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 (logβML) and potentiometric sensitivity (PSML) 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.
Keywords: QSPR; coumarin; florescent chemosensor; potentiometric sensitivity; quantum DFT calculation; stability constant; toxicity.