Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety
This addresses the critical challenge of optimizing guide RNA design for CRISPR-based genome editing, which is important for researchers and clinicians, but it is incremental as it reviews existing advances.
The paper reviews how artificial intelligence, particularly deep learning, improves the prediction of CRISPR guide RNA on-target activity and off-target risks, highlighting that AI models can markedly enhance efficiency and safety for genome editing applications.
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.