Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure–property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβ_ML) and potentiometric sensitivity (PS_ML) of 200 ligands in complexes with the heavy metal ions Cu²⁺, Cd²⁺, and Pb²⁺. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R²) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R² of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Diabetes mellitus is a major global health concern associated with severe metabolic and cardiovascular complications. This study evaluated the antidiabetic and antioxidant activities of Oxalis corniculata L. aerial parts, with a focus on α-glucosidase and α-amylase inhibition, using a combination of in vitro assays and in silico analyses. Among the tested fractions, the ethyl acetate fraction exhibited the strongest inhibitory activity against both enzymes, with IC₅₀ values of 0.097 and 0.015 mg/mL for α-glucosidase and α-amylase, respectively, surpassing those of the reference drug, acarbose. This fraction also demonstrated potent antioxidant activity, with IC₅₀ values of 0.025 and 0.020 mg/mL in DPPH and ABTS assays, respectively. To elucidate the underlying mechanisms beyond digestive enzyme inhibition, bioactive constituents were screened and evaluated using network pharmacology, molecular docking, molecular dynamics simulations, and density functional theory (DFT) calculations. Molecular docking and dynamic simulations confirmed stable and energetically favorable interactions with α-glucosidase and α-amylase. Network pharmacology analysis revealed that the antidiabetic effects of O. corniculata involve modulation of insulin resistance-related pathways, particularly PI3K/Akt signaling, GLUT4 translocation, and inflammation-associated targets, alongside regulation of oxidative stress through redox-related enzymes. Complementary DFT analysis provided molecular-level insights into the antioxidant mechanisms, highlighting favorable electronic properties that support efficient radical scavenging. Overall, this integrated experimental–computational study provided valuable evidence of O. corniculata aerial parts as a promising multi-target phytotherapeutic candidate for diabetes management, extending its therapeutic relevance beyond α-glucosidase and α-amylase inhibition.