Encoding the compounds to molecular fingerprints followed next in our framework. We used the extended connectivity fingerprint (ECFP) featurization with a default diameter of 4 (i.e., ECFP4), which considers a maximum of four neighbors. ECFPs are topological molecular representations developed for substructure and similarity searching. By encoding molecular structures into fingerprints, we obtained a binary array with a constant length of 2,048, making it a convenient input for the unsupervised/semi-supervised learning models. Furthermore, since the simplified molecular-input line-entry system (SMILES) sequences for all PFAS molecules are readily available, they can be easily converted into fingerprint-based representations using the RDKit software package. We then applied semi-supervised metric learning to the generated fingerprints by training machine learning models to predict the bioactivities of PFAS molecules by first (a) reducing the dimension of the fingerprint datasets and then (b) classifying/clustering them (see Figure ). Our QSAR model used a semi-supervised metric learning algorithm to automatically group/classify molecules with similar bioactivities. Metric learning has two main advantages: