CRISPR–Cas Systems and Deep Learning for Genome Editing: A Comprehensive Review of Models, Datasets and Resources

The discovery of various CRISPR–Cas systems has revolutionized genome engineering by enabling precise and programmable nucleic acid targeting. Continued exploration of CRISPR diversity, together with advances in computational modeling and deep learning (DL)-based design, has expanded the potential to manipulate nearly any genomic locus, thereby accelerating both basic research and therapeutic applications. This review systematically provides a structured and up-to-date overview of CRISPR–Cas technologies, including their classification, computational modeling strategies, and the integration of machine learning (ML) and DL approaches to predict guide RNA (gRNA) efficiency and specificity. The emphasis is placed on studies published between 2019 and 2025, which highlight significant progress in modeling Cas–gRNA–DNA interactions, optimizing on/off-target prediction, and developing comprehensive CRISPR-related datasets. By synthesizing recent developments in CRISPR biology, computational simulations, and artificial intelligence, this review underscores the importance of interdisciplinary integration to improve the accuracy, safety, and scalability of next-generation genome-editing systems.