Due to the sheer number of PFAS species, in vivo and in vitro biological experiments are both time-consuming and costly. As such, the construction of predictive and reliable quantitative-structure activity relationship (QSAR) models is essential for assessing the bioactivities of these contaminants (even for PFAS species that are yet to be made). Specifically, a QSAR model that can accurately predict the bioactivities of PFASs can be harnessed to screen several of these contaminants, saving immense time and experimental resources. While there have been prior machine learning studies on PFAS molecules, 9,10 most of these approaches used supervised learning techniques to suggest general structure-bioactivity trends after postprocessing of the data (i.e., the focus was on aggregate data for all targets as opposed to analyzing chemical trends specific to each target).