Lab-Feedback AI Modeling

Overview of the lab-feedback AI modeling cycle

Core Components

• High-Throughput Screening: High-throughput screening of antibody-antigen interactions.
• AI Modeling (Foundation Models): Large-scale foundation models for virus evolution and antibody design that continuously improve through feedback loops.
• Continuous Model Refinement: Systematic processes for updating models based on experimental validation and performance metrics through continual learning.

  Continual Learning (CL) in biological systems enables models to incrementally adapt to evolving data—such as new viral mutations or protein structures—without "forgetting" fundamental biophysical principles. By balancing plasticity (learning new patterns) with stability (retaining old knowledge), it allows models to stay current with dynamic biological changes. This approach avoids the prohibitive computational cost of retraining massive foundation models from scratch for every new discovery.

The Feedback Process

The lab-feedback cycle operates as a continuous loop: high-throughput experiments generate new data about viral behavior and immune responses, which AI systems analyze to identify patterns and discrepancies. Based on this analysis, new hypotheses are generated and AI models are updated with refined predictions. This iterative process enables rapid adaptation to emerging viral threats and continuous improvement in antibody design.