In this work, we present a new QSAR model using semi-supervised metric learning techniques to assess which functional groups affect bioactivities toward specific biological targets. Semi-supervised learning is a different machine learning approach that has the advantages of both supervised and unsupervised learning. It can be used on a dataset with primarily unlabeled data and only a few labeled data. Like unsupervised learning, it can also automatically cluster unlabeled data. Our approach is integrated with molecular docking calculations to predict possible bioactivities of PFAS molecules based on their chemical functional groups and specific biological targets (e.g., genes, proteins, or cell lines). Our approach first combines dimension reduction methods with clustering methods to classify PFASs based on their molecular structures. We then apply a semi-supervised metric learning method to improve classification accuracy. Finally, we use a molecular docking approach to shed light on the physicochemical reasons for their bioactivity. Our study provides the first unsupervised/semi-supervised learning approach for screening potentially bioactive PFAS molecules beyond conventional supervised learning or QSAR approaches. Our QSAR machine-learning framework, shown in Figure , utilizes four sequential steps followed by a reasoning/validation step: (1) collecting a training dataset from verified opensource databases, (2) encoding those compounds into molecular fingerprints, (3) clustering the data to predict chemical properties based on the molecular fingerprints and assessing the performance of the models, (4) evaluating the clustering by choosing the optimal model and predicting molecular groups responsible for bioactivity based on the clustering, and ( ) molecular docking simulations to rationalize the role of the chemical functional groups. All of our machine learning algorithms are publicly available (see Supporting Information).