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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.
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
Senior Scientist 2
Computational Sciences and Informatics
Sygnature Discovery
Dr Saurav Saha specialises in bioinformatics and computational biology, with a focus on enabling data-driven target discovery. He leads bioinformatics activities within cross-functional teams, developing and integrating scalable analytical workflows that support drug discovery programs. Saurav also plays a key role in mentoring scientists and growing bioinformatics capability, combining technical expertise with a collaborative approach to advancing computational strategies in research.
Senior Principal Scientist
Bioscience
Sygnature Discovery
Dr Daniel Bakowski has over seven years of experience at Sygnature Discovery, where he leads projects across target identification, validation, and early-stage drug discovery. With a strong background in molecular and cellular biology, pharmacology, and biochemistry, he has extensive expertise in assay development, high-throughput screening, and lead optimisation. Daniel is a seasoned project leader, known for driving innovative approaches in complex discovery programs within the biotechnology and pharmaceutical sectors.
Senior Global Product Marketing Manager
QIAGEN
Iman Bhattacharya leads marketing initiatives across clinical genomics, AI-driven decision support, and data science platforms. His work spans oncology, hereditary disease, and drug discovery, helping translate complex technologies into impactful solutions for healthcare and life sciences. Iman collaborates closely with product, sales, and ecosystem partners to drive go-to-market strategy and advance innovation in precision medicine.
Senior Manager, QDI Field Application Scientist
QIAGEN
Olivia Alder supports researchers in extracting meaningful insights from complex omics data. With a strong focus on the discovery portfolio, she has extensive experience guiding scientific teams in data interpretation and application. A passionate advocate for clean, reliable data, Olivia helps bridge the gap between advanced technologies and real-world research outcomes.
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