A facile sonochemical protocol for synthesis of 3-amino- and 4-amino-1,2,4-triazole derived Schiff bases as potential antibacterial agents

Significance of Triazole in Medicinal Chemistry: Advancement in Drug Design, Reward and Biological Activity

One of the triazole tautomers, 1,2,4-triazole derivatives, has a wide range of biological activities that suggest its potential therapeutic utility in medicinal chemistry. These actions include anti-inflammatory, anti-cancer, anti-bacterial, anti-tuberculosis, and anti-diabetic effects. Using computational simulations and models, we investigate the structure-activity relationships of 1,2,4-triazoles, showing how various modifications to the triazole core yield a variety of clinical therapeutic benefits. The review highlights the anti-inflammatory effect of 1,2,4-triazoles in relation to their ability to disrupt significant inflammatory mediators and pathways. We present in-silico data that illuminate the triazoles' capacity to inhibit cell division, encourage apoptosis, and stop metastasis in a range of cancer models. This review looks at the bactericidal and bacteriostatic properties of 1,2,4-triazole derivatives, with a focus on their potential efficacy against multi-drug resistant bacterial infections and their usage in tuberculosis therapy. In order to better understand these substances' potential anti-diabetic benefits, this review also looks at how they affect glucose metabolism regulation and insulin responsiveness. Coordinated efforts are required to translate the efficacy of 1,2,4-triazole compounds in preclinical models into practical therapeutic benefits. Based on the information provided, it can be concluded that 1,2,4-triazole derivatives are a promising class of diverse therapeutic agents with potential utility in a range of disorders. Their development and improvement might herald a new era of medical care that will be immensely advantageous to both patients and the medical community as a whole. This comprehensive research, which is further reinforced by in-silico investigations, highlights the great medicinal potential of 1,2,4-triazoles. Additionally, this study encourages more research into these substances and their enhancement for use in pharmaceutical development.

DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning-Application to COVID-19

This study formulates antiviral repositioning as a matrix completion problem wherein the antiviral drugs are along the rows and the viruses are along the columns. The input matrix is partially filled, with ones in positions where the antiviral drug has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) are encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion (GRMC) problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results of our curated RNA drug-virus association data set show that the proposed approach excels over state-of-the-art GRMC techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under trials for the same.