IN A former leatherworks just off Euston Road in London, a hopeful firm is starting up. BenevolentAI’s main room is large and open-plan. In it, scientists and coders sit busily on benches, plying their various trades. The firm’s star, though, has a private, temperature-controlled office. That star is a powerful computer that runs the software which sits at the heart of BenevolentAI’s business. This software is an artificial-intelligence system.
AI, as it is known for short, comes in several guises. But BenevolentAI’s version of it is a form of machine learning that can draw inferences about what it has learned. In particular, it can process natural language and formulate new ideas from what it reads. Its job is to sift through vast chemical libraries, medical databases and conventionally presented scientific papers, looking for potential drug molecules.
Nor is BenevolentAI a one-off. More and more people and firms believe that AI is well placed to help unpick biology and advance human health. Indeed, as Chris Bishop of Microsoft Research, in Cambridge, England, observes, one way of thinking about living organisms is to recognise that they are, in essence, complex systems which process information using a combination of hardware and software.
That thought has consequences. Whether it is the new Chan Zuckerberg Initiative (CZI), from the founder of Facebook and his wife, or the biological subsidiaries being set up by firms such as Alphabet (Google’s parent company), IBM and Microsoft, the new Big Idea in Silicon Valley is that in the squidgy worlds of biology and disease there are problems its software engineers can solve.
The discovery of new drugs is an early test of the belief that AI has much to offer biology and medicine. Pharmaceutical companies are finding it increasingly difficult to make headway in their search for novel products. The conventional approach is to screen large numbers of molecules for signs of pertinent biological effect, and then winnow away the dross in a series of more and more expensive tests and trials, in the hope of coming up with a golden nugget at the end. This way of doing things is, however, declining in productivity and rising in cost.
One explanation suggested for why drug discovery has become so hard is that most of the obvious useful molecules have been found. That leaves the obscure ones, which leads to long development periods and high failure rates. In theory, growing knowledge of the basic science involved ought to help. The trouble is that too much new information is being produced to be turned quickly into understanding.
Scientific output doubles every nine years. And data are, increasingly, salami-sliced for publication, to lengthen researchers’ personal bibliographies. That makes information hard to synthesise. A century ago someone could still, with effort, be an expert in most fields of medicine. Today, as Niven Narain of BERG Health, an AI and biotechnology firm in Framingham, Massachusetts, points out, it is not humanly possible to comprehend all the various types of data.
This is where AI comes in. Not only can it “ingest” everything from papers to molecular structures to genomic sequences to images, it can also learn, make connections and form hypotheses. It can, in weeks, elucidate salient links and offer new ideas that would take lifetimes of human endeavour to come up with. It can also weigh up the evidence for its hypotheses in an even-handed manner. In this it is unlike human beings, who become unreasonably attached to their own theories and pursue them doggedly. Such wasted effort besets the best of pharmaceutical firms.
For example, Richard Mead, a neuroscientist at the University of Sheffield, in England, says BenevolentAI has given him two ideas for drugs for ALS, a neurodegenerative disease that he works on. Both molecules remain confidential while their utility is being assessed. One is bang in the middle of what he and his team are doing already.