In summary, we have developed a new QSAR model validated with CheMLB structural alerts and molecular docking calculations, which constitutes the first application of semisupervised metric learning for predicting/rationalizing bioactivities in PFASs. Using a semisupervised metric learning algorithm, our machine-learning-based QSAR model accurately identified specific substructures, such as ester-containing groups, that play a possible role in determining bioactivities. With our semi-supervised learning approach, we obtained a distinct classification between bioactive and inactive molecules, resulting in an accuracy of up to 97.3% in the CF dataset. We also used semi-supervised metric learning to automatically classify/cluster and predict functional groups that could possibly play a role in bioactivity.