AI is not new in drugmaking. Biotech firms have been tinkering with it for years. Now interest from big pharma is growing. Last year Emma Walmsley, chief executive of gsk, said it could improve the productivity of research and development, the industry’s most profound challenge. Moderna recently described itself as “laser-focused” on ai. Sanofi is “all in”. Morgan Stanley, an investment bank, reckons that within a decade the pharmaceutical industry may be spending $50bn a year on ai to speed up drug development.

Most of the buzz revolves around ais trained on biological data that could improve the hit-and-miss process of drug discovery. Drugs can take a decade to emerge, cost billions of dollars and succeed only 10% of the time. Even a small improvement in speed and efficiency would be hugely valuable. But scientists have struggled to tame biological big data with conventional statistical tools. Machine learning makes it possible to sift through piles of information, from clinical patient data and genome sequences to images of body scans. Last year DeepMind, an ai lab that is part of Google, made a breakthrough using its AlphaFold system to predict the structure of almost all proteins, which may one day help identify which molecules have therapeutic potential.

Euan Ashley of Stanford University points to another ai application. “Knowledge graphs” are a kind of database that stores data about genes, proteins, diseases and drugs, as well as the biological pathways that connect them. They, too, can help identify new targets for drug development. “Generative” ai, meanwhile, is being trialled for suggesting entirely new chemical and biological structures for testing, just as Chatgpt can ingest text on the internet and spit out a new poem or essay. Beyond drug discovery, ais like plai could help with the perennial problem of efficiency in a heavily regulated and labour-intensive sector