We compiled the training dataset by merging the MLPCN screening data with the LC1234 screening data (Table ). We defined active compounds, also referred to as hits, as those with median % inhibition of the primary screens ≥ 35%, median % inhibition in retest screens ≥ 35%, and not flagged by the PAINS filter (Appendix A.1). The 35% single point activity threshold used for active label assignment corresponds to 3 standard deviations in the % inhibition distribution observed in the LC1234 and MLPCN screens. The merged training dataset contained 427,300 compounds with 554 actives. We split the training data into 10 folds for cross validation. The splitting method takes into account library information (LC1234 and MLPCN) by grouping the compounds based on their libraries and then stratifying each of these groups into 10 folds that maintain roughly the same binary activity label distribution. The splitting does not account for shared chemical structures across folds.