Building the AI-Ready Foundation: Practical Computational Workflows to Find and De-Risk Antibody Leads
Every AI model applied to antibody discovery is only as good as the data underneath it. While the promise of AI in antibody discovery is everywhere, from developability to affinity to lead selection, AI models require a robust, structured data foundation.
This webinar is designed for wet-lab scientists, computational biologists, and discovery leaders who want to go beyond theory and learn practical, best-in-class approaches to prioritizing antibody leads and preparing their pipelines for future machine learning integration.
What you’ll learn:
Repertoire Characterization for AI: Verify your panning campaign actually captures the high-quality, reproducible data that predictive models require.
Smart Antibody Clustering: Discover unique binding mechanisms by grouping redundant sequences, naturally structuring your data for downstream machine learning.
Decoding Selection Dynamics: Break down enrichment trajectories, binding specificity, and cross-antigen profiles to distinguish stable, true binders from non-specific "parasites."
In Silico De-risking: Automatically flag sequence liabilities early to avoid synthesizing dead ends and keep your AI training data clean.
Building an AI-Ready Loop: Map wet-lab assay results back to sequence lineages to optimize variants, streamline team workflows, and create a reusable foundation of training data.
Who should attend:
Biologists 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