A deep-learning based analysis framework for ultra-high throughput screening time-series data

Analysis of ultra-high-throughput screening data sets is a highly critical step in drug discovery campaigns. Due to various environmental and experimental error sources fast and reliable identification of possible candidate compounds is challenging. In this work, we introduce a novel deep-learning based analysis framework to analyze uHTS time-series data sets. Our framework is based on two independent deep-learning models. A deep-learning regression model reduces temporal and spatial signal variation across multitier plates caused by systematic and random errors and a separate variational autoencoder model is used for dimensionality reduction. In contrast to classical evaluation methods our approach is capable to derive lower dimensional representations of time-series signals without a-priori knowledge of the data generating mechanism. We tested our analysis framework on an experimental uHTS data set and identified two distinct classes of substances in the screened library which could be attributed to two biological modes of action. Selected substances belonging to both modes of action were successfully validated in a secondary screening experiment.