During the model-building phase (see Figure ), we employed an Active Learning (AL) cycle combined with a human-in-the-loop approach to iteratively refine the model with expert input while minimizing overall annotation effort by focusing on the most informative cases. This phase comprised the following steps: i) Training Set Creation: A training set comprising both affected (identified by expert annotation) and unaffected (selected using SNA balancing strategy) HPLC traces were assembled in each annotation round. ii) ML Model Building: A ML model was built using the training set, then the model screened the dataset of 25k HPLC experiments to identify traces that were potentially affected (requiring further annotation) as well as those most likely unaffected (which were used for SNA later). iii) Human Expert Annotation: The flagged traces were reviewed by a human expert who annotated each as affected or unaffected, further enriching the dataset and improving the model's accuracy. This iterative cycle continued until the ML model achieved satisfactory performance; in total, only three rounds (one initial and two AL) of annotation were conducted to sufficiently train our model.