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DeepMind's neural network predicted protein structures at CASP14 with a median backbone error of under 1 angstrom, comparable to experimental methods, and the team released predicted structures for essentially every human protein within a year.
AlphaFold 2 combines multiple-sequence alignments, an attention-based Evoformer network and an equivariant structure module trained end to end on the Protein Data Bank. At the blind CASP14 assessment in 2020 it achieved a median GDT of about 92 and a median backbone RMSD95 of 0.96 angstrom on the hardest targets, versus 2.8 angstrom for the next-best method.
The model provides per-residue confidence estimates (pLDDT), enabling users to distinguish reliable regions from disordered or uncertain ones. The accompanying AlphaFold Protein Structure Database, built with EMBL-EBI, released predicted structures for the human proteome and later for more than 200 million proteins.
For cancer drug discovery, AlphaFold accelerated structure-based design for targets without crystal structures and, with AlphaFold 3 (2024) extending to protein-ligand and protein-nucleic acid complexes, is now a routine part of the target-to-lead pipeline.
The shape of nearly every protein is now available to any researcher in seconds instead of years, which shortens the path from a cancer target to a designed molecule. It does not by itself produce drugs: binding pockets, dynamics and cellular context still need experiment.
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