# Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety

arXiv: https://arxiv.org/abs/2508.20130 · HTML: https://mlsanity.com/paper/2508.20130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 26 Aug 2025.

## Abstract (arXiv)

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.