Prediction of Human β-secretase 1 (BACE-1) Inhibitors for Alzheimer Therapeutic Agent by Using Fingerprint-based Neural Network Optimized by Bat Algorithm

Nugroho, A.; Septiawan, Reza Rendian; Kurniawan, Isman

doi:10.1109/iccosite57641.2023.10127718

Cited by 1 publication

(1 citation statement)

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“…In the study by Nugroho et al [28], they employed a neural network model to classify beta-secretase 1 activity for a dataset comprising 1531 compounds. They employed three optimization strategies to optimize the neural network: the Bat Algorithm, the Hybrid Bat Algorithm, and the Adaptive Bat Algorithm.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%