Asset Details
Do you wish to reserve the book?
AI sheds new light on genome editing
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
AI sheds new light on genome editing
Do you wish to request the book?
AI sheds new light on genome editing
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
AI sheds new light on genome editing
2026
Overview
Deep learning models facilitate the discovery, engineering, and design of novel genome editors.Protein structure prediction and homolog search are effective methods for mining genome editors that have long evaded detection by traditional sequence-based algorithms.AI-driven approaches turn protein engineering from a needle-in-a-haystack challenge into a tractable process, enabling the optimization of properties of genome editors through efficient in silico engineering (i.e., finding the most stabilizing mutations).Computational protein design methods now bypass evolutionary constraints, creating next-generation genome editors with functions unprecedented in nature.
Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology.
Artificial intelligence (AI) has revolutionized life sciences, driving transformative advances in engineering clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)-based genome editors for therapeutic and agricultural applications. Recent breakthroughs demonstrate how deep learning accelerates the discovery, engineering, and design of next-generation genome editing tools. In this review, we explore how AI-driven approaches are supercharging genome editing in three aspects: (i) structure-based methods for discovering novel genome editors neglected by conventional methods, (ii) engineering genome editors with enhanced properties, and (iii) the de novo design of entirely new genome editors endowed with bespoke functions. Finally, we discuss the current challenges and envision the future potential of data-driven AI to unlock new possibilities in genome editing, catalyzing innovations across biology and biotechnology.
