Weakly-Supervised Drug Efficiency Estimation with Confidence Score: Application to COVID-19 Drug Discovery

The COVID-19 pandemic has prompted a surge in drug repurposing studies. However, many promising hits identified by modern neural networks failed in the preclinical research, which has raised concerns about the reliability of current drug discovery methods. Among studies that explore the therapeutic potential of drugs for COVID-19 treatment is RxRx19a. Its dataset was derived from High Throughput Screening (HTS) experiments conducted by the Recursion biotechnology company. Prior research on hit discovery using this dataset involved learning healthy and infected cells' morphological features and utilizing this knowledge to estimate contaminated drugged cells' scores. Nevertheless, models have never seen drugged cells during training, so these cells' phenotypic features are out of their trained distribution. That being said, model estimations for treatment samples are not trusted in these methods and can lead to false positives. This work offers a first-in-field weakly-supervised drug efficiency estimation pipeline that utilizes the mixup methodology with a confidence score for its predictions. We applied our method to the RxRx19a dataset and showed that consensus between top hits predicted on different representation spaces increases using our confidence method. Further, we demonstrate that our pipeline is robust, stable, and sensitive to drug toxicity.