Combination of 2D and 3D-QSAR studies on DAPY and DANA derivatives as potent HIV-1 NNRTIs

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

Development of Heterocyclic PPAR Ligands for Potential Therapeutic Applications

The family of nuclear peroxisome proliferator-activated receptors (PPARα, PPARβ/δ, and PPARγ) is a set of ligand-activated transcription factors that regulate different functions in the body. Whereas activation of PPARα is known to reduce the levels of circulating triglycerides and regulate energy homeostasis, the activation of PPARγ brings about insulin sensitization and increases the metabolism of glucose. On the other hand, PPARβ when activated increases the metabolism of fatty acids. Further, these PPARs have been claimed to be utilized in various metabolic, neurological, and inflammatory diseases, neurodegenerative disorders, fertility or reproduction, pain, and obesity. A series of different heterocyclic scaffolds have been synthesized and evaluated for their ability to act as PPAR agonists. This review is a compilation of efforts on the part of medicinal chemists around the world to find novel compounds that may act as PPAR ligands along with patents in regards to PPAR ligands. The structure-activity relationship, as well as docking studies, have been documented to better understand the mechanistic investigations of various compounds, which will eventually aid in the design and development of new PPAR ligands. From the results of the structural activity relationship through the pharmacological and in silico evaluation the potency of heterocycles as PPAR ligands can be described in terms of their hydrogen bonding, hydrophobic interactions, and other interactions with PPAR.