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This is a In Silico Study study.

Artificial Intelligence-Aided In Silico Screening of Syzygium polyanthum Phytochemicals for Antidiabetic Drug Discovery Using ACO (Ant Colony Optimization) Algorithm

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The screening identified seven compounds with stronger binding to α-glucosidase than the reference drug miglitol, indicating potential for drug development.
Docking scores ranged from -96.2919 to -80.5188, outperforming miglitol and the native ligand in binding affinity results.
Using an AI-driven approach with the PLANTS software, molecular docking simulations were conducted to evaluate binding efficiencies of phytochemicals.
These findings highlight the effectiveness of AI in streamlining the antidiabetic drug discovery process by prioritizing promising compounds.

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Molecular docking identifies superior inhibitors of α-glucosidase in Syzygium polyanthum, suggesting a novel AI method for drug discovery.

Samsuri et al. (2025) studied this question.

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Molecular docking identifies superior inhibitors of α-glucosidase in Syzygium polyanthum, suggesting a novel AI method for drug discovery.