Despite its success, AlphaFold2 is not an all-knowing machine. It has solved one part of the protein folding problem, but not in the way a scientist would. It has not replaced biological experiments, but rather highlighted the need for them. The tool has drawn attention to the power of artificial intelligence in biology, inspiring new algorithms, biotech companies, and ways to practice science. Its successor, AlphaFold3, has moved to the next phase of biological prediction by modeling the structures of proteins in combination with other molecules like DNA or RNA.
Key takeaways:
- Google's AlphaFold2, an artificial intelligence tool, has made significant strides in solving the protein folding problem, which involves predicting the three-dimensional shape of a protein molecule from its one-dimensional molecular code.
- AlphaFold2's predictive models of 3D protein structures were over 90% accurate, five times better than its closest competitor, marking a significant breakthrough in molecular research.
- Despite its success, AlphaFold2 has not replaced biological experiments but has emphasized the need for them. It has also inspired new algorithms, biotech companies, and new ways to practice science.
- While AlphaFold2 has undeniably shifted the way biologists study proteins, there are still massive gaps that artificial intelligence hasn’t filled, such as simulating how proteins change through time or modeling them in the context in which they exist: within cells.