Book chapter: AI-aided drug development for protein degraders: Design, lead identification, and optimization
- grebner0
- 2 hours ago
- 1 min read
A new chapter in the book series Annual Reports in Medicinal Chemistry (Academic Press, 2025) was published by scientist from PROXIDRUGS partner Fraunhofer ITMP. It offers a comprehensive overview of AI-driven methods in the design and application of proximity-inducing therapeutics (proxidrugs).
After introducing targeted protein degradation, the chapter examines the key challenges in proxidrug design and the role of computational methods. It also provides a broader perspective on the foundations and evolution of artificial intelligence in drug discovery. Different AI approaches in design of proxidrugs, prediction of ternary complex formation and prediction models for bioactivities and ADME properties are discussed, along with the specific challenges of applying them to the development of proximity-inducing therapeutics.
The authors conclude that future progress in targeted protein degrader design will, among others, rely on diversifying datasets, employing advanced physicochemistry-informed modeling, and improving strategies to bridge the gap between activity prediction and in vivo efficacy.
