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arXiv · 2508.20130

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.

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BibTeXRIS

Alireza Abbaszadeh, Armita Shahlai. 2025-08-26. Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety. https://arxiv.org/abs/2508.20130

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