1,2,3-Triazolyl-tetrahydropyrimidine Conjugates as Potential Sterol Carrier Protein-2 Inhibitors: Larvicidal Activity against the Malaria Vector Anopheles arabiensis and In Silico Molecular Docking Study

Alteration of insect growth regulators by the action of inhibitors is becoming an attractive strategy to combat disease-transmitting insects. In the present study, we investigated the larvicidal effect of 1,2,3-triazolyl-pyrimidinone derivatives against the larvae of the mosquito Anopheles arabiensis, a vector of malaria. All compounds demonstrated insecticidal activity against mosquito larvae in a dose-dependent fashion. A preliminary study of the structure–activity relationship indicated that the electron-withdrawing substituent in the para position of the 4-phenyl-pyrimidinone moiety enhanced the molecules’ potency. A docking study of these derivatives revealed favorable binding affinity for the sterol carrier protein-2 receptor, a protein present in the intestine of the mosquito larvae. Being effective insecticides against the malaria-transmitting Anopheles arabiensis, 1,2,3-triazole-based pyrimidinones represent a starting point to develop novel inhibitors of insect growth regulators.     

Harnessing Deep Neural Networks to Analyze Multi-Channel Anion Sensing Characteristics of a Ru(II)-Pyrazolyl-Bis(Benzimidazole) Complex

In this work, the anion-responsive conduct of a Ru(II)-bipyridine complex incorporating pyrazolyl-bis (benzimidazole) ligand is thoroughly investigated in acetonitrile and water via absorption and emission spectroscopy as well as by square-wave voltammetry (SWV). Substantial alteration of the photo-redox behavior of the complex is observed in the presence of the selected anions. The free form of the complex exhibits emission indicating the “on-state”, while inclusion of anions leads to quenching of emission and represents the “off-state”. The restoration of the initial state of the complex is feasible in the presence of acid and the process is reversible and can be recycled. In essence, the complex functions as anion- and acid-responsive molecular switches. Additionally, we applied herein neural network based deep learning methodologies, viz. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS)} for thorough analysis and fully understand the multi-channel anion sensing behavior of the complex.