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

doi:10.60084/mp.v1i2.60

Cited by 22 publications

(16 citation statements)

References 34 publications

(34 reference statements)

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“…To predict the Kovats retention index for each compound, it is necessary to calculate molecular descriptors. Molecular descriptors are quantifiable properties of molecules that can be used to predict chemical behavior, including structural, electronic, and hydrophobic characteristics [16,17]. The calculation of these descriptors is conducted through the Online Chemical Modelling Environment [18].…”

Section: Datasetmentioning

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: Datasetmentioning

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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“…Finally, the preprocessed dataset was split into training and testing subsets in an 80-20 ratio. This allowed us to train the ensemble voting classifier model on a substantial portion of the data while reserving a separate portion for model validation and testing, ensuring its ability to generalize to unseen data [17].…”

Section: Data Preprocessingmentioning

confidence: 99%

Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers

Supriatna,

Saputra,

Hasan

2023

Infolitika J. Data Sci.

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This study introduces an ensemble voting classifier for red wine quality classification using machine learning algorithms. Wine quality assessment, traditionally reliant on subjective expert evaluations, is addressed through data-driven methodologies. The dataset comprises physicochemical attributes and quality ratings of red wines. Results reveal individual models with accuracy ranging from 0.816 to 0.873, while the ensemble approach significantly enhances accuracy. The combination of Random Forest and XGBoost achieves an accuracy of 0.885, demonstrating its potential in red wine quality assessment. In conclusion, this study showcases the potential of machine learning in enhancing the classification of red wine quality, offering a more objective and precise alternative to traditional sensory evaluation. The ensemble voting classifier, especially when combining Random Forest and XGBoost, provides a robust solution for this task, improving the accuracy of wine quality assessments.

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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]. However, the classification challenge is non-trivial, given the extensive chemical diversity of potential inhibitors and the need to achieve a delicate sensitivity-specificity balance [9].…”

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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Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

Cited by 22 publications

References 34 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

Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers

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

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