AI-Driven CRISPR Design: Methods, Tools, Applications and Challenges

<jats:p>While CRISPR-Cas systems have emerged as transformative gene-editing technologies, conventional design strategies reliant on empirical rules and trial-and-error remain inefficient and cost-prohibitive. Artificial intelligence (AI) presents novel opportunities to enhance the precision, efficiency, and automation of CRISPR design. This review provides a systematic survey of AI-driven tools and methodologies utilized in this domain. We categorize these approaches by learning paradigm, discussing how five distinct families—traditional supervised learning, deep learning, attention-based models, generative AI and foundation models, and reinforcement learning—facilitate CRISPR design. Functionally, we classify these tools into eight categories: database search, structure and function prediction, sequence and structure generation, virtual screening, DNA synthesis, and experimental design. These tools have yielded significant outcomes across therapeutic, agricultural, and basic research settings. Despite persisting challenges regarding data quality, interpretability, experimental validation, and safety, advances in multimodal AI and personalized design are poised to significantly expand the impact of AI on precision medicine. Furthermore, this review offers practical guidelines for tool selection tailored to researchers with varying computational expertise, serving as an essential resource for the gene editing, computational biology, and translational medicine communities.</jats:p>