Moving Beyond Slow, Sequential Antibody Optimization

Moving Beyond Slow, Sequential Antibody Optimization Antibody optimization often requires balancing multiple properties, with improvements in one area sometimes compromising progress in another. This webinar explores an integrated approach that enables researchers to evaluate and prioritize antibody candidates across multiple objectives within a single workflow. Discover how large-scale experimental binding data and computational modeling can streamline optimization, reduce iterative engineering cycles, and help identify promising candidates earlier. Join our speaker for practical insights into a more holistic optimization strategy that can improve confidence in lead selection while reducing the risk of late-stage development challenges. Attend this webinar to: Understand why optimizing antibody properties through separate campaigns can undo previous gains and how to optimize multiple objectives simultaneously Learn how AlphaBind predicts affinity for antibody candidates more than 20 mutations from the parent, enabling the computational screening of millions of sequences to identify promising leads Explore how to engineer affinity, developability, and freedom-to-operate in one workflow to help reduce late-stage developmental risks Ryan Emerson, PhD Senior Vice President, Data Science A-Alpha Bio