Prediction of antihypertensive activity of pyridazinone derivatives through multivariate image analysis applied to QSAR

Abstract

The antihypertensive activities of a series of pyridazinone derivatives have been modeled using an image-based approach for quantitative structure–activity relationships (MIA–QSAR). First, multivariate image analysis (MIA) descriptors were achieved from bidimensional images. Then, two regression methods were applied to correlate such descriptors with the antihypertensive activities: principal component regression (PCR) and principal component-artificial neural network (PC-ANN) with back-propagation learning algorithm, respectively. Correlation ranking procedure was used to rank the principal components and entered them into the models. The PC-ANN-based model for this series of compounds demonstrated higher predictive ability than the PCR-based model. The results supported that PCR model could predict the antihypertensive activity in pyridazinone derivatives with R2 ≥ 0.726 for training set and R2 ≥ 0.622 for validation set, whereas nonlinear method PC-ANN could predict the antihypertensive activity with R2 ≥ 0.970 for training set and with R2 ≥ 0.997 and R2 ≥ 0.982 for validation and test sets, respectively. The obtained results indicated that MIA descriptors may be useful to predict antihypertensive activity of pyridazinone derivatives.