John Jumper is an artificial intelligence researcher and biophysicist, whose work on AI-based protein structure prediction has enabled the routine prediction of protein structures to atomic accuracy. Dr Jumper led the development of the AlphaFold2 and AlphaFold3 systems at Google DeepMind, where he developed entirely new machine learning architectures to dramatically increase the ability of AI systems to learn from biological and chemical data. In particular, his work showed that it was possible to both predict a protein’s structure to atomic accuracy and for the structure prediction system to give an accurate measure of its confidence in the correctness of the prediction.
Dr Jumper was originally trained as a physicist and holds an MPhil in theoretical condensed matter physics from the University of Cambridge. Prior to Google DeepMind, he worked on molecular dynamics simulations of proteins, originally at D.E Shaw Research and later in his PhD at the University of Chicago. In his PhD, Dr Jumper developed principled machine learning methods to learn simulation parameters directly from the Protein Data Bank.
His work on protein structure prediction has won numerous awards, including the Breakthrough Prize in Life Sciences, the Canada Gairdner International Award, the Albert Lasker Award for Basic Medical Research, and the Nobel Prize in Chemistry.
Professional position
- Distinguished Scientist, Google DeepMind, Google
