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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions.

Kwang-Su Park¹, Minji Jeon²

¹College of Pharmacy, Keimyung University, Daegu 42601, Republic of Korea.

Pharmaceuticals (Basel, Switzerland)

|December 31, 2025

Summary

Artificial intelligence (AI) accelerates drug discovery by optimizing Proteolysis Targeting Chimeras (PROTACs). AI aids in designing these targeted protein degraders, overcoming challenges in development for enhanced therapeutic potential.

Keywords:

ADME prediction AI PROTAC PROTAC molecule design degradability ternary complex prediction

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Area of Science:

Drug Discovery
Medicinal Chemistry
Biotechnology

Background:

Proteolysis Targeting Chimeras (PROTACs) offer a novel therapeutic strategy for targeted protein degradation via the ubiquitin proteasome system.
Rational PROTAC design is complex, involving target-ligase compatibility, ternary complex formation, linker optimization, and efficiency assessment.

Purpose of the Study:

To review the application of artificial intelligence (AI) in accelerating the PROTAC development pipeline.
To analyze current AI strategies for PROTAC design, including structure prediction, degradability, linker design, and ADME properties.

Main Methods:

Review of existing AI applications in PROTAC development.
Analysis of AI-driven structure-based modeling for ternary complexes.
Exploration of AI for predicting degradability, designing linkers, and estimating pharmacokinetic properties.

Main Results:

AI models are increasingly used for ternary complex structure prediction, degradability assessment, and linker design.
AI assists in estimating ADME properties, crucial for PROTAC drug development.
Adaptation of AI approaches from related fields can address PROTAC discovery challenges.

Conclusions:

AI offers powerful tools to streamline and rationalize PROTAC discovery and development.
Overcoming challenges like limited data, interpretability, and generalizability in AI models is key.
AI-driven strategies promise to accelerate the creation of effective targeted protein degraders.