By analysing the structure of a protein it is possible to draw conclusions about its function. Obtaining the structure of a protein experimentally is however a time consuming and expensive process. By using evolution it is possible to infer the structure of a protein. AlphaFold2 (AF), the latest AI technology for protein structure prediction, uses evolutionary information to obtain protein structures in minutes instead of years at a fraction of the experimental cost. Here, we develop this technology further to predict the structure of interacting proteins. We create a confidence score, pDockQ, and show that this score rivals high-throughput experiments in distinguishing true and false protein-protein interactions (PPIs). Applying AF and the pDockQ score to a set of 65484 human PPIs we identify 1371 new high-confidence models. These models expand the structural knowledge of human protein complexes and can be used to e.g. develop new drugs or evaluate biological pathways. One limitation of AF is that the accuracy decreases with the number of proteins being predicted together and that the biggest protein complexes do not fit in the memory of the latest GPUs. To circumvent these issues, we predict subcomponents of protein complexes and assemble these together with Monte Carlo Tree search (MCTS). MCTS enables assembling some of the largest protein complexes using only sequence information and stoichiometry. Out of 175 protein complexes with 10-30 chains, 91 can be completely assembled with a median TM-score of 0.51. A third of these (30 complexes) are highly accurate (TM-score ≥0.8). The use of highly accurate protein structure prediction is revolutionising many fiends of biological research only one year after its realisation. Likely, this is only the beginning of a new era; the era of AI.