Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Emran, Talha Bin; Tallei, Trina Ekawati; Helwani, Zuchra; Idroes, Rinaldi

doi:10.60084/ijds.v1i1.91

Cited by 11 publications

(9 citation statements)

References 87 publications

(76 reference statements)

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“…Random Forest is an ensemble learning method that constructs multiple decision trees and combines their predictions. It is effective in handling high-dimensional data and reducing overfitting [33].…”

Section: Stacked Classifiermentioning

confidence: 99%

Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach

Noviandy,

Zahriah,

Yandri

et al. 2024

J. Educ. Manag. Learn.

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Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.

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Section: Stacked Classifiermentioning

confidence: 99%

Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach

Noviandy,

Zahriah,

Yandri

et al. 2024

J. Educ. Manag. Learn.

Self Cite

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“…To prepare the data for modeling, several necessary preprocessing steps [34,35] were conducted. The first step was to manually crop the region of interest (ROI) with a 1:1 aspect ratio on the AD wound area.…”

Section: Data Preparationmentioning

confidence: 99%

Evaluation of atopic dermatitis severity using artificial intelligence

Maulana,

Noviandy,

Suhendra

et al. 2023

Narra J

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Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.

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“…Classification of the inhibitory activity of compounds is crucial in drug discovery and development [7]. Accurate classification is essential to prioritize compounds for further investigation, ultimately leading to the identification of potential drug candidates [8].…”

Section: Introductionmentioning

confidence: 99%

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

Noviandy,

Nisa,

Idroes

et al. 2024

J. Comput. Theor. Appl.

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This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.

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Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Cited by 11 publications

References 87 publications

Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach

Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach

Evaluation of atopic dermatitis severity using artificial intelligence

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

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