Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling

Iron-associated reductants play a
crucial role in providing electrons
for various reductive transformations. However, developing reliable
predictive tools for estimating abiotic reduction rate constants (logk) in such systems has been impeded by the intricate nature
of these systems. Our recent study developed a machine learning (ML)
model based on 60 organic compounds toward one soluble Fe(II)-reductant.
In this study, we built a comprehensive kinetic data set covering
the reactivity of 117 organic and 10 inorganic compounds toward four
major types of Fe(II)-associated reductants. Separate ML models were
developed for organic and inorganic compounds, and the feature importance
analysis demonstrated the significance of resonance structures, reducible
functional groups, reductant descriptors, and pH in logk prediction. Mechanistic interpretation validated that the models
accurately learned the impact of various factors such as aromatic
substituents, complexation, bond dissociation energy, reduction potential,
LUMO energy, and dominant reductant species. Finally, we found that
38% of the 850,000 compounds in the Distributed Structure-Searchable
Toxicity (DSSTox) database contain at least one reducible functional
group, and the logk of 285,184 compounds could be
reasonably predicted using our model. Overall, the study is a significant
step toward reliable predictive tools for anticipating abiotic reduction
rate constants in iron-associated reductant systems.