In AI-driven molecule discovery, a predictive model's performance is not static. Statistical drift in screening data, evolving chemical libraries, or target-specific assay changes can silently degrade model accuracy, leading to poor candidate selection and wasted lab resources. This workflow automates continuous model health surveillance, replacing periodic manual checks with an orchestrated system that monitors prediction distributions, compares them to validation baselines, and flags deviations indicative of concept or data drift before they impact project timelines.




