The ability of AI to design experiments and new chemicals stands to revolutionize the field of synthetic biology. AI and machine learning are kicking synthetic biology up to new levels of innovation. Researchers see huge potential for novel drugs and other chemicals; some also see risks.
J ames Field believes his automated laboratory is closing in on a powerful new drug to kill cancer cells with unprecedented precision. Yet the drug wasn’t created by any of the biologists working in his lab — it was generated by artificial intelligence. Field is a protein engineer working in synthetic biology, a discipline that uses the latest technology to engineer novel cells or biological components.
In his world, researchers often talk about a cycle known as DBTL — design, build, test, learn — an iterative process where the end product, whether it is a molecule or a new strain of bacteria, is repeatedly optimized until it has the traits scientists seek. Field’s company, LabGenius, is one of a group of startups that is at the forefront of a revolution in which AI increasingly runs the DBTL cycle from beginning to end. The work takes place at the company’s sophisticated automated laboratory in a former biscuit factory in south London.
Such laboratories, known as biofoundries, first emerged in the 2010s as gene editing technology, combined with “high-throughput” machines — which can conduct hundreds of experiments at once — accelerated the DBTL cycle by many orders of magnitude. A revolutionary cancer treatment could transform autoimmune disease Experiments using biofoundries often involve adapting a piece of DNA that is then added to a host organism — frequently the bacterium _Escherichia coli_ or yeast — to get it to produce a desired molecule, which can be anything from an antibody to an ingredient for sustainable plastic. Algorithms have been integral to biofoundries from the start in managing the operation of high-throughput machines.
But the industry is now approaching a point where AI models are becoming sophisticated enough to program DNA to produce new medicines and industrial chemicals, an emerging field known as SynBioxAI. Rather than helping only to run the machinery, AI can now work iteratively, learning from results and suggesting new avenues of experimentation, reducing the development time for new drugs or industrial chemicals from years to months. According to Paul Freemont, who heads synthetic biology at Imperial College London, we are approaching a powerful point of “convergence of automation, data and machine learning and AI.” He was the founding chair of the Global Biofoundries Alliance, an international group of publicly funded biofoundries that launched in 2019 in Kobe, Japan, with 16 members.
The group has now grown to over 40 members across the world, spread from China to Mexico. Since that launch, countries have been rapidly investing in biofoundry capacity. The United States, for example, is spending $75 million on the development of five new biofoundries, while China has named biomanufacturing and synthetic biology as core strategic priorities for its 2026-2030 five-year plan.
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