Applications of Artificial Intelligence Techniques in Medical Drug Discovery and Precision Treatment

Main Article Content

Xiaojing Li

Abstract

Artificial intelligence (AI) has revolutionized various facets of medical drug discovery and precision treatment. This review paper provides a comprehensive overview of AI techniques applied in these domains, encompassing historical developments, core applications, comparative analyses, existing challenges, and future perspectives. The review begins with a historical overview of AI in medicine, charting its evolution from expert systems to deep learning. Core applications are then explored, including AI-driven drug target identification, de novo drug design, prediction of drug efficacy and toxicity, and patient stratification for precision treatment. Specific AI techniques such as machine learning, deep learning, and natural language processing are examined in the context of each application. A critical comparison of different AI approaches highlights their strengths and limitations. The review also addresses challenges in the field, such as data biases, lack of interpretability, and regulatory hurdles. Finally, future directions are discussed, emphasizing the potential of AI to transform drug discovery and personalized medicine. This review aims to serve as essential reference for researchers and practitioners in the intersection of AI and medicine, inspiring future advancements in the field.

Article Details

Section

Articles

How to Cite

Applications of Artificial Intelligence Techniques in Medical Drug Discovery and Precision Treatment. (2026). Journal of Sustainability, Policy, and Practice, 2(1), 125-131. https://schoalrx.com/index.php/jspp/article/view/82

References

1. J. Deng, Z. Yang, I. Ojima, D. Samaras, and F. Wang, “Artificial intelligence in drug discovery: applications and techniques,” Briefings Bioinform., vol. 23, no. 1, pp. 2022.

2. 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.

3. 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.

4. 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.

5. H. S. Chan, H. Shan, T. Dahoun, H. Vogel, and S. Yuan, “Advancing drug discovery via artificial intelligence,” Trends Pharmacol. Sci., vol. 40, no. 8, pp. 592-604, 2019.

6. V. Patel and M. Shah, “Artificial intelligence and machine learning in drug discovery and development,” Intell. Med., vol. 2, no. 3, pp. 134-140, 2022.

7. R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R. K. Ambasta, and P. Kumar, “Artificial intelligence to deep learning: machine intelligence approach for drug discovery,” Mol. Divers., vol. 25, no. 3, pp. 1315-1360, 2021.

8. M. A. Sellwood, M. Ahmed, M. H. Segler, and N. Brown, “Artificial intelligence in drug discovery,” Future Med. Chem., vol. 10, no. 17, pp. 2025-2028, 2018.

9. J. Jiménez-Luna, F. Grisoni, and G. Schneider, “Drug discovery with explainable artificial intelligence,” Nat. Mach. Intell., vol. 2, no. 10, pp. 573-584, 2020.

10. K. K. Mak, Y. H. Wong, and M. R. Pichika, “Artificial intelligence in drug discovery and development,” Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays, pp. 1461-1498, 2024.

11. 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.

12. C. Sarkar, B. Das, V. S. Rawat, J. B. Wahlang, A. Nongpiur, I. Tiewsoh, et al., “Artificial intelligence and machine learning technology driven modern drug discovery and development,” Int. J. Mol. Sci., vol. 24, no. 3, 2023.

13. Y. Jing, Y. Bian, Z. Hu, L. Wang, and X. Q. S. Xie, “Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era,” AAPS J., vol. 20, no. 3, 2018.

14. W. Chen, X. Liu, S. Zhang, and S. Chen, “Artificial intelligence for drug discovery: Resources, methods, and applications,” Mol. Ther. Nucleic Acids, vol. 31, pp. 691-702, 2023.

15. C. Hasselgren and T. I. Oprea, “Artificial intelligence for drug discovery: are we there yet?,” Annu. Rev. Pharmacol. Toxicol., vol. 64, no. 1, pp. 527-550, 2024.

16. 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.

17. 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.