Artificial intelligence approach in identification of differentially expressed genes of methyl glycoside against myocardial infarction

Abstract

To predict the targets of small molecules from traditional plants using an artificial intelligence (AI) approach for myocardial infarction. In this study, we used different web servers and software to predict the targets of small molecule from plant of interest. The methanolic extract of Aganosma dichotoma was screened and the presence of small molecules was confirmed by GC–MS analysis. In this study, Methyl beta d-xylopyranoside is a Methyl glycoside, which was identified as a small molecule (164.16 gms/mol). Pharmacokinetic and toxicity prediction of methyl glycoside showed good bioavailability, accepting the five rules of Lipinki and LD50 ≥ 5000 mg/kg. Differentially expressed genes were identified from the datasets and overlapping genes were assessed for gene enrichment analysis pathways with p < 0.05. A PPI network was constructed and hub genes were identified. Anti-oxidant potency of small molecule is validated through in-vitro anti-oxidant activity through DPPH and ABTS analysis where, small molecule DPPH, ABTS activity was reported to have an IC50 value of 51.09%, 80.67% and 48.41%, 90.19% at 0.1 μM/ml, 0.5 μM/ml concentrations respectively. The small molecule methyl beta d-xylopyranoside is a robust antioxidant that may act against myocardial infarction caused by ischemia and free radical generation, but needs to be validated through further in vivo studies.