QSAR-Based Stacked Ensemble Classifier for Hepatitis C NS5B Inhibitor Prediction

Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Irvanizam, Irvanizam; Subianto, Muhammad; Idroes, Rinaldi

doi:10.1109/cosite60233.2023.10250039

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…Hyperparameter tuning is a critical step in optimizing the performance of the BPNN model. This process involves adjusting various parameters that control the learning process and structure of the network [22]. In this study, we explored a range of values for several key hyperparameters to identify the optimal configuration for predicting Kovats retention indices of essential oil compounds.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

Safhadi,

Noviandy,

Irvanizam

et al. 2024

Infolitika J. Data Sci.

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The identification of chemical compounds in essential oils is crucial in industries such as pharmaceuticals, perfumery, and food. Kovats Retention Index (RI) values are essential for compound identification using gas chromatography-mass spectrometry (GC-MS). Traditional RI determination methods are time-consuming, labor-intensive, and susceptible to experimental variability. Recent advancements in data science suggest that artificial intelligence (AI) can enhance RI prediction accuracy and efficiency. However, the full potential of AI, particularly artificial neural networks (ANN), in predicting RI values remains underexplored. This study develops a backpropagation neural network (BPNN) model to predict the Kovats RI values of essential oil compounds using five molecular descriptors: ATSc1, VCH-7, SP-1, Kier1, and MLogP. We trained the BPNN on a dataset of 340 essential oil compounds and optimized it through hyperparameter tuning. We show that the optimized BPNN model, with an epoch count of 100, a learning rate of 0.1, a hidden layer size of 10 neurons, and the ReLU activation function, achieves an R² value of 0.934 and a Root Mean Squared Error (RMSE) of 76.98. These results indicate a high correlation between predicted and actual RI values and a low average prediction error. Our findings demonstrate that BPNNs can significantly improve the efficiency and accuracy of compound identification, reducing reliance on traditional experimental methods.

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Section: Hyperparameter Tuningmentioning

confidence: 99%

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

Safhadi,

Noviandy,

Irvanizam

et al. 2024

Infolitika J. Data Sci.

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“…In this study, we employed a stacked classifier approach to predict dropout risks and academic excellence. A stacked classifier is an ensemble learning method that combines the predictions of multiple base classifiers using a meta-classifier [29]. The goal is to leverage the strengths of different algorithms and create a more robust and accurate predictive model [30].…”

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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“…In recent years, there has been a significant leap forward in leveraging machine-learning techniques to address the challenges in drug discovery [10]- [12]. Machine-learning models can analyze vast chemical datasets, classify compound activities, and streamline the identification of potential drug candidates [13]- [16].…”

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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QSAR-Based Stacked Ensemble Classifier for Hepatitis C NS5B Inhibitor Prediction

Cited by 18 publications

References 29 publications

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

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

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

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