AlphaFold is a deep learning-based method that predicts protein structures with atomic accuracy, even in cases where no similar structures are known. It achieved high accuracy in the CASP14 assessment, outperforming other methods, and its performance generalizes to a large set of recently released PDB structures.

Some authors have filed patent applications relating to machine learning for predicting protein structures.

AlphaFold represents a significant breakthrough in protein structure prediction, achieving accuracy competitive with experimental methods. While some limitations exist regarding MSA depth and handling of protein complexes, its performance and broad applicability justify a high rating. The disclosed patent applications by some authors represent a potential conflict of interest.