Figure displays true-positive ratios and classifications between bioactive/inactive molecules on four representative targets that show the best performance in the CF dataset using semi-supervised metric learning (for example, in Fig. , we obtain a true-positive ratio of 97.3% by computing number of molecules containing esters and are also bioactive number of ester-containing molecules in the cluster ). Using the Maximum Common Structure (MCS) module in the RDKit software package on bioactive molecules, we found that the ester functional group is the critical substructure that causes bioactivity on Cyps (Figures and) and ATXN (Figure ). Table summarizes the substructures predicted to play a vital role in bioactivity toward nine different targets. The other 17 targets did not demonstrate as distinct clustering as the nine targets in Table due to a relatively weak correlation between molecular structure and bioactivity. lists the predicted substructures for specific targets. We used structural alerts to cross-check the validity of the predicted substructures that play a crucial role in bioactivity. Within the bioinformatics community, structural alerts are molecular functional groups associated with a particularly adverse outcome, in our case, bioactivity. We cross-referenced the CheMBL dataset to our machine learning results since it contains structural alert information for some PFAS molecules. Figure shows structural alerts of the molecules that are bioactive on CYP2CP, and, as mentioned previously, the ester group was found to be the critical structure that induces interaction with Cyps.