Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety
Alireza Abbaszadeh Armita Shahlai
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Abstract
CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.引用本文复制引用
Alireza Abbaszadeh,Armita Shahlai.Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety[EB/OL].(2025-08-26)[2025-12-23].https://arxiv.org/abs/2508.20130.学科分类
生物科学研究方法、生物科学研究技术/计算技术、计算机技术
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