Scientists recently gave an artificial intelligence system an assignment to come up with the genetic instructions for a virus. They made DNA from the system’s suggestions and put it into bacteria. Some of the bacteria began producing new viruses, which went on to infect other cells.
The genome of this virus contains only about 5,400 DNA letters, making it a manageable test of whole-genome design. Designing a genome is like composing a symphony: the individual parts must work together. A change in one protein can affect how it interacts with another, so a successful design may require coordinated changes across several genes. The researchers wanted to see whether AI could learn enough of these relationships to make this feasible. The team trained AI models on vast collections of DNA and gave them further training on a small virus called phi X174 and thousands of its relatives. The successful artificial viruses are close relatives of natural viruses. Yet one included a protein from a more distant relative, a change that previous human engineering attempts had failed to make work. In the AI-generated design, it worked alongside the other components. This offers a concrete example of AI finding a biological solution that conventional engineering struggled to achieve. Viruses are scary when they infect humans. But viruses that kill bacteria (like the ones designed here) can also be used to treat hard-to-treat bacterial infections. Viruses also serve as delivery vehicles for inserting therapeutic genes into the cells of patients. Better ways to design them could benefit medicine and laboratory research, although these AI-designed viruses have not been shown to be safe and effective treatments. AI will expand our ability to understand disease and design treatments, but greater capability makes human scrutiny more important. The hospital examples show the value of a system whose findings doctors assess before acting. Biological design needs comparable oversight before a proposed genome is turned into something that can reproduce. AI may help write the instructions, but humans must remain responsible for the outputs and their consequences.
