Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery
DOI:
https://doi.org/10.60084/mp.v2i2.217Keywords:
Inflammatory diseases, Inhibitor screening, Molecular descriptors, Pharmacoinformatics, Supervised learningAbstract
Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.
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References
- Bennett, J. M., Reeves, G., Billman, G. E., and Sturmberg, J. P. (2018). Inflammation–Nature’s Way to Efficiently Respond to All Types of Challenges: Implications for Understanding and Managing “the Epidemic” of Chronic Diseases, Frontiers in Medicine, Vol. 5. doi:10.3389/fmed.2018.00316.
- Campanati, A., Marani, A., Martina, E., Diotallevi, F., Radi, G., and Offidani, A. (2021). Psoriasis as an Immune-Mediated and Inflammatory Systemic Disease: From Pathophysiology to Novel Therapeutic Approaches, Biomedicines, Vol. 9, No. 11, 1511. doi:10.3390/biomedicines9111511.
- He, R., Chen, Y., and Cai, Q. (2020). The Role of the LTB4-BLT1 Axis in Health and Disease, Pharmacological Research, Vol. 158, 104857. doi:10.1016/j.phrs.2020.104857.
- Brandt, S. L., and Serezani, C. H. (2017). Too Much of a Good Thing: How Modulating Ltb 4 Actions Restore Host Defense in Homeostasis or Disease, Seminars in Immunology, Vol. 33, 37–43. doi:10.1016/j.smim.2017.08.006.
- Haeggström, J. Z. (2004). Leukotriene A4 Hydrolase/Aminopeptidase, the Gatekeeper of Chemotactic Leukotriene B4 Biosynthesis, Journal of Biological Chemistry, Vol. 279, No. 49, 50639–50642. doi:10.1074/jbc.R400027200.
- Röhn, T. A., Numao, S., Otto, H., Loesche, C., and Thoma, G. (2021). Drug Discovery Strategies for Novel Leukotriene A4 Hydrolase Inhibitors, Expert Opinion on Drug Discovery, Vol. 16, No. 12, 1483–1495. doi:10.1080/17460441.2021.1948998.
- Qin, R., Wang, H., and Yan, A. (2021). Classification and QSAR Models of Leukotriene A4 Hydrolase (LTA4H) Inhibitors by Machine Learning Methods, SAR and QSAR in Environmental Research, Vol. 32, No. 5, 411–431. doi:10.1080/1062936X.2021.1910862.
- Li, X., Xie, M., Lu, C., Mao, J., Cao, Y., Yang, Y., Wei, Y., Liu, X., Cao, S., Song, Y., Peng, J., Zhou, Y., Jiang, Q., Lin, G., Qin, S., Qi, M., Hou, M., Liu, X., Zhou, H., Yang, G., and Yang, C. (2020). Design and Synthesis of Leukotriene A4 Hydrolase Inhibitors to Alleviate Idiopathic Pulmonary Fibrosis and Acute Lung Injury, European Journal of Medicinal Chemistry, Vol. 203, 112614. doi:10.1016/j.ejmech.2020.112614.
- Wang, Z., and Yang, B. (2022). Polypharmacology in Clinical Applications—Anti-inflammation Polypharmacology, Polypharmacology, Springer International Publishing, Cham, 375–396. doi:10.1007/978-3-031-04998-9_11.
- Berdigaliyev, N., and Aljofan, M. (2020). An Overview of Drug Discovery and Development, Future Medicinal Chemistry, Vol. 12, No. 10, 939–947. doi:10.4155/fmc-2019-0307.
- Batool, M., Ahmad, B., and Choi, S. (2019). A Structure-Based Drug Discovery Paradigm, International Journal of Molecular Sciences, Vol. 20, No. 11, 2783. doi:10.3390/ijms20112783.
- Bano, I., Butt, U. D., and Mohsan, S. A. H. (2023). New Challenges in Drug Discovery, Novel Platforms for Drug Delivery Applications, Elsevier, 619–643. doi:10.1016/B978-0-323-91376-8.00021-5.
- Satpathy, R. (2024). Artificial Intelligence Techniques in the Classification and Screening of Compounds in Computer‐Aided Drug Design (CADD) Process, Artificial Intelligence and Machine Learning in Drug Design and Development, Wiley, 473–497. doi:10.1002/9781394234196.ch15.
- Lanne, A., Usselmann, L. E. J., Llowarch, P., Michaelides, I. N., Fillmore, M., and Holdgate, G. A. (2023). A Perspective on the Changing Landscape of Hts, Drug Discovery Today, Vol. 28, No. 8, 103670. doi:10.1016/j.drudis.2023.103670.
- Noviandy, T. R., Maulana, A., Emran, T. B., Idroes, G. M., and Idroes, R. (2023). QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine Learning Algorithms, Heca Journal of Applied Sciences, Vol. 1, No. 1, 1–7. doi:10.60084/hjas.v1i1.12.
- Khan, M. B., Shahrior, R., Asha, R. T., and Saha, P. S. (2021). Predicting AXL Inhibition of Chemicals using Molecular Descriptors and Machine Learning Methods, 2021 5th International Conference on Electrical Information and Communication Technology (EICT), IEEE, 1–6. doi:10.1109/EICT54103.2021.9733504.
- Noviandy, T. R., Maulana, A., Idroes, G. M., Emran, T. B., Tallei, T. E., Helwani, Z., and Idroes, R. (2023). Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review, Infolitika Journal of Data Science, Vol. 1, No. 1, 32–41. doi:10.60084/ijds.v1i1.91.
- Supriatna, D. J. I., Saputra, H., and Hasan, K. (2023). Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers, Infolitika Journal of Data Science, Vol. 1, No. 2, 42–47. doi:10.60084/ijds.v1i2.95.
- Noviandy, T. R., Nainggolan, S. I., Raihan, R., Firmansyah, I., and Idroes, R. (2023). Maternal Health Risk Detection Using Light Gradient Boosting Machine Approach, Infolitika Journal of Data Science, Vol. 1, No. 2, 48–55. doi:10.60084/ijds.v1i2.123.
- Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., and Overington, J. P. (2012). ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery, Nucleic Acids Research, Vol. 40, No. D1, D1100–D1107. doi:10.1093/nar/gkr777.
- Thakur, A., Kumar, A., Sharma, V., and Mehta, V. (2022). PIC50: An open source tool for interconversion of PIC50 values and IC50 for efficient data representation and analysis, BioRxiv, 2022.10.15.512366. doi:10.1101/2022.10.15.512366.
- Yu, T., Nantasenamat, C., Kachenton, S., Anuwongcharoen, N., and Piacham, T. (2023). Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer, ACS Omega, Vol. 8, No. 7, 6729–6742. doi:10.1021/acsomega.2c07346.
- Gaspar, H. A., Baskin, I. I., and Varnek, A. (2016). Visualization of a Multidimensional Descriptor Space, 243–267. doi:10.1021/bk-2016-1222.ch012.
- Noviandy, T. R., Idroes, G. M., and Hardi, I. (2024). An Interpretable Machine Learning Strategy for Antimalarial Drug Discovery with LightGBM and SHAP, Journal of Future Artificial Intelligence and Technologies, Vol. 1, No. 2, 84–95. doi:10.62411/faith.2024-16.
- Chen, X., Li, H., Tian, L., Li, Q., Luo, J., and Zhang, Y. (2020). Analysis of the Physicochemical Properties of Acaricides Based on Lipinski’s Rule of Five, Journal of Computational Biology, Vol. 27, No. 9, 1397–1406. doi:10.1089/cmb.2019.0323.
- Aqeel, I., Bilal, M., Majid, A., and Majid, T. (2022). Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease, Pharmaceuticals, Vol. 15, No. 11, 1333. doi:10.3390/ph15111333.
- Moriwaki, H., Tian, Y. S., Kawashita, N., and Takagi, T. (2018). Mordred: A Molecular Descriptor Calculator, Journal of Cheminformatics, Vol. 10, No. 1, 1–14. doi:10.1186/s13321-018-0258-y.
- Noviandy, T. R., Maulana, A., Idroes, G. M., Irvanizam, I., Subianto, M., and Idroes, R. (2023). QSAR-Based Stacked Ensemble Classifier for Hepatitis C NS5B Inhibitor Prediction, 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), IEEE, 220–225. doi:10.1109/COSITE60233.2023.10250039.
- Noviandy, T. R., Idroes, G. M., and Hardi, I. (2024). Machine Learning Approach to Predict AXL Kinase Inhibitor Activity for Cancer Drug Discovery Using XGBoost and Bayesian Optimization, Journal of Soft Computing and Data Mining, Vol. 5, No. 1, 46–56.
- Noviandy, T. R., Maulana, A., Idroes, G. M., Maulydia, N. B., Patwekar, M., Suhendra, R., and Idroes, R. (2023). Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer’s Disease Drug Discovery, Malacca Pharmaceutics, Vol. 1, No. 2, 48–54. doi:10.60084/mp.v1i2.60.
- Idroes, R., Noviandy, T. R., Maulana, A., Suhendra, R., and Sasmita, N. R. (2023). ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography, Infolitika Journal of Data Science, Vol. 1, No. 1, 1–14. doi:10.60084/ijds.v1i1.73.
- Noviandy, T. R., Idroes, G. M., Hardi, I., Afjal, M., and Ray, S. (2024). A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry, Infolitika Journal of Data Science, Vol. 2, No. 1, 34–44. doi:10.60084/ijds.v2i1.199.
- Sari, L., Romadloni, A., Lityaningrum, R., and Hastuti, H. D. (2023). Implementation of LightGBM and Random Forest in Potential Customer Classification, TIERS Information Technology Journal, Vol. 4, No. 1, 43–55. doi:10.38043/tiers.v4i1.4355.
- Suhendra, R., Husdayanti, N., Suryadi, S., Juliwardi, I., Sanusi, S., Ridho, A., Ardiansyah, M., Murhaban, M., and Ikhsan, I. (2023). Cardiovascular Disease Prediction Using Gradient Boosting Classifier, Infolitika Journal of Data Science, Vol. 1, No. 2, 56–62. doi:10.60084/ijds.v1i2.131.
- Noviandy, T. R., Idroes, G. M., and Hardi, I. (2024). Enhancing Loan Approval Decision-Making: An Interpretable Machine Learning Approach Using LightGBM for Digital Economy Development, Malaysian Journal of Computing (MJOC), Vol. 9, No. 1, 1734–1745. doi:10.24191/mjoc.v9i1.25691.
- Gupta, N. S., Mohta, Y., Heda, K., Armaan, R., Valarmathi, B., and Arulkumaran, G. (2023). Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis, Journal of Environmental and Public Health, Vol. 2023, 1–26. doi:10.1155/2023/4916267.
- Srisongkram, T., and Weerapreeyakul, N. (2022). Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study, International Journal of Molecular Sciences, Vol. 24, No. 1, 669. doi:10.3390/ijms24010669.
- Safriandono, A. N., Setiadi, D. R. I. M., Dahlan, A., Rahmanti, F. Z., Wibisono, I. S., and Ojugo, A. A. (2024). Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification, Journal of Future Artificial Intelligence and Technologies, Vol. 1, No. 1, 51–63. doi:10.62411/faith.2024-12.
- Mienye, I. D., and Sun, Y. (2022). A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects, IEEE Access, Vol. 10, 99129–99149. doi:10.1109/ACCESS.2022.3207287.
- Idroes, G. M., Noviandy, T. R., Maulana, A., Zahriah, Z., Suhendrayatna, S., Suhartono, E., Khairan, K., Kusumo, F., Helwani, Z., and Abd Rahman, S. (2023). Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring, Leuser Journal of Environmental Studies, Vol. 1, No. 2, 62–68. doi:10.60084/ljes.v1i2.99.
- Noviandy, T. R., Nisa, K., Idroes, G. M., Hardi, I., and Sasmita, N. R. (2024). Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM, Journal of Computing Theories and Applications, Vol. 2, No. 2, 138–147. doi:10.62411/jcta.10129.
- Tharwat, A. (2021). Classification Assessment Methods, Applied Computing and Informatics, Vol. 17, No. 1, 168–192. doi:10.1016/j.aci.2018.08.003.
- Cook, J., and Ramadas, V. (2020). When to Consult Precision-Recall Curves, The Stata Journal: Promoting Communications on Statistics and Stata, Vol. 20, No. 1, 131–148. doi:10.1177/1536867X20909693.
- Zhou, Z., and Hooker, G. (2021). Unbiased Measurement of Feature Importance in Tree-Based Methods, ACM Transactions on Knowledge Discovery from Data, Vol. 15, No. 2, 1–21. doi:10.1145/3429445.
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Copyright (c) 2024 Teuku Rizky Noviandy, Ghifari Maulana Idroes, Fazlin Mohd Fauzi, Rinaldi Idroes
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