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All Journal Jurnal Masyarakat Informatika Chimica et Natura Acta
Samsuri, Ahmad
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Environmental Science Materials Science & Nanotechnology

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Artificial Intelligence-Aided In Silico Screening of Syzygium polyanthum Phytochemicals for Antidiabetic Drug Discovery Using ACO (Ant Colony Optimization) Algorithm Samsuri, Ahmad; Hermawan, Faris; Zikri, Adi Tiara; Vifta, Rissa Laila; Puspitasari, Anita Dwi
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73574

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.
Studi Penambatan Molekul Senyawa Metabolit Sekunder Batang Kayu Manis (Cinnamomum burmanni) sebagai Kandidat Obat Antidiabetes Mellitus Tipe II Puspitasari, Anita Dwi; Murti, Nugrahaeni Kresna; Samsuri, Ahmad; Shofiyah, Jihan Labiba Nur
Chimica et Natura Acta Vol 12, No 2 (2024)
Publisher : Departemen Kimia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/cna.v12.n2.53563

Abstract

Diabetes Mellitus (DM) tipe II merupakan penyakit kronis yang prevalensinya semakin meningkat setiap tahunnya. Salah satu strategi terapi untuk pengobatan DM tipe II melalui penghambatan enzim α-glucosidase. Dalam beberapa tahun terakhir, pencarian obat antidiabetes alami semakin populer. Ekstrak batang kayu manis (Cinnamomum burmanni) diketahui berpotensi sebagai antidiabetes. Penelitian ini bertujuan untuk mengidentifikasi senyawa metabolit sekunder dari batang kayu manis (Cinnamomum burmanni) yang paling potensial sebagai inhibitor α-glucosidase secara in silico. Sembilan senyawa metabolit sekunder hasil isolasi batang kayu manis dari penelitian sebelumnya dilakukan penambatan molekul terhadap enzim α-glucosidase kode PDB 2JKE dengan pembanding miglitol dan dianalisis berdasarkan energi pengikatannya. Hasil penelitian menunjukkan bahwa kode PDB 2JKE valid dengan nilai RMSD 0,2912 Å. Berdasarkan analisis penambatan molekul, senyawa nerolidol memiliki score docking (-81,165) yang lebih kecil dibandingkan miglitol (-80,2642) sehingga secara teoritis senyawa nerolidol memiliki penghambatan yang lebih baik terhadap enzim α-glucosidase dibandingkan miglitol. Selain itu, profil ADMET menunjukkan bahwa nerolidol tidak melanggar Lipinski Rules dan tidak toksik.
Co-Authors Anita Dwi Puspitasari Faris Hermawan, Faris Murti, Nugrahaeni Kresna Shofiyah, Jihan Labiba Nur vifta, rissa laila Zikri, Adi Tiara