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STORM Therapeutics: From Lead to Pre-Candidate Nomination in 18 Months
STORM Therapeutics: From Lead to Pre-Candidate Nomination in 18 Months
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AI Meets Expertise: A hybrid Workflow For Modern Target ID | QIAGEN & Sygnature
In drug discovery, generating targets is no longer the challenge. The real question is how to identify the few worth investing months of research and significant resources to pursue. Hear expert perspectives on how AI, pathway analysis and scientific expertise are shaping modern target identification.
Prefer to read rather than watch?
Read our blog which shares the key insights from the discussion.
When AI Produces Hundreds of Answers: Turning Insight into Action
AI-driven workflows can identify hundreds of potential therapeutic targets in minutes.
But quantity doesn’t equal confidence.
A target may appear statistically compelling while hiding critical biological risks, tissue-specific safety concerns or clinical limitations that only emerge through deeper investigation.
The question isn’t: « Can AI find targets? »
It’s: « How do you identify the targets worth pursuing and eliminate those that aren’t? »
In this 45-minute fireside chat, experts from Sygnature Discovery and QIAGEN Digital Insights share their perspectives on the evolving role of AI in target identification. Drawing on their combined expertise in bioinformatics, pathway analysis, translational biology, and drug discovery, the panel explores the practical challenges of moving from AI-generated target lists to confident, evidence-based decisions.
Through real-world examples and discussion, viewers will gain insights into how scientific expertise, experimental validation, and data-driven analysis can be combined to improve target prioritization, reduce risk, and support more effective drug discovery programs.
Register to watch this webinar on demand
In This Webinar You’ll Learn
Beyond AI Rankings
Why highly-ranked targets can fail when biological context is missing.
Human Expertise in the Loop
How multidisciplinary teams challenge, validate and refine AI-generated insights.
Making AI Explainable
What scientists need to see before committing to experimental validation.
From Targets to Pathways
Why understanding pathway biology can reveal opportunities AI alone may miss.
Fail Faster
How leading teams design workflows that identify risks early and avoid costly dead ends.
Key Discussion Topics
- How AI-generated target lists are evaluated in real-world discovery programs
- Why biological context remains critical when prioritizing targets
- The role of pathway analysis in understanding disease mechanisms
- Balancing AI efficiency with expert scientific judgment
- Approaches for identifying safety, tractability and translational risks earlier
- What « human-in-the-loop » and « lab-in-the-loop » workflows look like in practice
« Don’t design a program where you are likely to get success. Design a program that will flag failures. »
Saurav Saha, Sygnature Discovery
Meet The Experts
Ready to Explore the Full Discussion?
