Glycogen synthase kinase-3 beta (GSK-3β) plays a crucial role in multiple cellular processes and is implicated in different types of cancers and neurological disorders, including Alzheimer’s disease. Despite extensive efforts to develop novel GSK-3β inhibitors, the discovery of potent and selective lead compounds remains a challenge. In this study, we evaluated the GSK-3β inhibitory potential of semisynthetic flavonoid derivatives, which exhibited sub-micromolar activity. To gain further insights, we employed molecular docking, molecular dynamics simulations, and pharmacokinetic profile predictions. The docking studies revealed that the most potent inhibitor, compound 10, establishes key interactions with the ATP-binding site. Molecular dynamics simulations further confirmed that compound 10 maintains stable interactions with GSK-3β throughout the simulation. Additionally, pharmacokinetic predictions identified compound 3 as a promising candidate for Alzheimer’s disease therapy due to its ability to cross the blood–brain barrier. These findings suggest that, within the studied flavonoid derivatives, these compounds (particularly 10 and 3) hold potential as lead compounds for GSK-3β inhibition. The combination of strong enzymatic inhibition, stable binding interactions, and favorable pharmacokinetic properties highlights their promise for further development in cancer and neurodegenerative disease research.

The Corona virus disease-19 (COVID-19) pandemic challenged the scientific community in the search for developing effective treatments, such as medicine and/or a vaccine candidate. The SARS-CoV-2 virus and its variants, Omega, Omicron, and Delta, remain as a major threat to human health, causing significant morbidity and mortality worldwide. Given that computational methods are thought to be quick, easy, and inexpensive, they have been widely used in this scenario to design new anti-COVID-19 drug candidates. In addition, heterocyclic scaffolds have been explored exhaustively for their biological properties and as fruitful sources of new molecular entities to fulfill the chemical space available.In light of this, we intend to highlight the synthetic techniques used to produce novel heterocyclic derivatives that may serve as effective anti-COVID-19 lead candidates by focusing on important viral proteins and using computational tools. Then, the objective of this article, with a theoretical nature, is to contribute to the delimitation of organic chemistry methods to achieve new anti- COVID-19 agents.

A growing global population and increasingly stringent food-security demands, together with evolving challenges such as crop diseases, resistant weeds, and agrochemical-resistant pathogenic microorganisms, require new strategies to safeguard agricultural productivity. Succinate dehydrogenase (SDH, also known as complex II) participates in both the tricarboxylic acid cycle and the mitochondrial respiratory chain in pathogenic fungi, thereby affecting fungal metabolism and mitochondrial respiration. This enzyme has therefore been widely explored as a target in the search for new fungicides. Currently, 25 commercially available succinate dehydrogenase inhibitors (SDHIs) are used to protect economically important crops. The discovery of new agrochemicals is driven by the need to mitigate annual losses in agricultural productivity, estimated at approximately US$220 billion per year. This review summarizes advances in the synthesis of potent SDHIs over the last decade, including semisynthetic approaches and strategies combining carboxamide scaffolds with heterocyclic moieties based on bioisosterism, scaffold hopping, and molecular hybridization. The studies summarized here show that numerous hit compounds have recently been synthesized with EC50 values against several fungal strains and IC50 values for SDH inhibition that compare favorably with those of reference agrochemicals such as boscalid, thifluzamide, fluxapyroxad, fenfuram, and fluopyram. Finally, both established and emerging synthetic methods aimed at accelerating the generation of large compound libraries are increasingly supported by robust computational approaches, including 3D-QSAR and molecular dynamics simulations, to predict binding modes and provide structural insights for future investigations.