Presented at SLAS EU 2021 Digital
Drug discovery is an expensive, long and uncertain endeavor. Following its successive industrialization during the past three decades, it is now undergoing a digital transformation, with the goals of better leveraging scientific creativity, distributing experience across teams, and preserving institutional knowledge while favorably tipping the cost-success balance of discovery. An important part of this process is data workflow automation, by the introduction and automation of digital lab workflows.
In many instances, the process driving the discovery cascade is compound screening. As such, robotic process automation has been pioneered in small molecule and—increasingly—biologics discovery. In this tutorial, we show how the corresponding data workflow is being realized and automated, enabling fast and rich assay cascades to fuel project team decisions and in silico predictions.
We will guide you through the automation of assay registration and experiment design, data capture, data processing, QC, analysis, and hit selection. We will touch on the required semantic annotation, quality assurance, standardization and experiment-adaptive business logic to produce FAIR data in the process.
The results are concise, deep bioactivity result sets, where decision-ready summaries efficiently enable the scientist to plan the next experiment, and AI-ready, fully structured and annotated multivariate data sets that can inform automated in-silico predictions. A corresponding case study will be presented.