Automated data analysis in Genedata Screener: From review-by-exception to AI exploration
Tuesday, December 1 at 16:00 GMT | 17:00 CET | 11:00 EST | 08:00 PST
The rapid growth of robotic throughput in drug discovery underscores the need for advanced data analysis workflows. This webinar examines the current automation gap in data analysis and explores industry trends toward closed-loop, AI-driven DMTA cycles that enable faster, more adaptive decision-making.
Focusing on Genedata Screener’s evolving capabilities, the speakers will explore the efforts made to develop review-by-exception and aim to transform manual review into fully automated, intelligent processes. The potential of AI agents to improve quality control, data validation, and result rescue will also be addressed, with the goal of accelerating drug discovery timelines.
Certificate of attendance
If you attend the live webinar, you will automatically receive a certificate of attendance, including a learning outcomes summary, for continuing education purposes.
If you view the on-demand webinar, you can request a certificate of attendance by emailing editor@selectscience.net.
Webinar details
- Cost: Free to attend
- Location: Online
- Duration: 60 minutes
Registration is required to secure your place. If you register but can’t attend live, you will receive a link to the on‑demand recording once it becomes available.
Speakers
Who should attend?
- R&D scientists: Explore how automated analysis and review-by-exception can streamline experimental data review.
- Automation scientists: See how automation can extend from laboratory processes into data analysis, QC, validation, and reporting.
- Bioinformaticians & Data scientists: Explore the role of automated data workflows and AI agents in data assessment and decision-making.
- IT & Digital transformation leaders: Gain insights into how increasingly automated and AI-driven data analysis can contribute to the transition toward closed-loop drug discovery workflows.
- R&D managers: Understand how automation can reduce data analysis bottlenecks and accelerate drug discovery workflows.
Featured products
Request Quote for All Products
What will this webinar cover?
- The role of automated data analysis in enabling closed-loop drug discovery workflows.
- How review-by-exception workflows reduce manual review through automated QC, validation, and masking.
- Different levels of automation through Co-Scientist and Auto-Scientist workflows, from targeted expert review to automated processing and publication of results.
- The potential of AI agents to improve quality control, data validation, and result rescue within automated workflows.