Recent Developments in Drug Discovery: From Artificial Intelligence to Targeted Protein Degradation

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.