Closing the Loop Between Prediction and Reality
The Lab-Feedback AI Modeling represents the culmination of our research approach, creating a dynamic system where experimental results continuously inform and improve AI predictions.
Overview of the Lab-Feedback AI Loop
Core Components
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High-Throughput Screening: High-throughput screening of antibody-antigen interactions.
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AI Modeling (Foundation Models): Large-scale foundation models for virus evolution and antibody design that continuously improve through feedback loops.
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Continuous Model Refinement: Systematic processes for updating models based on experimental validation and performance metrics.
Feedback Cycle
Experimental Data
High-throughput experiments generate new data about viral behavior and immune responses
AI Analysis
AI systems analyze experimental results and identify patterns or discrepancies
Hypothesis Generation
New hypotheses are generated based on AI analysis of experimental data
Model Update
AI models are updated with new knowledge and refined predictions
Real-World Applications
This feedback loop is particularly powerful for responding to emerging viral threats and designing antibodies.