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All Journal HAYATI Journal of Biosciences Jurnal Penelitian Sains Jurnal Pengabdian West Science Life Science and Biotechnology Bioeksperimen
Farhan, Abdillah Maulana
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Immunology & microbiology Mathematics Physics Public Health Social Sciences

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Journal : Bioeksperimen

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Pharmacological prediction of Marchantia polymorpha: GC-MS and molecular docking approaches Farhan, Abdillah Maulana
Bioeksperimen: Jurnal Penelitian Biologi March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/bioeksperimen.v12i1.15983

Abstract

Marchantia polymorpha is the liverworts that can be found abundantly in the Mount Pasang Jember area. The metabolite profiling of M. polymorpha has not yet been conducted.  The method used in this study was untargeted metabolite profiling using GC-MS, and the results of the metabolite compound profiling were analyzed in silico using bioinformatics-based, namely PASS Online, SwisADME, and Molecular Docking using PyRx 8.0 with AutodockVina. The aims  of docking with this protein was adjusted to the PASS Online results, namely alkenylglycerophosphocholine hydrolase inhibitor and antiseborrheic, which are closely related to antifungals. The natural metabolite compounds detected from M. polymorpha were fatty acids (20%), terpenoids (16%), and phenolics (10%). Terpenoids-phenolics compounds were analyzed in silico to predict pharmacological potential. Terpenoids showed that the compound with the highest Pa value was 3,7-Cyclodecadiene (Pa=0.920), predicted to be an alkenylglycerophosphocholine hydrolase inhibitor, while the phenolic compound with the highest Pa value was hydroquinone (Pa=0.927), predicted to be an antiseborrheic. Based on SwissADME in silico Druglikeness, five compounds out of a total of six terpenoid and phenolic compounds showed compliance with Lipinski's theory. In addition, the in silico results also showed a bioavailability score of 0.55. Molecular docking was performed on the target protein sterol 14-α-demethylase (CYP51) from Candida albicans (PDB ID: 5TZ1). This protein is representative of antifungal agents. The results of molecular docking showed that the compound 3,7-Cyclodecadiene consistently had the strongest binding affinity value of -10.1 kcal/mol with residues ILE A:55, ALA A:62, PHE A:58, and TRP B:5  These results  imply that further research on M. polymorpha metabolites should be conducted using comprehensive methods to explore their potential in the field of health.
Co-Authors Aulasya, Ulfie Sakha Azizah, Alya Najwa Dexa Sya’ban, Sabaquna Biliman Dwi Setyati Dwinianti, Edia Fitri Febiani, Tika Fuad Bahrul Ulum Gilani, Noor Azizah Ilmiah , Ika iffah Inayah, Ulfatul Masayu, Muhammad Zulfikar Arian Maulana Farhan, Abdillah Mufidi, Farah Fakhirah Perkasa, Excel Taruna Reksa Putri, Yusi Ananda Risqi, Alexandria Zazila Rosida, Waki'atil Shodiq, Abdulloh Hasan Shodiq, Muhammad Husein Su’udi, Mukhamad