CRISPR-GPT for agentic automation of gene-editing experiments

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools and a specialized LLM fine…

• NS
  National Science Foundation

  Award: 1653435
• DE
  Donald E. and Delia B. Baxter Foundation
• NH
  National Human Genome Research Institute

  Award: R35HG011316, 1R01GM141627