Metabolic profiling of Lomatozona artemisiifolia Baker plants grown in vitro and collected from nature using molecular networking and chemometric analysis.

Brazilian Cerrado is recognised as a biodiversity hotspot due to the presence of endemic species with great biological potential. Particularly, Lomatozona artemisiifolia, is a rare species found in the Cerrado region in midwestern Brazil. Efforts have been made for its conservation in the Cerrado, such as the use of in vitro micropropagation, demanding a comparative analysis between grown plants and those collected from nature. For this purpose, we performed the chemical study of L. artemisiifolia by LC-ESI-MS/MS and molecular networking analysis in the Global Natural Products Social Molecular Networking (GNPS) with in silico annotation using Network Annotation Propagation (NAP), which led to the observation of labdane diterpenes and flavonoid subclasses as the most representative specialised metabolites of this plant. In addition, molecular networking and chemometric analysis were correlated, allowing the metabolite profile emerging from field growth and micropropagation conditions to be observed.

"pySiRC": Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants.

We developed a web application structured in a machine learning and molecular fingerprint algorithm for the automatic calculation of the reaction rate constant of the oxidative processes of organic pollutants by •OH and SO4•- radicals in the aqueous phase-the pySiRC platform. The model development followed the OECD principles: internal and external validation, applicability domain, and mechanistic interpretation. Three machine learning algorithms combined with molecular fingerprints were evaluated, and all the models resulted in high goodness-of-fit for the training set with R2 > 0.931 for the •OH radical and R2 > 0.916 for the SO4•- radical and good predictive capacity for the test set with Rext2 = Qext2 values in the range of 0.639-0.823 and 0.767-0.824 for the •OH and SO4•- radicals. The model was interpreted using the SHAP (SHapley Additive exPlanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing and -donating groups interfere with the reactivity of the •OH and SO4•- radicals. We hope that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water treatment units based on advanced oxidative technologies.