Machine Learning-Based QSPR Modeling of log CMC Values of Per- and Polyfluoroalkyl Substances (PFASs) and the Identification of Property Cliffs

Per- and polyfluoroalkyl substances (PFASs) possess sufficient water solubility and chemical durability, enabling them to affect both terrestrial and aquatic environments. Their wide chemical diversity makes it difficult to assess property end points experimentally. In this study, experimental critical micelle concentration (log CMC) data for representative PFASs and non-PFASs were used to develop computational predictive models. We developed a quantitative structure-property relationship (QSPR) model and a quantitative read-across structure-property relationship (q-RASPR) model for PFASs and defluorinated/non-PFASs separately and explored the importance of similarity and error-based features along with molecular descriptors. Subsequently, a combined data set of PFASs and non-PFASs was used to develop a read-across structure property relationship coupled with the multiclass arithmetic residuals in the K-groups analysis framework (ARKA-RASPR) for capturing response range-specific contribution. We explored the physicochemical insights of our developed models for PFASs and non-PFASs separately to understand the underlying reasons for the positive or negative correlations between the modeled descriptors and response variables. Furthermore, we developed a Python-based expert system, PFAS_(CMC)_Predictor v1.0 (available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/pfas-cmc-predictor), to predict the CMC value of a true external set comprising PFAS surfactants exclusively and any organic surfactants separately and to provide applicability domain status with graphical representation, thus emphasizing overall practicality and accessibility of this study. Additionally, we identified the property cliffs and investigated the reason for their cliff behavior. The developed models will further help in mapping sustainable mitigation strategies and control measures to reduce PFAS persistence.