Over the past several years, the term PFAS (per- and polyfluoroalkyl substances) has grown to be emblematic of environmental contamination, garnering public, scientific, and regulatory concern. PFAS are synthesized by two processes, direct fluorination (e.g., electrochemical fluorination) and oligomerization (e.g., fluorotelomerization). More than a megatonne of PFAS is produced yearly, and thousands of PFAS wind up in end-use products. Atmospheric and aqueous fugitive releases during manufacturing, use, and disposal have resulted in the global distribution of these compounds. Volatile PFAS facilitate long-range transport, commonly followed by complex transformation schemes to recalcitrant terminal PFAS, which do not degrade under environmental conditions and thus migrate through the environment and accumulate in biota through multiple pathways. Efforts to remediate PFAS-contaminated matrices still are in their infancy, with much current research targeting drinking water.

Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant conditions. The hydrolysis reaction schemes in the library encode the process science gathered from peer-reviewed literature and regulatory reports. Each scheme has been ranked on a scale of one to six based on the median half-life in a data set compiled from literature-reported hydrolysis rates. These ranks are used to predict the most likely transformation route when more than one structural fragment susceptible to hydrolysis is present in a molecule of interest. Separate rank assignments are established for pH 5, 7, and 9 to represent standard conditions in hydrolysis studies required for registration of pesticides in Organisation for Economic Co-operation and Development (OECD) member countries. The library is applied to predict the likely hydrolytic transformation products for two lists of chemicals, one representative of chemicals used in commerce and the other specific to pesticides, to evaluate which hydrolysis reaction pathways are most likely to be relevant for organic chemicals found in the natural environment.

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Cheminformatics-based applications to predict transformation pathways of environmental contaminants are useful to quickly prioritize contaminants with potentially toxic/persistent products. Direct photolysis can be an important degradation pathway for sunlight-absorbing compounds in aquatic systems. In this study, we developed the first freely available direct phototransformation pathway predictive tool, which uses a rule-based reaction library. Journal publications studying diverse contaminants (such as pesticides, pharmaceuticals, and energetic compounds) were systematically compiled to encode 155 reaction schemes into the reaction library. The execution result of this predictive tool was internally evaluated against 390 compounds from the compiled journal publications and externally evaluated against 138 compounds from the regulatory reports. The recall (sensitivity) and precision (selectivity) were 0.62 and 0.35, respectively, for internal evaluation, and 0.56 and 0.20, respectively, for external evaluation, when only the products formed from the first reaction step were counted. This predictive tool could help to narrow the data gaps in chemical registration/evaluation and inform future experimental studies.

Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (K), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of K and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.