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Taken from: https://www.forbes.com/sites/robtoews/2021/10/03/alphafold-is-the-most-important-achievement-in-ai-ever/
"With all this said, it is important to note that AlphaFold has meaningful limitations.
Its predictions are not always as accurate as more traditional experimental methods. It predicts one stable conformation per protein, bu…
Taken from: https://www.forbes.com/sites/robtoews/2021/10/03/alphafold-is-the-most-important-achievement-in-ai-ever/
"With all this said, it is important to note that AlphaFold has meaningful limitations.
Its predictions are not always as accurate as more traditional experimental methods. It predicts one stable conformation per protein, but proteins are dynamic and may change shape as they move through the body. Edge cases—like intrinsically disordered proteins and unnatural amino acids—can trip AlphaFold up.
AlphaFold generates predictions about individual protein structures, but it sheds little light on multiprotein complexes, protein-DNA interactions, protein-small molecule interactions, and the like—dynamics that are essential to understand for many biomedical use cases. And because (like any AI system) AlphaFold has learned to make predictions based on its training data, it may struggle to accurately predict the shapes of unusual new proteins, including de novo protein designs not found in nature."