AlphaFold

AlphaFold is a deep learning system from Google DeepMind that predicts a protein's 3D structure from its amino acid sequence, a problem that had stumped biology for decades. Before AlphaFold, working out a single protein's shape often meant years of painstaking lab work using techniques like X-ray crystallography or cryo-EM. AlphaFold does it computationally in minutes to hours, with accuracy that in many cases rivals experimental methods.

The AlphaFold Protein Structure Database, built with EMBL-EBI, holds predicted structures for over 200 million proteins, essentially covering nearly every protein sequence known to science. Researchers in drug discovery, structural biology, and medicine use it to identify drug targets, understand how disease-causing mutations disrupt protein function, and study enzymes and pathogens without waiting on slow wet-lab structure determination. It's free to use and has become a standard reference tool in molecular biology labs worldwide.

What sets AlphaFold apart is both its accuracy and its openness: DeepMind released the model and the full database rather than keeping it proprietary, which let the scientific community build on it almost immediately. Its successor, AlphaFold 3, extended predictions to protein interactions with DNA, RNA, ligands, and other molecules. The original work earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry, an unusual honor for a software tool.