AI/ML models are only as powerful as the experimental wet-lab insights that are used as input data to generate feedback. This workshop explores how lab automation, assay standardization, and closed‑loop experimentation are transforming biologic therapeutic discovery by enabling continuous, self‑improving AI workflows.

Participants will explore:

• The role of lab automation in enabling AI‑driven protein discovery at scale
• How to design lab workflows that generate high‑quality, AI‑usable experimental data rather than isolated results
• How to integrate automated wet‑lab experimentation with in silico prediction pipelines to create continuous lab‑in‑the‑loop learning cycles
• Where lab automation delivers the greatest ROI today, such as, binder screening and affinity maturation, developability profiling and high‑throughput functional and biophysical assays
• Variability across automated platforms, assays, and labs, unpacking the implications for AI/ML model performance
• Human‑in‑the‑loop decision‑making, QA and QC, where expert input remains critical despite automation
• Scaling automated discovery workflows while maintaining data reproducibility, QC, and regulatory confidence