AI Empowers Gene Editing: Core Applications, System Optimization, and Ethical Considerations

Guo Huilin

University of Northern China Electricity Power

Abstract：

Gene editing technologies, particularly CRISPR-Cas systems, have revolutionized biotechnology but face persistent challenges in efficiency, precision, and predictability, necessitating AI-driven solutions to unlock their full potential. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), is transforming gene editing by enhancing sgRNA design accuracy through multi-omics data analysis, optimizing protocols (enzyme selection, delivery strategies), and enabling de novo design of nucleases via models like Transformers. AI also aids in predicting off-target effects, interpreting editing outcomes, and assessing phenotypic impacts, while addressing ethical and regulatory issues. Future advancements aim to develop comprehensive predictive models, advance personalized therapies, and tackle climate-resilient crops, though equitable access and responsible innovation remain critical for realizing the vision of precise genome editing to address global challenges. The synergy between AI and gene editing accelerates the shift from "reading" to "writing" genomes, redefining biotechnology's role in medicine, agriculture, and synthetic biology.

Key Words：

gene editing; CRISPR-cas; sgrna design; off-target prediction; bioethics; A