Closed-loop agentic AI in drug discovery

Area of Science:

• Drug discovery and development
• Artificial intelligence in medicine
• Computational biology

Background:

• The field of drug discovery is transitioning from standalone artificial intelligence (AI) tools to integrated, closed-loop, agent-driven systems.
• Recent advancements in large language models (LLMs), generative frameworks, and self-driving systems are enabling adaptive, multi-agent ecosystems.
• These systems are capable of hypothesis generation, iterative optimization, and autonomous decision-making in drug development.

Purpose of the Study:

• To critically examine the evolving landscape of agentic drug discovery.
• To illustrate the transition towards hybrid human-AI intelligence and digital twins.
• To highlight the development of regulation-ready autonomous platforms for drug discovery.

Main Methods:

• Review and analysis of current trends in AI-driven drug discovery.
• Examination of the integration of prediction and experimental execution in closed-loop systems.
• Assessment of challenges and opportunities in autonomous drug discovery platforms.

Main Results:

• Emergence of adaptive, multi-agent ecosystems for drug discovery.
• Identification of key challenges including data bias, limited interpretability, coordination fragility, and regulatory misalignment.
• Illustration of the shift towards hybrid human-AI intelligence, digital twins, and autonomous platforms.

Conclusions:

• Resolving current challenges is crucial for the successful translation of agentic drug discovery into reliable clinical outcomes.
• The field is moving towards a self-evolving scientific enterprise rather than remaining tool-driven.
• Future drug discovery may rely on autonomous platforms and hybrid human-AI collaboration.