DOI: https://doi.org/10.14710/jmasif.16.2.73574

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

1Bachelor of Pharmacy Study Program, Faculty of Pharmacy, Universitas Wahid Hasyim, Semarang, Indonesia

2Research Center for Pharmaceutical Ingredient and Traditional Medicine, National Research and Innovation Agency (BRIN), Banten, Indonesia

3Department of Materials Science and Engineering, Chonnam National University,, South Korea

4 Faculty of Pharmacy, Universitas Islam Sultan Agung, Semarang, Indonesia

5 Bachelor of Pharmacy Study Program, Faculty of Pharmacy, Universitas Wahid Hasyim, Semarang, Indonesia, Indonesia

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Received: 23 May 2025; Revised: 8 Jul 2025; Accepted: 14 Jul 2025; Published: 15 Jul 2025.
Open Access Copyright (c) 2025 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

This research employs an artificial intelligence (AI)-driven molecular docking approach to identify potential antidiabetic compounds from Syzygium polyanthum phytochemicals targeting the α-glucosidase enzyme. The docking simulations were conducted using the PLANTS software, which utilizes an ant colony optimization (ACO) algorithm, a nature-inspired AI technique that mimics the foraging behavior of ants to explore ligand binding conformations efficiently. PLANTS integrates multiple empirical scoring functions, including ChemPLP, to evaluate protein-ligand interactions by modeling steric complementarity, hydrogen bonding, and torsional potentials, enabling accurate prediction of binding affinities. The protein structure with PDB code 2JKE was validated with a root-mean-square deviation (RMSD) of 0.2912 Å, confirming the reliability of the docking protocol. Screening results revealed seven phytochemical compounds Hexadecanoic acid 2-hydroxy-1-(hydroxymethyl), Methyl oleate, Methyl palmitate, Phytol, 9,12,15-Octadecatrien-1-ol, Nerolidol, and Eicosane exhibited lower docking scores (-96.2919 to -80.5188) than both the reference drug miglitol (-80.2642) and the native ligand (-77.2910), indicating stronger and more stable binding to the α-glucosidase active site. These findings suggest that the identified compounds have superior theoretical inhibitory potential compared to miglitol, a clinically used α-glucosidase inhibitor. The AI-based in silico screening using PLANTS thus provides a powerful, cost-effective strategy for accelerating antidiabetic drug discovery by prioritizing promising natural compounds for further experimental validation.

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Keywords: Artificial Intelligence, PLANTS software, Syzygium polyanthum, Antidiabetic Drug

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Section: Research Article
Language : EN
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