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

Teuku Rizky Noviandy; Khairun Nisa; Ghalieb Mutig Idroes; Irsan Hardi; Novi Reandy Sasmita

doi:10.62411/jcta.10129

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

Teuku Rizky Noviandy, Khairun Nisa, Ghalieb Mutig Idroes, Irsan Hardi, Novi Reandy Sasmita

Abstract

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.

Keywords

ChEMBL; Machine-learning; Molecular descriptor; QSAR; Virtual screening

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References

A. Gholami, “Alzheimer’s disease: The role of proteins in formation, mechanisms, and new therapeutic approaches,” Neurosci. Lett., vol. 817, p. 137532, Nov. 2023, doi: 10.1016/j.neulet.2023.137532.

A. Gustavsson et al., “Global estimates on the number of persons across the Alzheimer’s disease continuum,” Alzheimer’s Dement., vol. 19, no. 2, pp. 658–670, Feb. 2023, doi: 10.1002/alz.12694.

Y. Wang, Y. Zhang, and E. Yu, “Targeted examination of amyloid beta and tau protein accumulation via positron emission tomography for the differential diagnosis of Alzheimer’s disease based on the A/T(N) research framework,” Clin. Neurol. Neurosurg., vol. 236, p. 108071, Jan. 2024, doi: 10.1016/j.clineuro.2023.108071.

R. Singh et al., “Classification of beta?site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods,” Chem. Biol. Drug Des., vol. 98, no. 6, pp. 1079–1097, Dec. 2021, doi: 10.1111/cbdd.13965.

K. S. Orobets and A. L. Karamyshev, “Amyloid Precursor Protein and Alzheimer’s Disease,” Int. J. Mol. Sci., vol. 24, no. 19, p. 14794, Sep. 2023, doi: 10.3390/ijms241914794.

I. Hajdú, B. M. Végh, A. Szilágyi, and P. Závodszky, “Beta-Secretase 1 Recruits Amyloid-Beta Precursor Protein to ROCK2 Kinase, Resulting in Erroneous Phosphorylation and Beta-Amyloid Plaque Formation,” Int. J. Mol. Sci., vol. 24, no. 13, p. 10416, Jun. 2023, doi: 10.3390/ijms241310416.

T. R. Noviandy et al., “Ensemble Machine learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review,” Infolitika J. Data Sci., vol. 1, no. 1, pp. 32–41, Sep. 2023, doi: 10.60084/ijds.v1i1.91.

T. R. Noviandy et al., “Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery,” Malacca Pharm., vol. 1, no. 2, pp. 48–54, 2023, doi: 10.60084/mp.v1i2.60.

K. Roy and S. Kar, “How to Judge Predictive Quality of Classification and Regression Based QSAR Models?,” Z. Ul-Haq and J. D. B. T.-F. in C. C. Madura, Eds. Bentham Science Publishers, 2015, pp. 71–120. doi: https://doi.org/10.1016/B978-1-60805-979-9.50003-2.

T. R. Noviandy, A. Maulana, G. M. Idroes, I. Irvanizam, M. Subianto, and R. Idroes, “QSAR-Based Stacked Ensemble Classifier for Hepatitis C NS5B Inhibitor Prediction,” in 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), Aug. 2023, pp. 220–225. doi: 10.1109/COSITE60233.2023.10250039.

M. Azizah, A. Yanuar, and F. Firdayani, “Dimensional Reduction of QSAR Features Using a Machine learning Approach on the SARS-Cov-2 Inhibitor Database,” J. Penelit. Pendidik. IPA, vol. 8, no. 6, pp. 3095–3101, Dec. 2022, doi: 10.29303/jppipa.v8i6.2432.

K. Mondal and S. K. S, “QSAR Classification Models for Predicting 3CLPro-protease Inhibitor Activity,” in 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2021, pp. 1–6. doi: 10.1109/GUCON50781.2021.9573896.

N. B. Maulydia, K. Khairan, and T. R. Noviandy, “Prediction of Pharmacokinetic Parameters from Ethanolic Extract Mane Leaves (Vitex pinnata L.) in Geothermal Manifestation of Seulawah Agam Ie-Seu’um, Aceh,” Malacca Pharm., vol. 1, no. 1, pp. 16–21, Jun. 2023, doi: 10.60084/mp.v1i1.33.

M. Agustia et al., “Application of Fuzzy Support Vector Regression to Predict the Kovats Retention Indices of Flavors and Fragrances,” in 2022 International Conference on Electrical Engineering and Informatics (ICELTICs), Sep. 2022, pp. 13–18. doi: 10.1109/ICELTICs56128.2022.9932124.

R. Idroes et al., “Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index,” Int. Rev. Model. Simulations, vol. 14, no. 2, p. 137, 2021.

T. R. Noviandy et al., “The Prediction of Kovats Retention Indices of Essential Oils at Gas Chromatography Using Genetic Algorithm-Multiple Linear Regression and Support Vector Regression,” J. Eng. Sci. Technol., vol. 17, no. 1, pp. 306–326, 2022.

S. Simeon et al., “Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking,” PeerJ, vol. 4, p. e2322, Aug. 2016, doi: 10.7717/peerj.2322.

M. Abdullahi, G. A. Shallangwa, and A. Uzairu, “In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype,” Beni-Suef Univ. J. Basic Appl. Sci., vol. 9, no. 1, p. 2, Dec. 2020, doi: 10.1186/s43088-019-0023-y.

T. R. Noviandy, A. Maulana, T. B. Emran, G. M. Idroes, and R. Idroes, “QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine learning Algorithms,” Heca J. Appl. Sci., vol. 1, no. 1, pp. 1–7, 2023, doi: 10.60084/hjas.v1i1.12.

I. Ponzoni et al., “QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease,” Sci. Rep., vol. 9, no. 1, p. 9102, Jun. 2019, doi: 10.1038/s41598-019-45522-3.

A. Fernández-Torras, A. Comajuncosa-Creus, M. Duran-Frigola, and P. Aloy, “Connecting chemistry and biology through molecular descriptors,” Curr. Opin. Chem. Biol., vol. 66, p. 102090, Feb. 2022, doi: 10.1016/j.cbpa.2021.09.001.

G. Ke et al., “Lightgbm: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 30, 2017.

T. R. Noviandy, S. I. Nainggolan, R. Raihan, I. Firmansyah, and R. Idroes, “Maternal Health Risk Detection Using Light Gradient Boosting Machine Approach,” Infolitika J. Data Sci., vol. 1, no. 2, pp. 48–55, Dec. 2023, doi: 10.60084/ijds.v1i2.123.

L. Sari, A. Romadloni, R. Lityaningrum, and H. D. Hastuti, “Implementation of LightGBM and Random Forest in Potential Customer Classification,” TIERS Inf. Technol. J., vol. 4, no. 1, pp. 43–55, Jun. 2023, doi: 10.38043/tiers.v4i1.4355.

P. Kumari, A. Nath, and R. Chaube, “Identification of human drug targets using machine-learning algorithms,” Comput. Biol. Med., vol. 56, pp. 175–181, Jan. 2015, doi: 10.1016/j.compbiomed.2014.11.008.

G. B. McGaughey and M. K. Holloway, “Structure-guided design of ?-secretase (BACE-1) inhibitors,” Expert Opin. Drug Discov., vol. 2, no. 8, pp. 1129–1138, Aug. 2007, doi: 10.1517/17460441.2.8.1129.

I. Ponzoni et al., “QSAR classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer’s disease,” Sci. Rep., vol. 9, no. 1, pp. 1–13, 2019.

A. F. Nugroho, R. Rendian Septiawan, and I. Kurniawan, “Prediction of Human ?-secretase 1 (BACE-1) Inhibitors for Alzheimer Therapeutic Agent by Using Fingerprint-based Neural Network Optimized by Bat Algorithm,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Feb. 2023, pp. 257–261. doi: 10.1109/ICCoSITE57641.2023.10127718.

A. Gaulton et al., “ChEMBL: a large-scale bioactivity database for drug discovery,” Nucleic Acids Res., vol. 40, no. D1, pp. D1100–D1107, Jan. 2012, doi: 10.1093/nar/gkr777.

S. Simeon et al., “Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking,” PeerJ, vol. 4, p. e2322, 2016.

H. Moriwaki, Y.-S. Tian, N. Kawashita, and T. Takagi, “Mordred: a molecular descriptor calculator,” J. Cheminform., vol. 10, no. 1, p. 4, Dec. 2018, doi: 10.1186/s13321-018-0258-y.

A. Mauri, V. Consonni, and R. Todeschini, “Molecular Descriptors,” in Handbook of Computational Chemistry, Cham: Springer International Publishing, 2017, pp. 2065–2093. doi: 10.1007/978-3-319-27282-5_51.

T. Yu, C. Nantasenamat, S. Kachenton, N. Anuwongcharoen, and T. Piacham, “Cheminformatic Analysis and Machine learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer,” ACS Omega, vol. 8, no. 7, pp. 6729–6742, Feb. 2023, doi: 10.1021/acsomega.2c07346.

DOI: https://doi.org/10.62411/jcta.10129

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Journal of Computing Theories and Applications published by Dian Nuswantoro University, Semarang, Indonesia collaborates with the Intelligent System (IntSys) Research and is supported by Future Techno Science Foundation, Indonesia.

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