Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
This work addresses drug discovery by enabling efficient screening of compounds for neurotoxicity, but it is incremental as it applies an existing deep learning method to a specific dataset.
The paper tackled the problem of predicting the biological activity of chemical compounds on primary neuronal cells using high-throughput imaging, achieving 99.6% accuracy in a binary classification task for distinguishing treated and untreated cells.
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-$β_{(25-35)}$.