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All Journal HAYATI Journal of Biosciences Bulletin of Chemical Reaction Engineering & Catalysis INDONESIAN JOURNAL OF PHARMACY Indonesian Journal of Chemistry Unnes Journal of Public Health Narra J International Journal of Hydrological and Environmental for Sustainability 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
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Journal : Malacca Pharmaceutics

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Ficus minahassae (Teijsm. & de Vriese) Miq.: A Fig Full of Health Benefits from North Sulawesi, Indonesia: A Mini Review Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi; Fatimawali, Fatimawali
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.24

Abstract

Plants have been widely utilized as traditional medicine for an extended period of time. Numerous traditional remedies have demonstrated inherent anti-disease properties. Among the countries that extensively rely on traditional medicine, Indonesia stands out. Within the region of North Sulawesi, Indonesia, Ficus minahassae, an indigenous plant possessing several health benefits, is utilized by the local community as a traditional medicinal resource. This plant is employed for the treatment of various ailments such as rheumatism, physical discomfort, stimulation of lactation in breastfeeding women, bruises, relapse, fever, fatigue, migraines, bodily pain, headaches, convulsions, colds, coughs, influenza, and fractures. Typically, the leaves, roots, and stems of F. minahassae are boiled and consumed. Additionally, this plant has been reported to possess antibacterial and antioxidant properties. However, scientific investigations exploring the health advantages of F. minahassae are significantly limited in comparison to other traditional medicines. Consequently, it is highly recommended to conduct further research on the health benefits associated with this plant.
Antibacterial Activity of n-Hexane Dragon’s Blood Resin Extract (Daemonorops draco wild Blume) from Bener Meriah, Aceh Province, Indonesia Khairan, Khairan; Arini, Musfira; Idroes, Rinaldi; Awang, Khalijah; Jacob, Claus
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.29

Abstract

The dragon’s blood resin (Daemonorops draco wild Blume) has been used in folk medicine for pharmacological activities such as antimicrobial, antivirus, anti-inflammation, gastrointestinal disorders, blood circulation dysfunctions, antitumor, and cancer. This study was designated to evaluate the antibacterial activity of n-Hexane dragon’s blood resin extract against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans 10231. The other purpose of this study was to determine the secondary metabolites compound of n-Hexane dragon’s blood resin extract. The antimicrobial activities of the n-Hexane dragon’s blood resin extract was determined using well diffusion method and the results showed that the extract at concentration of 15% exhibited antimicrobial activities against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans 10231 with the diameter inhibition of 13.20 mm; 21.3 mm; and 13.0 mm respectively. The phytochemicals screening showed that the extract contains secondary metabolites in the form of flavonoids. The GC-MS analysis showed that n-Hexane dragon’s blood resin extract contains 48 chemicals compounds, and the compound at RT 26 was indicated a Drachorhodin compound (C17H 18O3) with the mass ration of m/z was 270 g/mol. Overall, the n-Hexane dragon’s blood resin extract be a good choice for antimicrobial agent against bacteria and fungi.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.60

Abstract

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.
The Potent Antimicrobial Spectrum of Patchouli: Systematic Review of Its Antifungal, Antibacterial, and Antiviral Properties Kemala, Pati; Idroes, Rinaldi; Khairan, Khairan; Ramli, Muliadi; Tallei, Trina Ekawati; Helwani, Zuchra; Rahman, Sunarti Abd
Malacca Pharmaceutics Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i1.156

Abstract

ntention towards natural essential oils from medicinal plants has increased rapidly over the past decade as these oils have antimicrobial and antioxidant properties against various chronic diseases. One essential oil source with antimicrobial properties is the essential oil from Pogostemon cablin (Blanco) Benth. This review aims to provide information on using patchouli oil as an antimicrobial against bacterial, fungal, and viral pathogens in the last five years. There were 37 articles found in the PUBMED database by June 15, 2023. After searching, 6 of them were duplicates. A total of 2 papers were inaccessible, 4 were not research articles, and five were excluded because they were irrelevant to the scope of this study. This review shows that research related to patchouli as an antimicrobial in the last five years involves Pogostemon cablin leaf samples as silver nanoparticle bioreductors. Patchouli oil is used in membrane, nanocomposite film, and starch hydrogel manufacturing. Patchouli oil is a prestigious antimicrobial agent because it can fight numerous pathogenic microbes from bacteria, fungi, and viruses.
Exploring the Medicinal Potential of Blumea balsamifera: Insights from Molecular Docking and Molecular Dynamics Simulations Analyses Maulydia, Nur Balqis; Khairan, Khairan; Tallei, Trina Ekawati; Salaswati, Salaswati; Musdalifah, Annisa; Nabila, Fiki Farah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i1.168

Abstract

Blumea balsamifera from the Ie-Jue geothermal area in Aceh Province, Indonesia, has been reported to have a variety of secondary metabolites. However, there is limited information about the activity of these chemical metabolites from B. balsamifera. The aim of this study is to evaluate the therapeutic potential of these compounds using molecular docking and molecular dynamics simulations. Six selective compounds were thoroughly evaluated using molecular docking techniques for their inhibitory effects on both Coronavirus protease and human interleukin receptors. Additionally, druglikeness assessments based on the Lipinski rule of five were performed to evaluate these six ligands. Our results show that stigmasterol, a key component of B. balsamifera, has demonstrated low binding free energy values across four receptors. Furthermore, molecular dynamics simulations confirmed the stability of the top ligand-receptor complex, particularly stigmasterol-1IRA, based on five parameters, indicating its stability as an inhibitor. This research highlights the potential of stigmasterol as a therapeutic agent derived from medicinal plants of B. balsamifera and underscores the value of our molecular approach in identifying opportunities for pharmaceutical development.
Hybrid Handwash with Silver Nanoparticles from Calotropis gigantea Leaves and Patchouli Oil: Development and Properties Salsabila, Indah; Khairan, Khairan; Kemala, Pati; Idroes, Ghifari Maulana; Isnaini, Nadia; Maulydia, Nur Balqis; El-Shazly, Mohamed; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.206

Abstract

When washing hands, handwashing is one way to prevent diseases caused by bacteria such as Staphylococcus aureus and Escherichia coli, the most common bacteria that can cause infections. The production of handwash utilizing silver nanoparticles as an active antibacterial agent remains a relatively infrequent practice. The synthesis of silver nanoparticles from the leaves of Calotropis gigantea, which grows in the geothermal area of Ie Seu-um Aceh Besar, has been carried out using the green synthesis method and hybrid green synthesis with patchouli oil. Handwash with active ingredients such as silver nanoparticles was successfully formulated, evaluated, and tested against S. aureus and E. coli. The organoleptic characteristics, pH, viscosity, foam height measurements, density, irritation, and antibacterial activity against S. aureus and E. coli were evaluated. The results showed that the organoleptic properties of the handwash with silver nanoparticles were not changed during a 30-day storage period, with pH values in the range of 9.7-10.3, and did not cause irritation upon using silver nanoparticle handwash. The best formula for handwashing with silver nanoparticles in inhibiting the growth of S. aureus and E. coli bacteria was F2, with inhibition zones of 12.9 ± 2.85 mm and 10.95 ± 0.8 mm, respectively. The formulated handwash with silver nanoparticles met the requirements of good liquid soap according to the Indonesian National Standard (SNI) with potent antibacterial activity.
Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Mohd Fauzi, Fazlin; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.217

Abstract

Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.
QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Tallei, Trina Ekawati; Handayani, Dian; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.226

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.
Bioactive Phytochemicals from Memecylon edule: Targeting Planktonic and Biofilm States of Pseudomonas aeruginosa Suryawati, Suryawati; Idroes, Rinaldi; Hertiani, Triana; Khairan, Khairan
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.258

Abstract

The rise in antimicrobial resistance has prompted the search for potent antimicrobial agents, with plants from unique environments, such as geothermal areas, offering potential due to their diverse phytochemical profiles. This study evaluated the antiplanktonic and antibiofilm activity of M. edule fractions from the geothermal area, Jaboi, Sabang. Crude extracts of M. edule were sequentially fractionated using hexane, ethyl acetate, and ethanol. The resulting fractions were further analyzed for their phytochemical content and antiplanktonic and antibiofilm activities. The ethyl acetate fraction demonstrated superior antiplanktonic and antibiofilm activities compared to other fractions, inhibiting 50% of Pseudomonas aeruginosa planktonic and biofilm formation at a concentration of 10 mg/mL. The most potent fraction exhibited the phenolic content, 672.84 mg GAE/g extract, surpassing the other fractions. The GC-MS analysis shows the presence of pyrogallol, hexadecanoic acid, cedran-diol, and sitosterol. These findings highlight the potential of the ethyl acetate fraction of M. edule as a source of bioactive compounds with promising antiplanktonic and antibiofilm properties, laying the groundwork for future research into its therapeutic applications against biofilm-associated infections.
Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma Noviandy, Teuku Rizky; Imelda, Eva; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.271

Abstract

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play a crucial role in this treatment by reducing aqueous humor production. However, existing CA-II inhibitors often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient and targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict CA-II inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple machine learning models, including Support Vector Machine, Gradient Boosting, and Random Forest, we identify SVM as the most effective classifier, achieving the highest accuracy (83.70%) and F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling and hyperparameter optimization. Our findings underscore the potential of machine learning-based virtual screening in accelerating CA-II inhibitor identification and advocate for integrating AI-driven approaches with traditional drug discovery techniques. Future directions include deep learning enhancements and hybrid machine learning-docking frameworks to improve prediction accuracy and facilitate the development of more potent and selective glaucoma treatments.
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 Claus Jacob Claus Jacob Claus Jacob, Claus Cundaningsih, Nurvita Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova 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 Mauer Idroes Haerul Anwar Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hartono Hartono 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 Jasin, Faisal M 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 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 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 Balqis Maulydia Nur, Adrian Rahmat Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Patwekar, Faheem Patwekar, Mohsina 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 Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi Supriatno Supriatno Supriatno Suryadi Suryadi Suryawati Suryawati Taopik Ridwan Taufik Ridwan Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei, Trina E. TRINA EKAWATI TALLEI Trina Ekawati Tallei Tuti Fadlilah Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil