ABSTRACT:
Identifying and classifying dopamine D2 receptor agonists and antagonists is essential for the drug discovery and development. In this study, we employed machine learning algorithms, namely, XGBoost, LGBM, ExtraTree, and AdaBoost Classifier, in combination with RDKit molecular descriptors, to classify dopamine D2 receptor ligands. The dataset consisted of 195 molecules, comprising 69 dopamine agonists and 126 dopamine antagonists. The models were trained using 75% of the dataset and evaluated on the remaining 25%. The classifiers demonstrated high accuracy and F1 scores, with the AdaBoost Classifier achieving the highest accuracy of 92%. Receiver operating characteristic (ROC) analysis further confirmed the robustness of the model, as indicated by the area under the curve (AUC) values. The AUC values for the AdaBoost, Extra Tree, LGBM, and XGB classifiers were 0.92, 0.90, 0.87, and 0.89, respectively. Feature selection analysis revealed the important molecular descriptors that significantly contribute to the classification models. The ExtraTree classifier selected the highest number of descriptors (167), while the intersection of the selected descriptors among all models indicated 24 common features that crucial for classification. Classification of external compounds using the developed models revealed that sinedabet was classified as a dopamine D2 receptor antagonist, while lisuride, ropinirole, and quinpirole were classified as dopamine D2 receptor agonists.
Cite this article:
Suprapto Suprapto, Yatim Lailun Ni’mah. Classification of Dopamine D2 receptor ligands using RDKit Molecular descriptors and Machine Learning Algorithms. Research Journal of Pharmacy and Technology. 2024; 17(9):4507-4. doi: 10.52711/0974-360X.2024.00697
Cite(Electronic):
Suprapto Suprapto, Yatim Lailun Ni’mah. Classification of Dopamine D2 receptor ligands using RDKit Molecular descriptors and Machine Learning Algorithms. Research Journal of Pharmacy and Technology. 2024; 17(9):4507-4. doi: 10.52711/0974-360X.2024.00697 Available on: https://rjptonline.org/AbstractView.aspx?PID=2024-17-9-58
REFERENCES:
1. Im D. Inoue A. Fujiwara T. Nakane T. Yamanaka Y. Uemura T. Mori C. Shiimura Y. Kimura KT. Asada H. Nomura N. Tanaka T. Yamashita A. Nango E. Tono K. Kadji FMN. Aoki J. Iwata S. Shimamura T. Structure of the Dopamine D2 Receptor in Complex with the Antipsychotic Drug Spiperone. Nat Commun. 2020; 11(1): 6442. https://doi.org/10.1038/s41467-020-20221-0.
2. Bueschbell B. Barreto CAV. Preto AJ. Schiedel AC. Moreira IS. A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods. Molecules. 2019; 24(7): 1196. https://doi.org/10.3390/molecules24071196.
3. Bhargava K. Nath R. Seth PK. Pant KK. Dixit RK. Molecular Docking Studies of D2 Dopamine Receptor with Risperidone Derivatives. Bioinformation. 2014; 10(1): 8–12. https://doi.org/10.6026/97320630010008.
4. Raghubabu K., Jagannadharao V., Ramu BK. Assay of Ropinirole hydrochloride in Pharmaceutical Preparations by Visible Spectrophotometry. Asian J. Pharm. Ana. 2012; 2(2): 41-45.
5. Choi J. Horner KA. 2022. Dopamine Agonists. In StatPearls StatPearls Publishing: Treasure Island FL.
6. Yamuna M. Elakkiya A. Mathematical Models in Drug Discovery, Development and Treatment of Various Diseases – A Case Study. Research J. Pharm. and Tech. 2017; 10(12): 4397-4401. doi: 10.5958/0974-360X.2017.00810.1
7. Goode-Romero G. Winnberg U. Domínguez L. Ibarra IA. Vargas R. Winnberg E. Martínez A. New Information of Dopaminergic Agents Based on Quantum Chemistry Calculations. Sci Rep. 2020; 10(1): 21581. https://doi.org/10.1038/s41598-020-78446-4.
8. Riniker S. Landrum GA. Similarity Maps - a Visualization Strategy for Molecular Fingerprints and Machine-Learning Methods. Journal of Cheminformatics. 2013; 5(1): 43. https://doi.org/10.1186/1758-2946-5-43.
9. Meenakshi K. Safa M. Karthick T. Sivaranjani NA. Novel Study of Machine Learning Algorithms for Classifying Health Care Data. Research J. Pharm. and Tech. 2017; 10(5): 1429-1432. doi: 10.5958/0974-360X.2017.00253.0
10. Chaurasia V. Pal S. Skin Diseases Prediction: Binary Classification Machine Learning and Multi Model Ensemble Techniques. Research J. Pharm. and Tech. 2019; 12(8): 3829-3832. doi: 10.5958/0974-360X.2019.00656.5
11. Ruiz-Moreno AJ. Reyes-Romero A. Dömling A. Velasco-Velázquez MA. In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates. Molecules. 2021; 26(7): 1877. https://doi.org/10.3390/molecules26071877.
12. Ali AMS. Pan Tompkins Algorithm based ECG Signal Classification. Research J. Pharm. and Tech. 2017; 10(12): 4365-4367. doi: 10.5958/0974-360X.2017.00802.2.
13. Indraja B. Annapurani K. Classification of Medicines using Naive bayes Classifier. Research J. Pharm. and Tech. 2018; 11(5): 1940-1944. doi: 10.5958/0974-360X.2018.00360.8.
14. Shyamala DM. Sruthi AN. Saranya JC. MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis. Research J. Pharm. and Tech. 2018; 11(2):434-438. doi: 10.5958/0974-360X.2018.00080.X.
15. Kumar NK. Vigneswari D. Hepatitis- Infectious Disease Prediction using Classification Algorithms. Research J. Pharm. and Tech. 2019; 12(8): 3720-3725. doi: 10.5958/0974-360X.2019.00636.X.
16. Pillai N. Dasgupta A. Sudsakorn S. Fretland J. Mavroudis PD. Machine Learning Guided Early Drug Discovery of Small Molecules. Drug Discovery Today. 2022; 27(8): 2209–2215. https://doi.org/10.1016/j.drudis.2022.03.017.
17. Raju B. Narendra G. Verma H. Kumar M. Sapra B. Kaur G. jain SK. Silakari O. Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors. ACS Omega. 2017; 7(36): 31999–32013. https://doi.org/10.1021/acsomega.2c02983.
18. Kamath V. Pai A. Application of Molecular Descriptors in Modern Computational Drug Design –An Overview. Research Journal of Pharmacy and Technology. 2017; 10(9): 3237–3241. https://doi.org/10.5958/0974-360X.2017.00574.1.
19. Li J. Luo D. Wen T. Liu Q. Mo Z. Representative Feature Selection of Molecular Descriptors in QSAR Modeling. Journal of Molecular Structure. 2013; 1244: 131249. https://doi.org/10.1016/j.molstruc.2021.131249.
20. Pedregosa F. Varoquaux G. Gramfort A. Michel V. Thirion B. Grisel O. Blondel M. Prettenhofer P. Weiss R. Dubourg V. Vanderplas J. Passos A. Cournapeau D. Brucher M. Perrot M. Duchesnay É. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12(85): 2825–2830.
21. Dibia KT. Igbokwe PK. Ezemagu GI. Asadu CO. Exploration of the Quantitative Structure-Activity Relationships for Predicting Cyclooxygenase-2 Inhibition Bioactivity by Machine Learning Approaches. Results in Chemistry. 2022; 4: 100272. https://doi.org/10.1016/j.rechem.2021.100272.
22. Landrum GA. RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org/ accessed 2022-03-15..
23. Schmidt LG. Kuhn S. Smolka M. Schmidt K. Rommelspacher H. Lisuride a Dopamine D2 Receptor Agonist and Anticraving Drug Expectancy as Modifiers of Relapse in Alcohol Dependence. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2022; 26(2): 209–217. https://doi.org/10.1016/S0278-584601.00214-7.
24. Wengrovitz A. Ivantsova E. Crespo N. Patel M. Souders CL. Martyniuk CJ. Differential Effects of Dopamine Receptor Agonists Ropinirole and Quinpirole on Locomotor and Anxiolytic Behaviors in Larval Zebrafish Danio Rerio.: A Role for the GABAergic and Glutamate System. Neurotoxicology and Teratology. 2023; 98: 107183. https://doi.org/10.1016/j.ntt.2023.107183.
25. Calverley P. Keating ET. Goldman M. Casty F. Conclusion Lessons from the Novel D2 Dopamine Receptor Β2-Adrenoceptor Agonist Viozan TM: Chronic Obstructive Pulmonary Disease and Drug Development Implications. Respiratory Medicine. 2003; 97: S71–S74. https://doi.org/10.1016/S0954-611103.80017-3.
26. Kamath V. Pai A. Application of Molecular Descriptors in Modern Computational Drug Design –An Overview. Research J. Pharm. and Tech. 2017; 10(9): 3237-3241. doi: 10.5958/0974-360X.2017.00574.1.