Robots, computers, and algorithms are hunting for potential new therapies in ways humans can’t—by processing huge volumes of data and building previously unimagined molecules.

At an old biscuit factory in South London, giant mixers and industrial ovens have been replaced by robotic arms, incubators, and DNA sequencing machines. James Field and his company LabGenius aren’t making sweet treats; they’re cooking up a revolutionary, AI-powered approach to engineering new medical antibodies.

In nature, antibodies are the body’s response to disease and serve as the immune system’s front-line troops. They’re strands of protein that are specially shaped to stick to foreign invaders so that they can be flushed from the system. Since the 1980s, pharmaceutical companies have been making synthetic antibodies to treat diseases like cancer, and to reduce the chance of transplanted organs being rejected.

But designing these antibodies is a slow process for humans—protein designers must wade through the millions of potential combinations of amino acids to find the ones that will fold together in exactly the right way, and then test them all experimentally, tweaking some variables to improve some characteristics of the treatment while hoping that doesn’t make it worse in other ways. “If you want to create a new therapeutic antibody, somewhere in this infinite space of potential molecules sits the molecule you want to find,” says Field, the founder and CEO of LabGenius.

He started the company in 2012 when, while studying for a PhD in synthetic biology at Imperial College London, he saw the costs of DNA sequencing, computation, and robotics all coming down. LabGenius makes use of all three to largely automate the antibody discovery process. At the lab in Bermondsey, a machine learning algorithm designs antibodies to target specific diseases, and then automated robotic systems build and grow them in the lab, run tests, and feed the data back into the algorithm, all with limited human supervision. There are rooms for culturing diseased cells, growing antibodies, and sequencing their DNA: Technicians in lab coats prepare samples and tap away at computers as machines whir in the background.

Human scientists start by identifying a search space of potential antibodies for tackling a particular disease: They need proteins that can differentiate between healthy and diseased cells, stick to the diseased cells, and then recruit an immune cell to finish the job. But these proteins could sit anywhere in the infinite search space of potential options. LabGenius has developed a machine learning model that can explore that space much more quickly and effectively. “The only input you give the system as a human is, here’s an example of a healthy cell, here’s an example of a diseased cell,” says Field. “And then you let the system explore the different [antibody] designs that can differentiate between them.”

The model selects more than 700 initial options from across a search space of 100,000 potential antibodies, and then automatically designs, builds, and tests them, with the aim of finding potentially fruitful areas to investigate in more depth. Think of choosing the perfect car from a field of thousands: You might start by choosing a broad color, and then filter from there into specific shades.