RESUMEN
Diabetes mellitus (DM) manifests as a complex and chronic metabolic disorder, posing a significant threat to global public health and contributing substantially to mortality rates. It is characterized by elevated blood glucose levels or hyperglycemia and requires effective preventive and therapeutic strategies. One promising approach involves targeting the inhibition of α- glucosidase and α-amylase, key enzymes responsible for carbohydrate hydrolysis. Inhibiting these enzymes proves beneficial in reducing postprandial glucose levels and mitigating postprandial hyperglycemia. However, existing antidiabetic medications are associated with undesirable side effects, highlighting the need to develop new molecules with increased efficacy and reduced side effects. Traditional methods for designing such molecules are often lengthy and costly. To address this, computer-based molecular modeling tools offer a promising approach to evaluate the antidiabetic activities of chemical compounds. This review aims to compile information on chemical compounds assessed for their anti-diabetic activities through molecular modeling, with a particular focus on the period from 2020 to 2023.
RESUMEN
Candida albicans and Aspergillus fumigatus are recognized as significant fungal pathogens, responsible for various human infections. The rapid emergence of drug-resistant strains among these fungi requires the identification and development of innovative antifungal therapies. We undertook a comprehensive screening of 297 naturally occurring compounds to address this challenge. Using computational docking techniques, we systematically analyzed the binding affinity of each compound to key proteins from Candida albicans (PDB ID: 1EAG) and Aspergillus fumigatus (PDB ID: 3DJE). This rigorous in silico examination aimed to unveil compounds that could potentially inhibit the activity of these fungal infections. This was followed by an ADMET analysis of the top-ranked compound, providing valuable insights into the pharmacokinetic properties and potential toxicological profiles. To further validate our findings, the molecular reactivity and stability were computed using the DFT calculation and molecular dynamics simulation, providing a deeper understanding of the stability and behavior of the top-ranking compounds in a biological environment. The outcomes of our study identified a subset of natural compounds that, based on our analysis, demonstrate notable potential as antifungal candidates. With further experimental validation, these compounds could pave the way for new therapeutic strategies against drug-resistant fungal pathogens.