Integrating Graph Neural Networks into Computational Drug Design for Pediatric Diseases

Article Sidebar

PDF
Published: 2026-01-31
Keywords:
Graph Neural Networks (GNNs), computational drug design (CDD), pediatric diseases, drug discovery, Deep Learning, drug-target interaction, molecular property prediction

Main Article Content

Lina Zhang
School of Life Sciences, Central South University, Changsha, China
Wei Zhou
School of Life Sciences, Central South University, Changsha, China

Abstract

Computational drug design (CDD) has emerged as a powerful tool for accelerating the discovery of novel therapeutics, particularly for diseases affecting pediatric populations. However, traditional CDD methods often struggle with the complexities of pediatric diseases, including unique biological targets and developmental considerations. Graph Neural Networks (GNNs), a class of deep learning models capable of processing graph-structured data, have shown remarkable promise in various CDD tasks, such as drug-target interaction prediction, molecular property prediction, and de novo drug design. This review provides a comprehensive overview of the integration of GNNs into CDD for pediatric diseases. We begin with a historical overview of CDD and its applications in pediatrics, highlighting the limitations of traditional approaches. We then delve into the core concepts of GNNs and their specific adaptations for CDD. We discuss the application of GNNs across diverse pediatric diseases, including cancers, genetic disorders, and infectious diseases, focusing on how GNNs can address specific challenges such as data scarcity and target heterogeneity. A comparative analysis of different GNN architectures and their performance in pediatric CDD is presented, along with a discussion of current challenges and limitations, such as the need for improved interpretability and validation. Finally, we explore future perspectives and opportunities for GNN-driven CDD in pediatrics, including the integration of multi-omics data, the development of personalized medicine approaches, and the application of explainable AI techniques. This review aims to provide a valuable resource for researchers and practitioners interested in leveraging GNNs to accelerate the development of safe and effective treatments for pediatric diseases.

Article Details

Issue

Vol. 1 No. 2 (2025): 2025 International Conference on Computer Science, Artificial Intelligence and Communication Systems (CSACS 2025)

Section

Articles

How to Cite

Integrating Graph Neural Networks into Computational Drug Design for Pediatric Diseases. (2026). Journal of Science, Innovation & Social Impact, 1(2), 175-182. https://sagespress.com/index.php/JSISI/article/view/73

References

1. Y. S. Cai, “Organizational restructuring of fintech enterprises: A strategic study balancing compliance and innovation,” Financial Economics Insights, vol. 3, no. 1, pp. 1–10, 2026.

2. C. Abate, S. Decherchi, and A. Cavalli, “Graph neural networks for conditional de novo drug design,” Wiley Interdisciplinary Reviews: Computational Molecular Science, vol. 13, no. 4, e1651, 2023.

3. R. Satheeskumar, “Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning,” Intelligent Pharmacy, vol. 3, no. 2, pp. 127-140, 2025.

4. J. Jin, T. Zhu, and C. Li, "Graph Neural Network-Based Prediction Framework for Protein-Ligand Binding Affinity: A Case Study on Pediatric Gastrointestinal Disease Targets," Journal of Medicine and Life Sciences, vol. 1, no. 3, pp. 136–142, 2025.

5. C. Wang, G. A. Kumar, and J. C. Rajapakse, “Drug discovery and mechanism prediction with explainable graph neural networks,” Scientific Reports, vol. 15, no. 1, 179, 2025.

6. J. Xiong et al., “Graph neural networks for automated de novo drug design,” Drug discovery today, vol. 26, no. 6, pp. 1382-1393, 2021.

7. R. Luo, X. Chen, and Z. Ding, "SeqUDA-Rec: Sequential user behavior enhanced recommendation via global unsupervised data augmentation for personalized content marketing," arXiv preprint arXiv:2509.17361, 2025.

8. Y. Ding, Y. Shen, K. Yi, and Y. G. Wang, “GNNs in drug design,” in Deep Learning in Drug Design, Academic Press, 2026, pp. 31-50.

9. K. Wang et al., “Recent advances from computer-aided drug design to artificial intelligence drug design,” RSC Medicinal Chemistry, vol. 15, no. 12, pp. 3978-4000, 2024.

10. P. Bongini, “Graph neural networks for drug discovery: An integrated decision support pipeline,” in 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 2023, pp. 218-223.

11. V. Ponzi and C. Napoli, “Graph Neural Networks: Architectures, Applications, and Future Directions,” IEEE Access, 2025.

12. J. L. Zhao, “Graph-based deep dive on AI startup revenue composition and venture capital network effect,” Economics and Management Innovation, vol. 3, no. 1, pp. 27–36, 2026.

13. G. Corso et al., “Graph neural networks,” Nature Reviews Methods Primers, vol. 4, no. 1, 17, 2024.

14. Y. Wang, Z. Li, and A. Barati Farimani, “Graph neural networks for molecules,” in Machine learning in molecular sciences, Cham: Springer International Publishing, 2023, pp. 21-66.

15. S. Li, K. Liu, and X. Chen, “A context-aware personalized recommendation framework integrating user clustering and BERT-based sentiment analysis,” Journal of Computer, Signal, and System Research, vol. 2, no. 6, pp. 100–108, 2025.

16. R. R. Saxena and R. Saxena, “Applying graph neural networks in pharmacology,” Authorea Preprints, 2024.

17. Y. Li, G. Zhang, P. Wang, Z. G. Yu, and G. Huang, “Graph neural networks in biomedical data: a review,” Current Bioinformatics, vol. 17, no. 6, pp. 483-492, 2022.

18. C. L. Cheong, “Research on AI security strategies and practical approaches for risk management,” Journal of Computer, Signal, and System Research, vol. 2, no. 7, pp. 98–115, 2025.