Authors : Dr. B. Arun Kumar

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/47utyaeu

Scribd : https://tinyurl.com/32k7nd78

DOI : https://doi.org/10.38124/ijisrt/25dec1413

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence (AI) and targeted protein degradation (TPD) are changing drug discovery by introducing new methods for identifying targets and therapies. AI-powered methods like graph neural networks, deep learning,and generative models, are used throughout the drug development process. This includes selecting targets in silico, screening virtually, optimizing leads, and assessing preclinical safety. At the same time, TPD techniques, such as molecular glues and proteolysis-targeting chimeras (PROTACs), and LYTACs, let researchers selectively remove disease proteins by using cellular degradation systems. This review summarizes recent advancements from 2019 to 2025 in both fields. We explore AI platforms and case studies, such as generative design and AlphaFold, as well as new TPD methods like PROTACs and similar degraders. We highlight important tools and examples. Additionally, we address current challenges, data limitations, and the complexity of designing PROTACs. We also look into ethical and regulatory concerns, including data privacy, AI transparency, and the evaluation of new methods, along with future perspectives. Recent achievements, including AI-designed PROTACs that combine inhibition with targeted degradation, showcase the benefits of these technologies working together. Our aim is to give a complete overview of various therapeutic areas.

References :

1. Ferreira, F. J. N., & Carneiro, A. S. (2025). AI-Driven Drug Discovery: A Comprehensive Review. ACS Omega, 10(23), 23889–23903. [https://doi.org/10.1021/acsomega.5c00549]
2. Zhong, G., Chang, X., Xie, W., & Zhou, X. (2024). Targeted protein degradation: advances in drug discovery and clinical practice. Signal Transduction and Targeted Therapy, 9, Article 308. [https://doi.org/10.1038/s41392-024-02004-x]
3. Park, K.-S., & Jeon, M. (2025). Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions. Pharmaceuticals, 18(12), 1793. [https://doi.org/10.3390/ph18121793]
4. Fan, Y., Wu, Y., & Wang, Z. (2025). Exploring the ethical issues posed by AI and big data technologies in drugdevelopment. Acta Pharmaceutica Sinica B (2025).
5. Madaminov, F. (2025, July). Regulating the Use of AI in Drug Development: Legal Challenges and Compliance Strategies. FDLI SmartBrief. [https://www.fdli.org/2025/07/regulating-the-use-of-ai-in-drug-development-legal-challenges-and-compliance-strategies/]
6. Wang, Y., Wang, X., Liu, T., et al. (2025). Discovery of a bifunctional PKMYT1-targeting PROTAC empowered by AI-generation. Nature Communications, 16, 10759. [https://doi.org/10.1038/s41467-025-65796-8]
7. Wang, Y., Wang, X., Liu, T., et al. (2025). Discovery of a bifunctional PKMYT1-targeting PROTAC empowered by AI-generation. Nature Communications, 16, 10759. [https://doi.org/10.1038/s41467-025-65796-8]
8. Bazzacco, A., Mercorelli, B., & Loregian, A. (2025). PROteolysis TArgeting Chimeras (PROTACs) and beyond: targeted degradation as a new path to fight microbial pathogens. FEMS Microbiology Reviews, 49, fuaf046. [https://doi.org/10.1093/femsre/fuaf046]