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All Journal HAYATI Journal of Biosciences INDONESIAN JOURNAL OF PHARMACY Indonesian Journal of Chemistry Unnes Journal of Public Health Narra J Jurnal Natural Jurnal Kedokteran Hewan Heca Journal of Applied Sciences Malacca Pharmaceutics Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Indonesian Journal of Case Reports Indatu Journal of Management and Accounting Grimsa Journal of Science Engineering and Technology International Journal of Renewable Energy Development Journal Of Carbazon JURNAL REKAYASA KIMIA & LINGKUNGAN
Rinaldi Idroes
Department Of Chemistry, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

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Journal : malacca pharmaceutics

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Antibacterial Potential of Geothermal Plant Extracts from Jaboi Crater, Indonesia: A Thin Layer Chromatography-Bioautography Approach Khairan, Khairan; Mubaraq, Farhil; Maulydia, Nur Balqis; Awang, Khalijah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 2 (2025): September 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i2.312

Abstract

Antimicrobial resistance (AMR) poses an urgent global health concern, prompting the need for alternative therapeutic agents. This study evaluated the antimicrobial potential of ethyl acetate extracts from five medicinal plant species (Memecylon edule, Garcinia dioica, Syzygium sp., Memecylon caeruleum, and Aporosa octandra) collected from the geothermal Jaboi Crater in Aceh, Indonesia. Phytochemical profiling was performed using thin layer chromatography (TLC), and antimicrobial activity was assessed via TLC-bioautography against Escherichia coli, Staphylococcus aureus, and Candida albicans. The results revealed the presence of phenolic and terpenoid compounds, with antibacterial activity observed only against E. coli. No inhibition was detected against S. aureus or C. albicans. The study highlights the selective antimicrobial potential of geothermal plant extracts and underscores the relevance of bioautography as a rapid screening tool. While preliminary, these findings support further investigation into geothermal flora as a source of antibacterial compounds and call for advanced studies to isolate active constituents and explore their mechanisms of action.
Interpretable Machine Learning QSAR Models for Classification and Screening of VEGFR-2 Inhibitors in Anticancer Drug Discovery Noviandy, Teuku Rizky; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 2 (2025): September 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i2.339

Abstract

Cancer remains a major global health burden, with angiogenesis playing a central role in tumor growth and progression. Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a key mediator of angiogenesis and an attractive therapeutic target, but existing inhibitors are limited by reduced efficacy, toxicity, and resistance, creating a need for more effective predictive models in drug discovery. In this study, an interpretable machine learning based QSAR approach was developed using a curated dataset of 10,221 VEGFR-2 inhibitors from ChEMBL represented by 164 molecular descriptors. Four algorithms, kNN, AdaBoost, Random Forest, and XGBoost, were compared, and XGBoost achieved the best results with an accuracy of 83.67 percent, sensitivity of 91.38 percent, specificity of 71.73 percent, F1-score of 87.17 percent, and AUC of 0.9009. Model interpretation with LIME identified molecular descriptors related to hydrogen bonding, electrostatics, and lipophilicity as key contributors to activity. These results indicate that interpretable ensemble models can combine strong predictive performance with mechanistic insights, supporting rational design and optimization of novel VEGFR-2 inhibitors for anticancer therapy.
A Systematic Review on the Transformation of Bone Waste into Valuable Dental Biomaterials Diansari, Viona; Idroes, Rinaldi; Sunarso, Sunarso; Fitriyani, Sri
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.385

Abstract

Bone waste is a sustainable, calcium-rich resource for the production of hydroxyapatite (HA), a biomaterial widely used in dental and bone tissue engineering. This systematic review evaluates recent advances in the extraction, transformation, and biological performance of HA derived from bone waste. A total of 20 records were initially identified, of which 11 full-text articles met the eligibility criteria and were included in the qualitative synthesis. The reviewed studies demonstrate that bone waste can be effectively converted into HA through several routes, including thermal-based extraction (calcination, annealing, and sintering at 600–1000°C), alkaline hydrolysis, and hydrothermal or microwave-assisted methods, enabling the production of micro- and nano-sized HA with high purity. Post-extraction functionalization, such as ion doping (Mg²⁺, Na⁺, Co²⁺), drug loading, and composite formation, further enhances osteogenic, antimicrobial, and mechanical properties. Physicochemical characterization using XRD and FTIR consistently confirmed the formation of non-stoichiometric, ion-substituted HA with Ca/P ratios ranging from 1.6 to 1.9, closely resembling biogenic apatite. The presence of multiscale porosity (25–65%) and nano-scale features promotes protein adsorption, ion exchange, and cellular interactions. In vitro studies confirmed cytocompatibility, while ALP activity and mineralization assays demonstrated strong osteogenic potential. Overall, bone waste–derived HA offers biomimetic, functional, and environmentally sustainable alternatives for dental and maxillofacial applications.
QSAR Modeling of Beta-2 Adrenergic Receptor Ligands Using Molecular Descriptor–Based Machine Learning Noviandy, Teuku Rizky; Patwekar, Mohsina; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.394

Abstract

The Beta-2 Adrenergic Receptor (ADRB2) is a well-characterized G protein–coupled receptor widely studied in pharmacology and drug discovery. In this study, quantitative structure–activity relationship (QSAR) models were developed using molecular descriptor–based machine learning approaches to predict the activity of ADRB2 ligands. A curated dataset of 745 compounds with experimentally determined IC₅₀ values was obtained from the ChEMBL database. Two-dimensional molecular descriptors were calculated and preprocessed to remove low-variance and highly correlated features, resulting in a refined feature set for model development. The dataset was categorized into active and inactive compounds and divided into training and testing subsets. Four machine learning algorithms. Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Among the models, Random Forest achieved the best performance, with an accuracy of 89.26%, F1-score of 89.87%, and AUC of 0.926, followed by Gradient Boosting with an accuracy of 87.92% and AUC of 0.922. Analysis of physicochemical descriptors indicated that hydrogen-bond donor capacity (nHD) shows a statistically significant association with variations in compound activity toward ADRB2, while lipophilicity (LogP) and hydrogen-bond acceptor count (nHA) do not exhibit statistically significant differences between activity classes. Overall, the results demonstrate that molecular descriptor–based machine learning models, particularly ensemble methods, provide an effective framework for predicting ADRB2-related compound activity and support the prioritization of candidate molecules in computational drug discovery.
Co-Authors - Fakhrurrazi - Mahmud Abas, Abdul Hawil Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Agus Winarsih Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Akyuni, Qurrata Amirah, Kelsy Andri Yadi Paembonan Arini, Musfira Asep Rusyana Azhar, Fauzul Azharuddin Azharuddin BAKRI, TEDY KURNIAWAN Binawati Ginting Boy M Bachtiar Boy M. Bachtiar Claus Jacob Claus Jacob Claus Jacob, Claus Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova Diana S. Ningsih Diana Setya Ningsih, Diana Earlia, Nanda EKA SAFITRI Eka Safitri Eka Safitri El-Shazly, Mohamed Elisa Purwaendah Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Estevam, Ethiene Castellucci Ethiene Castellucci Estevam Eti Rohaeti Evi Yufita Ezzat, Abdelrahman O. Faddillah, Vira Faisal Abdullah Faisal, Farassa Rani Faradilla Faradilla FARADILLA, FARADILLA Farnida Farnida Fatimawali . Fauzi, Fazlin M. Fauzi, Fazlin Mohd Fazlin Mohd Fauzi Firaihanil Jannah Ghalieb Mutig Idroes Ghani, Azman Abdul Ghazi M. Idroes Ghazi Mauer Idroes Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hesti Meilina Hizir Sofyan Husdayanti, Noviana Ida Zahrina Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Ilham Maulana Ilham Maulana Imelda, Eva Imran Imran Ira Maya Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Ismail Ismail Isnaini, Nadia Isra Firmansyah, Isra Jannah, Firaihanil Jannah, Rizka Auliatul Kairupan, Tara S. Karl Herbert Schaefer Karl Herbert Schaefer, Karl Herbert Karomah, Alfi Hudatul Kemala, Pati Khairan . Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan KHAIRI SUHUD Khairi Suhud Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lelifajri Lelifajri Lelifajri Lelifajri Lubis, Vanizra F. M. Rafi M. Yogi Riyantama Isjoni Madya, Muhammad Miftahul Mahmudi Mahmudi Maimun Syukri, Maimun Malahayati Malahayati MARIA BINTANG Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur B. Maulydia, Nur Balqis Maysarah, Hilda Md Sani, Nor Diyana Mikyal Bulqiah, Mikyal Mirda, Erisna Misbullah, Alim Misrahanum Misrahanum Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Mubaraq, Farhil Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Diah Muhammad Ridha Adhari, Muhammad Ridha Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Munawar, Agus Murniana Murniana Mursal Mursal Mursyida, Waliam Musdalifah, Annisa Muslem Muslem Muslem Muslem, Muslem Muzakir N. Nazaruddin Nabila, Fiki Farah Nainggolan, Sarah Ika Nanda Earlia Nasrullah Idris Nasrullah Idris NAZARUDDIN NAZARUDDIN Nazaruddin Nazaruddin Neonufa, Godlief Frederick Ningsih, Diana S. Niode, Nurdjannah Jane Nor Diyana Md Sani Novi Reandy Sasmita Noviandy, Teuku R. Nugraha, Gartika Nur B. Maulydia Nur Balqis Maulydia Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Pati Kemala Patwekar, Faheem Patwekar, Mohsina Pebriani, Liska Nova Prakoeswa, Cita RS. Purwaendah, Elisa Putra, Noviandi I. Qurrata Akyuni Rahmadi Rahmadi Rahmadi Rahmadi Rahman, Isra Farliadi Rahman, Sunarti Abd Raihan Raihan Raihan Raihan, Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Ringga, Edi Saputra Rizka Auliatul Jannah Rizkia, Tatsa Romadhoni, Yenni Rusdi Andid Safhadi, Aulia Al-Jihad Saiful . Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Satrio, Justinus Septaningsih, Dewi Anggraini Shafira, Ghina A. Siti Aisyah Solly Aryza Souvia Rahimah Sri Fitriyani Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi Sunarso Sunarso Supriatno Supriatno Supriatno Suryadi Suryadi Suryawati Suryawati Taopik Ridwan Taufik Ridwan Taufiq Karma Teuku Rizky Noviandy Teuku Rizky Noviandy Teuku Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei Trina E. Tallei, Trina E. TRINA EKAWATI TALLEI Trina Ekawati Tallei Tuti Fadlilah Viona Diansari Widya FATRIASARI Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil