Novel 1,2,3-Triazole-Based Benzothiazole Derivatives: Efficient Synthesis, DFT, Molecular Docking, and ADMET Studies.

A new series of 1,2,3-triazole derivatives 5a-f based on benzothiazole were synthesized by the 1,3-dipolar cycloaddition reaction of S-propargyl mercaptobenzothiazole and α-halo ester/amide in moderate to good yields (47-75%). The structure of all products was characterized by 1H NMR, 13C NMR, and CHN elemental data. This protocol is easy and green and proceeds under mild and green reaction conditions with available starting materials. The structural and electronic analysis and 1H and 13C chemical shifts of the characterized structure of 5e were also calculated by applying the B3LYP/6-31 + G(d, p) level of density functional theory (DFT) method. In the final section, all the synthesized compounds were evaluated for their anti-inflammatory activity by biochemical COX-2 inhibition, antifungal inhibition with CYP51, anti-tuberculosis target protein ENR, DPRE1, pks13, and Thymidylate kinase by molecular docking studies. The ADMET analysis of the molecules 5a-f revealed that 5d and 5a are the most-promising drug-like molecules out of the six synthesized molecules.

De novo antioxidant peptide design via machine learning and DFT studies.

Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.