AI assisted optimization of CRISPR Cas systems and functional gene screening in cucurbit crops

Cucurbit crops such as cucumber (Cucumis sativus), watermelon (Citrullus lanatus), and melon (Cucumis melo) are globally important horticultural species with diverse economic and nutritional values. However, their genetic improvement is often constrained by complex genomes, genotype-dependent transformation efficiencies, and the polygenic nature of key traits. The CRISPR/Cas genome editing system has emerged as a transformative tool for functional gene validation and trait enhancement in these crops. Yet, challenges such as guide RNA design, off-target effects, and regulatory uncertainty remain significant obstacles to widespread application. In recent years, artificial intelligence (AI)–driven models and multi-omics technologies have been increasingly integrated with CRISPR workflows to address these challenges. AI-based tools have improved the prediction of sgRNA activity and specificity, enabling more precise and efficient editing strategies. Simultaneously, transcriptomic, epigenomic, and single-cell omics data have facilitated the identification and prioritization of functional genes involved in fruit quality, stress tolerance, and disease resistance. Case studies in cucumber, watermelon, and melon demonstrate the power of this integrated approach, from achieving broad virus resistance by editing eIF4E, to conferring powdery mildew resistance via multiplex MLO knockouts, and manipulating sex determination through WIP1 to streamline hybrid seed production. We outline a stepwise workflow—omics-driven target nomination → AI-guided sgRNA design (on-target efficiency) → variant-aware off-target enumeration with ML scoring → repair-outcome prediction → delivery & editing → validation (amplicon sequencing/GUIDE-seq) → trait evaluation. We also clarify which AI predictions have been experimentally validated in cucurbits versus inferred from other plant systems, and discuss current limitations (genotype-dependent transformation, mosaicism, off-target risks) together with regulatory considerations across major jurisdictions. This review synthesizes global advancements from leading research groups across Asia, Europe, and the Americas, highlighting a worldwide collaborative effort to harness these technologies for cucurbit crop improvement. These examples illustrate how AI-assisted CRISPR design translates into practical outcomes across regions.