Foundation models and AI agents in oncology drug discovery

Area of Science:

• Oncology
• Drug Discovery
• Artificial Intelligence

Background:

• Oncology drug discovery faces challenges including high attrition rates, slow experimental cycles, and poor translation of preclinical findings to clinical outcomes.
• Current methods struggle with uncertainty in complex molecular and cellular systems, limiting the efficiency and success of developing new cancer treatments.

Purpose of the Study:

• To review how artificial intelligence (AI) is transforming oncology drug discovery.
• To examine the integration of AI across biological modeling, generative design, and autonomous platforms.
• To discuss the implications for targeted protein degradation, clinical validation, and regulatory pathways.

Main Methods:

• Review of AI applications in oncology drug discovery, focusing on three layers: biological foundation models, generative design, and autonomous discovery platforms.
• Analysis of targeted protein degradation strategies and their integration with AI.
• Examination of clinical validation and regulatory considerations for AI-driven drug discovery.

Main Results:

• AI enhances biological foundation models to reduce uncertainty in molecular and cellular systems.
• Generative design methods improve drug candidate quality and shorten medicinal chemistry timelines.
• Autonomous discovery platforms integrate reasoning, experimentation, and feedback for accelerated development.

Conclusions:

• AI is restructuring oncology drug discovery through advanced modeling, design, and autonomous platforms.
• Future advancements require focusing on causal inference, context generalization, interpretability, and regulatory-grade evidence, not just model scale.
• Targeted protein degradation and AI integration show promise for improved clinical translation.