Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.

Abstract

New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine learning models, these provide more accurate and automated results with high testing accuracy. The Schiff base ruthenium complexes used in the proposed study were synthesized using 4-aminoantipyrine derivatives. 4-Aminoantipyrine is a biologically active pharmacophore. The geometry of the complexes was confirmed by IR, electronic, and magnetic measurements. XRD analysis pointed out the nanocrystalline behavior of the chelates. The molecular structures have been optimized using DFT calculations. The ruthenium complexes are one of the main chemotherapeutic agents in anticancer therapy over platinum drugs due to a wide range of peculiarities. Complexes exhibit octahedral geometry as confirmed by magnetic measurements exhibiting more biological activity. The complexes are redox active depicting high biological potency. The Ru chelates also display high photocatalytic efficiency. The chelates also adhere to Lipinski's rule of five as evidenced from mole inspiration calculations. Among the chelates, RuL³ exhibits high anticancer potency suggesting a valuable candidate for the treatment of cancer. RuL⁵ has high antibacterial efficiency, and RuL⁴ complex possesses high antifungal activity. The chelates may serve as potential antimicrobial agents.