In-Vitro Antibody Discovery Masterclass: Mining Display Libraries for Top Leads
In today’s fast-moving therapeutic landscape, antibody discovery teams need to make lead decisions earlier without being slowed down by fragmented tools or complex, custom bioinformatics pipelines.
This masterclass is designed for scientists and discovery leaders who want to go beyond theory and learn practical, best-in-class approaches to prioritizing antibody leads and de-risking programs early. Through real-world examples and end-to-end workflows, you’ll learn how top pharma and biotech teams move from raw sequencing data to high-quality antibody candidates—faster and with greater confidence.
What you’ll learn:
Clustering and motif-level analysis: How to identify unique binding mechanisms by uncovering emergent clones and structural motifs across successive display selection rounds
Tracking panning dynamics: Techniques to monitor clone frequency and sequence liabilities over panning rounds
Decomposing selection dynamics: See how we break down selection into three independent axes—Enrichment Quality (distinguishing Stable Binders from Parasites by trajectory), Binding Specificity (via parallel panning data), and Cross-Antigen Profiles
Developability-based prioritization: Approaches to automatically flagging structural risks and sequence liabilities to de-risk synthetic or immune library outputs early in the funnel
No-code lead selection: Real-world visual workflows to efficiently sort, filter, and isolate best-in-class antibody leads from thousands of raw display sequences without writing a single line of code
Workflow optimization: Insights into how top pharma and biotech teams streamline their in vitro data analysis pipelines to accelerate candidate selection
Who should attend:
Biologists, immunologists, scientists working on in-vitro antibody discovery
Bioinformaticians and computational biologists seeking to reduce bottlenecks in their analysis
Project leads and decision‑makers in biotech/pharma who want to streamline the antibody‑lead pipeline, reduce risk and time‑to‑lead