Closed Loop Antibody Engineering: Combining High-Throughput Campaigns with ML for Multi-Objective Design
- Closed loop to static screening: Antibody discovery becomes dramatically more efficient when HTx data and ML are integrated into an iterative, learning system, not a one-off screening pipeline
- Multi-objective is the real bottleneck: Optimizing affinity alone is solved; the challenge (and opportunity) is balancing affinity, specificity, and developability simultaneously, and this requires new modeling and data strategies
- Data quality beats model complexity: The biggest gains come from designing the right experiments (diverse, informative, comparable), not from using more complex ML models