In their search for new disease-fighting medicines, drug makers have long employed a laborious trial-and-error process to identify the right compounds. But what if artificial intelligence could predict the makeup of a new drug molecule the way Google figures out what you're searching for, or email programs anticipate your replies--like "Got it, thanks"? That's the aim of a new approach that uses an AI technique known as natural language processing--the same technology that enables OpenAI's ChatGPT to generate human-like responses--to analyze and synthesize proteins, which are the building blocks of life and of many drugs. The approach exploits the fact that biological codes have something in common with search queries and email texts: Both are represented by a series of letters. A look at how innovation and technology are transforming the way we live, work and play.

Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person. Abbott said the test case was not about any sort of legal rights for machines, rather it was about trying to get a patent for "the inventive output from an AI" that lacks a traditional human inventor. Some firms were already using AI programmes to discover new drugs or to find ways to repurpose materials but the companies that many of the lawyers on the case represent wanted greater clarity on patent ownership before investing further, he said. The application was declined in Australia but later overturned by the Federal Court in 2021 which said the country's patent act had no specific provision excluding AI systems as inventors.

"Our work proves that AI algorithms can be employed in a targeted manner to design active ingredients with the same effects as natural substances, but with simpler structures," Schneider says, adding: "This helps not only to manufacture new drugs, but also places us on the cusp of a potentially fundamental change in medical- chemical research." That is to say, the ETH research group's methods make it possible to find drugs that do the same things as existing drugs but are based on different structures. This could make it easier in future to design new unpatented molecular structures. There is currently intense debate regarding both the extent to which AI could be used to systematically circumvent patent protection and the possible patenting of molecules designed by "creative" AI. In any case, the pharmaceutical industry will have to adapt its research approach to a new rulebook.

Artificial intelligence (AI) is able to recognize the biological activity of natural products in a targeted manner, as researchers at ETH Zurich have demonstrated. Moreover, AI helps to find molecules that have the same effect as a natural substance but are easier to manufacture. This opens up huge possibilities for drug discovery, which also has potential to rewrite the rulebook for pharmaceutical research. Nature has a vast store of medicinal substances. "Over 50 percent of all drugs today are inspired by nature," says Gisbert Schneider, Professor of Computer- Assisted Drug Design at ETH Zurich.

Since it launched in 2017, Facebook's machine-learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot-farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. Combining PyTorch with Microsoft Azure Machine Learning, AstraZeneca's technology can comb through massive amounts of data to gain new insights about the complex links between drugs, diseases, genes, proteins or molecules. Those insights are used to feed an algorithm that can, in turn, recommend a number of drug targets for a given disease for scientists to test in the lab. The method could allow for huge strides in a sector like drug discovery, which so far has been based on costly and time-consuming trial-and-error methods.

Not many scientists get solicited for photo ops, but for Daphne Koller it's a regular occurrence. "It happens at pretty much any event that has tech people," Koller says when asked about one recent snapshot. It's not like I feel like this is something I deserve." Selfie requests are just one sign of Koller's stardom, earned from more than 20 years bridging computer science, biology and education. She chalked up a string of accolades along the way: getting a master's degree from Jerusalem's Hebrew University at 18; becoming a Stanford University professor focused on machine learning at 26; winning, nearly a decade later, a Mac Arthur "genius grant" for research that combined artificial intelligence and genomics; and cofounding $1 billion (valuation) Coursera, an early platform to let people around the world take university classes for free. The next act for this 51-year-old innovator: Insitro, a firm in South San Francisco that aims to find new drugs by sorting through masses of data. If it succeeds, it will have overturned how drugs get discovered. Lab biologists typically focus on a few specific proteins as drug targets. If those fail, data scientists make suggestions for others to try. Insitro, on the other hand, wants to collect much more data before the biologists go off on their hunt. It will leverage advances in bioengineering (such as Crispr gene editing) and in software that enables computers to see things that escape humans. Koller describes her aha moment this way: "Machine learning is now doing amazing things if you give it enough data.