PCA), multiple linear regression (MLR), multiple nonlinear regression (MNLR), and an artificial neural network (ANN). Among the models generated, the best MLR and MNLR approaches achieved determination coefficients (R²) of 0.71 and 0.91. To further assess their reliability, we performed an external validation on a test set of three compounds, confirmed their predictive accuracy, and yielded R² test values of 0.70 and 0.83, respectively. Furthermore, the ANN model, built with a 4-4-1 architecture, showed excellent performance, achieving a correlation coefficient of 0.96 with leave-one-out cross-validation coefficients (Q²) of 0.79. These results indicate that the selected descriptors and calculated parameters are sufficient to reliably predict the biological activity of bastadins as ACAT1 inhibitors, providing a solid basis for the computer-aided design of novel anticancer agents.