  Understanding the morphological changes of primary neuronal cells induced by
chemical compounds is essential for drug discovery. Using the data from a
single high-throughput imaging assay, a classification model for predicting the
biological activity of candidate compounds was introduced. The image
recognition model which is based on deep convolutional neural network (CNN)
architecture with residual connections achieved accuracy of 99.6$\%$ on a
binary classification task of distinguishing untreated and treated rodent
primary neuronal cells with Amyloid-$\beta_{(25-35)}$.
