Crisprgpt For Agentic Automation Of Geneediting Experiments Yuanhao Qu Kaixuan Huang Ming Yin Kanghong Zhan Dyllan Liu Di Yin Henry C Cousins William A Johnson Xiaotong Wang Mihir Shah Russ B Altman Denny Zhou Mengdi Wang Le Cong by Yuanhao Qu & Kaixuan Huang & Ming Yin & Kanghong Zhan & Dyllan Liu & Di Yin & Henry C. Cousins & William A. Johnson & Xiaotong Wang & Mihir Shah & Russ B. Altman & Denny Zhou & Mengdi Wang & Le Cong instant download after payment.

Performing efective 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-specifc 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–artifcial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools and a specialized LLM fne tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing guide RNAs, choosing delivery methods, drafting protocols, designing assays and analysing data. We showcase the potential of CRISPR-GPT by knocking out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in a human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across diferent modalities, validating its efectiveness as an AI co-pilot in genome engineering.