CNS-DDI: An Integrated Graph Neural Network Framework for Predicting Central Nervous System Related Drug-Drug Interactions

Abstract

The central nervous system (CNS) is one of the most complex and vital systems of the human body and is particularly interrelated with all other systems. Treatment modalities targeting the CNS as well as those targeting other systems may directly or indirectly affect the CNS. Especially in cases of polypharmacy, drug-drug interactions (DDIs) can lead to severe problems. The widespread use of drugs that have an effect on the CNS and the unpredictability of possible interactions between these drugs both complicate the treatment processes of patients and considerably increase health costs. In this study, a novel method based on Graph Convolutional Neural Networks (GCN) is proposed to predict CNS-related DDIs. The proposed approach utilizes a data fusion method by exploiting both graph structures and physical properties of drug molecules. This integrated approach enabled a more comprehensive and reliable prediction of drug interactions. The developed model achieved 98.67% accuracy and 0.994 AUC in the training process and 98.40% accuracy and 0.991 AUC in the validation process. A Graphical Interface (GUI) was designed to make the developed model easily usable by users. The integration of molecular structure and interaction network data sets a new benchmark for reliability and accuracy in DDIs prediction, addressing a critical need in modern healthcare systems. The developed methods and tools have significant potential for predicting drug interactions in the drug discovery process and in polypharmacy situations.

Keywords

• Artificial intelligence in drug
• Deep learning in drug discovery
• Central Nervous System (CNS)
• Drug-Drug Interactions (DDIs)
• Computational chemistry
• drug discovery

References

1. T. V Dunwiddie and S. A. Masino, “The role and regulation of adenosine in the central nervous system,” Annu. Rev. Neurosci., vol. 24, no. 1, pp. 31–55, 2001.
2. J. F. Thayer, A. L. Hansen, E. Saus-Rose, and B. H. Johnsen, “Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health,” Ann. Behav. Med., vol. 37, no. 2, pp. 141–153, 2009.
3. P. Brodal, The central nervous system. oxford university Press, 2010.
4. D. E. Pankevich, B. M. Altevogt, J. Dunlop, F. H. Gage, and S. E. Hyman, “Improving and accelerating drug development for nervous system disorders,” Neuron, vol. 84, no. 3, pp. 546–553, 2014.
5. A. S. Kesselheim, T. J. Hwang, and J. M. Franklin, “Two decades of new drug development for central nervous system disorders,” Nat. Rev. Drug Discov., vol. 14, no. 12, p. 815, 2015.
6. D. J. Begley, “Delivery of therapeutic agents to the central nervous system: the problems and the possibilities,” Pharmacol. Ther., vol. 104, no. 1, pp. 29–45, 2004.
7. E. C. M. de Lange, “The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects,” Fluids Barriers CNS, vol. 10, pp. 1–16, 2013.
8. A. Misra, S. Ganesh, A. Shahiwala, and S. P. Shah, “Drug delivery to the central nervous system: a review,” J Pharm Pharm Sci, vol. 6, no. 2, pp. 252–273, 2003.

9. C. Palleria et al., “Pharmacokinetic drug-drug interaction and their implication in clinical management,” J. Res. Med. Sci. Off. J. Isfahan Univ. Med. Sci., vol. 18, no. 7, p. 601, 2013.
10. T. Roblek, T. Vaupotic, A. Mrhar, and M. Lainscak, “Drug-drug interaction software in clinical practice: a systematic review,” Eur. J. Clin. Pharmacol., vol. 71, pp. 131–142, 2015.
11. L. Magro, U. Moretti, and R. Leone, “Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions,” Expert Opin. Drug Saf., vol. 11, no. 1, pp. 83–94, 2012.
12. E. N. van Roon et al., “Clinical relevance of drug-drug interactions: a structured assessment procedure,” Drug Saf., vol. 28, pp. 1131–1139, 2005.
13. T. Zhang, J. Leng, and Y. Liu, “Deep learning for drug–drug interaction extraction from the literature: a review,” Brief. Bioinform., vol. 21, no. 5, pp. 1609–1627, 2020.
14. M. A. Pala and M. Z. Yıldız, “Improving cellular analysis throughput of lens-free holographic microscopy with circular Hough transform and convolutional neural networks,” Opt. Laser Technol., vol. 176, p. 110920, 2024.
15. M. A. Pala, M. E. Çimen, A. Akgül, M. Z. Yıldız, and A. F. Boz, “Fractal dimension-based viability analysis of cancer cell lines in lens-free holographic microscopy via machine learning,” Eur. Phys. J. Spec. Top. 2021, pp. 1–12, Nov. 2021, doi: 10.1140/EPJS/S11734-021-00342-3.
16. K. Han et al., “A review of approaches for predicting drug–drug interactions based on machine learning,” Front. Pharmacol., vol. 12, p. 814858, 2022.
17. Y. Deng, X. Xu, Y. Qiu, J. Xia, W. Zhang, and S. Liu, “A multimodal deep learning framework for predicting drug–drug interaction events,” Bioinformatics, vol. 36, no. 15, pp. 4316–4322, 2020.
18. X. Li, Z. Xiong, W. Zhang, and S. Liu, “Deep learning for drug‐drug interaction prediction: A comprehensive review,” Quant. Biol., vol. 12, no. 1, pp. 30–52, 2024.
19. Y.-H. Feng and S.-W. Zhang, “Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs,” Molecules, vol. 27, no. 9, p. 3004, 2022.
20. J. You, R. D. McLeod, and P. Hu, “Predicting drug-target interaction network using deep learning model,” Comput. Biol. Chem., vol. 80, pp. 90–101, 2019.
21. E. Kim and H. Nam, “DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions,” J. Cheminform., vol. 14, no. 1, p. 9, 2022.
22. X. Han, R. Xie, X. Li, and J. Li, “Smilegnn: drug–drug interaction prediction based on the smiles and graph neural network,” Life, vol. 12, no. 2, p. 319, 2022.
23. S. Vilar, R. Harpaz, E. Uriarte, L. Santana, R. Rabadan, and C. Friedman, “Drug—drug interaction through molecular structure similarity analysis,” J. Am. Med. Informatics Assoc., vol. 19, no. 6, pp. 1066–1074, 2012.
24. H. Ibrahim, A. M. El Kerdawy, A. Abdo, and A. S. Eldin, “Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions,” Informatics Med. Unlocked, vol. 26, p. 100699, 2021.
25. S. Jain, E. Chouzenoux, K. Kumar, and A. Majumdar, “Graph regularized probabilistic matrix factorization for drug-drug interactions prediction,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 5, pp. 2565–2574, 2023.
26. Z. Li, X. Tu, Y. Chen, and W. Lin, “HetDDI: a pre-trained heterogeneous graph neural network model for drug–drug interaction prediction,” Brief. Bioinform., vol. 24, no. 6, p. bbad385, Nov. 2023, doi: 10.1093/bib/bbad385.
27. X. Lin, Z. Quan, Z.-J. Wang, T. Ma, and X. Zeng, “KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}, C. Bessiere, Ed., International Joint Conferences on Artificial Intelligence Organization, 2020, pp. 2739–2745. doi: 10.24963/ijcai.2020/380.
28. Y. Zhang et al., “Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network,” Nat. Comput. Sci., vol. 3, no. 12, pp. 1023–1033, 2023.
29. J. Gao, Z. Wu, R. Al-Sabri, B. M. Oloulade, and J. Chen, “AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network,” IEEE J. Biomed. Heal. Informatics, 2024.
30. H. Liu, S. Li, and Z. Yu, “Predicting drug-drug interactions using heterogeneous graph neural networks: HGNN-DDI,” Appl. Comput. Eng., vol. 79, pp. 77–89, 2024.
31. C. Knox et al., “DrugBank 6.0: the DrugBank knowledgebase for 2024,” Nucleic Acids Res., vol. 52, no. D1, pp. D1265–D1275, 2024.
32. S. Kim et al., “PubChem substance and compound databases,” Nucleic Acids Res., vol. 44, no. D1, pp. D1202–D1213, 2016.
33. G. Landrum, “Rdkit documentation,” Release, vol. 1, no. 1–79, p. 4, 2013.
34. M. R. Prusty, T. Jayanthi, and K. Velusamy, “Weighted-SMOTE: A modification to SMOTE for event classification in sodium cooled fast reactors,” Prog. Nucl. Energy, vol. 100, pp. 355–364, 2017.
35. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Trans. neural networks Learn. Syst., vol. 32, no. 1, pp. 4–24, 2020.
36. S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Comput. Soc. Networks, vol. 6, no. 1, pp. 1–23, 2019.
37. A. Akgul, Y. Karaca, M. A. Pala, M. E. Çimen, A. F. Boz, and M. Z. Yildiz, “Chaos Theory, Advanced Metaheuristic Algorithms And Their Newfangled Deep Learning Architecture Optimization Applications: A Review,” Fractals, vol. 32, no. 03, p. 2430001, 2024.