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All Journal Malacca Pharmaceutics Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Patwekar, Mohsina
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Immunology & microbiology Medicine & Pharmacology Physics

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Journal : Malacca Pharmaceutics

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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.
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 Estevam, Ethiene Castellucci Ghazi Mauer Idroes Harera, Cheariva Firsa Kemala, Pati Khairan Khairan Maulana, Aga Maulydia, Nur Balqis Maysarah, Hilda Mohd Fauzi, Fazlin Patwekar, Faheem Rinaldi Idroes Suhendra, Rivansyah Teuku Rizky Noviandy TRINA EKAWATI TALLEI