AlphaFold

node

The protein structure prediction system built at Google DeepMind, and the strongest single instance of machine learning demonstrably changing a scientific practice. The problem it addresses — inferring a protein's three-dimensional fold from its amino-acid sequence — had been open for roughly fifty years and was the subject of a standing blind experiment, CASP, precisely because the field could not agree on whose method worked. At CASP14 in late 2020 AlphaFold2 produced predictions with a median domain GDT_TS reported at 92.4, a level of accuracy the assessors described as competitive with experimental determination for most targets; the method was published in Nature in July 2021 (596, 583–589). What makes this different from most capability claims is that the test was blind, externally run and not chosen by the claimant, and that the result was then used: predicted structures became a routine first step in structural biology rather than a curiosity. Its limits are equally documented and are not marginal — see the same records.

A node is not a place. Drawing it on a map would assert something about the world that no stored fact supports.