No abstract

Distribution coefficients (KDS) were measured by equilibrating a coal oil comparative reference material (CRM-1) with water and then separating the oil and water phases. Aqueous phase concentrations were determined by direct analysis of this phase, while organic phase concentrations were determined from the original oil composition by difference. The log K o values obtained for acidic and basic components were generally <3, while those for the neutral components ranged from 3 to 6. For aromatic hydrocarbons, strong correlations were observed between log KD and log Sw (water solubility), and between log K o and log Kow (octanol/water partition coefficient). Alkylated benzenes had significantly higher Kos than did unsubstituted aromatics of similar molecular weight. Examination of homologs revealed an increase of 0.307 IOg KD units per additional carbon atom for polynuclear aromatic hydrocarbons having from 10 to 16 carbons. Alkyl substituent effects determined for various sets of homologs ranged from 0.391 to 0.466 log K~ units perCH 2-group added.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant hazard because of their widespread industrial uses, environmental persistence, and bioaccumulation. A growing, increasingly diverse inventory of PFAS, including 8163 chemicals, has recently been updated by the U.S. Environmental Protection Agency. However, with the exception of a handful of well-studied examples, little is known about their human toxicity potential because of the substantial resources required for in vivo toxicity experiments. We tackle the problem of expensive in vivo experiments by evaluating multiple machine learning (ML) methods, including random forests, deep neural networks (DNN), graph convolutional networks, and Gaussian processes, for predicting acute toxicity (e.g., median lethal dose, or LD50) of PFAS compounds. To address the scarcity of toxicity information for PFAS, publicly available datasets of oral rat LD50 for all organic compounds are aggregated and used to develop state-of-the-art ML source models for transfer learning. A total of 519 fluorinated compounds containing two or more C-F bonds with known toxicity are used for knowledge transfer to ensembles of the best-performing source model, DNN, to generate the target models for the PFAS domain with access to uncertainty. This study predicts toxicity for PFAS with a defined chemical structure. To further inform prediction confidence, the transfer-learned model is embedded within a SelectiveNet architecture, where the model is allowed to identify regions of prediction with greater confidence and abstain from those with high uncertainty using a calibrated cutoff rate.

No abstract

No abstract