Researchers find weaknesses in AlphaFold 3

The artificial intelligence–based structure-prediction software AlphaFold, originally developed by Google DeepMind, has quickly become an important tool for drug discovery. One of the improvements in its newest iteration, AlphaFold 3, which DeepMind made together with its drug-discovery spin-off Isomorphic Labs, is more-accurate structure predictions for protein and nucleic acid complexes. But when U.S. National Institute of Standards and Technology researchers Christina Bergonzo and Alexander Grishaev put AlphaFold 3 to the test, they discovered that there are cases where the tool’s predictions don’t match experimentally determined structures ( J. Chem. Inf. Model. 2024, DOI: 10.1021/acs.jcim.5c00245). The researchers asked the program to predict the structures of a number of RNA and DNA sequences, with some of the RNA sequences coordinated to metal ions. They also selected two sequences—each with structures that change dramatically with a single mutation—and asked AlphaFold to predict the structures before and after each mutation. The researchers compared those