The Adapted POM Analysis of Avenanthramides In Silico
Abstract
POM analysis and related approaches are significant tools based on calculating various physico-chemical properties and predicting biological activity, ADME parameters, and toxicity of a molecule. These methods are used to evaluate a molecule’s potential to become a drug candidate. Avenanthramides (AVNs) are promising secondary metabolites specific to Avena spp. (oat). They comprise the amides of anthranilic acid linked to various polyphenolic acids with or without post-condensation molecule transformation. These natural compounds have been reported to exert numerous biological effects, including antioxidant, anti-inflammatory, hepatoprotective, antiatherogenic, and antiproliferative properties. To date, almost 50 various AVNs have been identified. We performed a modified POM analysis of 42 AVNs using MOLINSPIRATION, SWISSADME, and OSIRIS software. The evaluation of primary in silico parameters revealed significant differences among individual AVNs, highlighting the most promising candidates. These preliminary results may help coordinate and initiate other research projects focused on particular AVNs, especially those with predicted bioactivity, low toxicity, optimal ADME parameters, and promising perspectives.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.