Finding maximum clique is a well-researched NP-complete problem. For the practical applicability of algorithms for finding the maximum clique, they must be fast enough on the target domain of graphs. There has been a lot of progress made in recent years in the field of machine learning on graphs. In the master's thesis we use modern approaches to machine learning on graphs to speed up the dynamic algorithm for finding the maximum clique. Speedups are tested with different types of graphs with an emphasis on different types of protein graphs. We find that speeding up the maximum clique search is possible and can be achieved with a good choice of machine learning model. We also find that the speedups are not large but are consistent on almost all the graphs presented.