Artificial intelligence (AI) agents are transforming drug discovery by automating complex research processes, significantly accelerating timelines from months to hours. A team led by Srijit Seal, Dinh Long Huynh, and Moudather Chelbi presents a thorough examination of agentic AI systems that merge large language models with tools for perception, computation, and action. These AI agents autonomously integrate biomedical data, execute experiments, and refine hypotheses, providing substantial improvements in efficiency and accuracy.
The document highlights the application of AI in toxicology and drug development, emphasizing practical design considerations and data challenges. Key innovations include using Retrieval-Augmented Generation (RAG) for real-time data access and the creation of digital twins for simulating experiments. Case studies reveal significant achievements, such as rapid literature reviews and 400-fold reductions in workflow timelines. Additionally, the multi-agent AI system offers strategic analyses, maximizing drug candidate assessments and enhancing business development within pharmaceutical settings. This innovative approach promises to reshape the future of drug discovery.
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