In 2025, we will see AI and machine learning begin to amplify the impact of Crispr genome editing on medicine, agriculture, climate change, and the basic research that underpins these fields. It’s worth saying up front that the AI field is awash with big promises like this. With any major new technological advancement, there is always a hype cycle, and we are in one now. In many cases, the benefits of AI will occur within a few years, but in genomics and life sciences research we are seeing real impacts right now.
In my field, Crispr gene editing and genomics in general, we often deal with huge data sets or, in many cases, not able address them properly because we simply don’t have the tools or the time. Supercomputers can take weeks or months to analyze subsets of data for a given question, so we have to be very selective about which questions we choose to ask. AI and machine learning are already removing these limitations, and we are using AI tools to quickly search and make discoveries in our large genomic data sets.
In my lab, we recently used artificial intelligence tools to help us find small gene-editing proteins that had not been discovered in public genome databases because we simply did not have the ability to analyze all the data we had collected. A group at the Innovative Genomics Institute, the research institute I founded 10 years ago at UC Berkeley, recently joined forces with members of the Department of Electrical Engineering and Computer Science (EECS) and the Center for Computational Biology, and developed a way to use a large language model, similar to that used by many popular chatbots, to predict new functional RNA molecules that have greater heat tolerance compared to natural sequences. Imagine what else is waiting to be discovered in the enormous structural and genomic databases that scientists have collectively built over the past decades.
These types of discoveries have real-world applications. In the two examples above, smaller genome editors can help deliver therapies to cells more efficiently, and predicting thermostable RNA molecules will help improve biomanufacturing processes that generate drugs and other valuable products. In healthcare and drug development, we have recently seen the approval of the first Crispr-based therapy for sickle cell anemia, and there are around 7,000 other genetic diseases awaiting similar therapy. AI can help speed up the development process by predicting the best editing targets, maximizing Crispr’s accuracy and efficiency, and reducing off-target effects. In agriculture, AI-based Crispr advances promise to create more resilient, productive and nutritious crops, ensuring greater food security and reducing time to market by helping researchers focus on the most fruitful approaches. In climate, AI and Crispr could open up new solutions to improve natural carbon capture and environmental sustainability.
It’s still early days, but the potential to properly harness the combined power of AI and Crispr, arguably the two most profound technologies of our time, is clear and exciting, and has already begun.