AlphaFold Changed Science. After 5 Years, It’s Still Evolving

AlphaFold, the artificial intelligence system developed by Google DeepMind , has just turned five. Over the past few years, we've periodically reported on its successes ; last year, it won the Nobel Prize in Chemistry . Until AlphaFold's debut in November 2020, DeepMind had been best known for teaching an artificial intelligence to beat human champions at the ancient game of Go . Then it started playing something more serious, aiming its deep learning algorithms at one of the most difficult problems in modern science: . The result was AlphaFold2, a system capable of predicting the three-dimensional shape of proteins with atomic accuracy. protein folding Its work culminated in the compilation of a database that now contains over 200 million predicted structures, essentially the entire known protein universe, and is used by nearly 3.5 million researchers in 190 countries around the world The Nature article published in 2021 describing the algorithm has been cited 40,000 times to date. Last year, AlphaFold 3 arrived, extending the capabilities of artificial intelligence to DNA, RNA, and drugs. That transition is not without challenges-such as “ structural hallucinations ” in the disordered regions of proteins-but it marks a step toward the future. To understand what the next five years holds for AlphaFold, WIRED spoke with Pushmeet Kohli, vice president of research at DeepMind and architect of the AI ​​for Science division. WIRED: Dr. Kohli, the arrival of AlphaFold 2 five years ago has been called "the iPhone moment" for biology. Tell us about the transition from challenges like the game of Go to a fundamental scientific problem like protein folding, and what was your role in this transition? Pushmeet Kohli: Science has been central to our mission from day one. Demis Hassabis founded Google DeepMind on the idea that AI could be the best tool ever invented for accelerating scientific discovery. Games were always a testing ground, and a way to develop techniques we knew would eventually tackle real-world problems. My role has really been about identifying and pursuing scientific problems where AI can make a transformative impact, outlining the key ingredients required to unlock progress, and bringing together a multidisciplinary team to work on these grand challenges. What AlphaGo proved was that neural networks combined with planning and search could master incredibly complex systems. Protein folding had those same characteristics. The crucial difference was that solving it would unlock discoveries across biology and medicine that could genuinely improve people's lives. We focus on what I call “root node problems,” areas where the scientific community agrees solutions would be transformative, but where conventional approaches won't get us there in the next five to 10 years. Think of it like a tree of knowledge-if you solve these root problems, you unlock entire new branches of research. Protein folding was definitely one of those. Looking ahead, I see three key areas of opportunity: building more powerful models that can truly reason and collaborate with scientists like a research partner, getting these tools into the hands of...

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