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T. Chohan

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Open access Aug 2026

Unveiling the antioxidant and antimicrobial potential of Astragalus tephrosoides methanol extract: Phytochemical screening, GC-MS profiling, computational and experimental approaches.

BACKGROUND Plant-derived bioactive compounds have significant pharmaceutical applications. OBJECTIVES This study focused on the methanol extract of Astragalus tephrosoides (A. tephrosoides) to evaluate its phytochemical composition, biological activities and in-silico properties. METHOD Phytochemical screening, GC-MS profiling, antioxidant (DPPH), antimicrobial and phytotoxic assays were performed. Molecular docking against bacterial proteins (DNA gyrase, lanosterol-14-α-demethylase, PBP2A and chitin synthase) and density functional theory (DFT) analyses were conducted for the bioactive compounds identified by GC-MS. RESULTS Phytochemical analysis confirmed the presence of alkaloids, steroids, terpenoids, saponins, phenolics, volatile oils and coumarins in the extract. GC-MS analysis revealed the presence of 17 bioactive compounds. The extract exhibited notable antioxidant activity (IC50 = 0.66 mg/mL), comparable to that of ascorbic acid (IC50 = 0.58 mg/mL). Antibacterial effects were observed with net absorbance values of 0.860 (E. coli, P. aeruginosa), 0.770 (B. subtilis), 0.656 (S. aureus) and 0.550 (S. typhi) and MICs of 1.0-0.12 µg/mL. The extract exhibited antifungal activity comparable to that of miconazole and moderate phytotoxicity (66.6%) at 1000 μg/mL. Docking revealed moderate binding affinities, while DFT analysis revealed significant HOMO/LUMO gaps, indicating their thermodynamic stability. CONCLUSION A. tephrosoides contains bioactive compounds with promising antioxidant, antimicrobial and moderate phytotoxic activities, as supported by computational studies.

Fakhra Batool, Muhammad Wajid, H. Alzahrani et al. · 0 citations
Jul 2026

Modeling Structure-Activity Relationships with Machine Learning to Identify DPP4 Inhibitors as potential Therapeutics for Type 2 Diabetes

Findings identify CP20 as a promising lead scaffold for the development of novel DPP4 inhibitors and demonstrate the effectiveness of an ensemble machine learning-guided computational framework for accelerating antidiabetic drug discovery.

Iqra Anwar, T. Chohan, Drakhshaan et al. · 0 citations

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