In 2025, we will see AI and machine learning begin to amplify the impact of Crispr genome editing in medicine, agriculture, climate change, and the basic research that underpins these fields. It’s worth saying up front that the field of AI is full of big promises like this. With any major new technological advancement, there is always a hype cycle, and we are living in it right now. In many cases, the benefits of AI will be a few years away, but in genomics and life sciences research, we are seeing real impacts now.
In my field, Crispr gene editing and genomics in general, we often deal with huge data sets or, in many cases, we can’t treat them properly because we simply don’t have the tools or the time. Supercomputers can take weeks or even months to analyze subsets of data for a given question. So we need to be very selective about the 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 vast genomic data sets.
In my lab, we recently used AI tools to help us find small gene-editing proteins that had not been discovered in public genome databases because we simply didn’t have not the ability to analyze all the data we collected. A group from the Innovative Genomics Institute, the research institute I founded 10 years ago at UC Berkeley, recently teamed up 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 than natural sequences. Imagine what awaits to be discovered in the enormous genomic and structural databases that scientists have collectively built over the past decades.
These types of findings have real-world applications. For both examples above, smaller genome editors can contribute to more efficient delivery of therapies into cells, and prediction of heat-stable RNA molecules will help improve biomanufacturing processes that generate drugs and other valuable products. In healthcare and drug development, we recently saw the approval of the first Crispr-based treatment for sickle cell disease, and approximately 7,000 other genetic diseases await similar treatment. AI can help accelerate the development process by predicting the best editing targets, maximizing Crispr’s accuracy and efficiency, and reducing off-target effects. In agriculture, Crispr’s AI-powered 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. In the climate space, AI and Crispr could open up new solutions to improve natural carbon capture and environmental sustainability.
We’re still early days, but the potential to appropriately harness the joint power of AI and Crispr, arguably the two most advanced technologies of our time, is clear and exciting – and that has already started.